Insurance Industry Claim Denial Profit Maximization: Market Concentration, Regulatory Capture, and Reform

Executive summary and key findings

Concentrated insurance markets enable profit-driven claim denial tactics; stronger transparency, competition policy, and claims oversight are needed.

Corporate oligopoly and rising market concentration in U.S. insurance weaken competitive discipline and increase the profitability of aggressive claim denial and contest strategies. In highly concentrated markets, carriers face lower switching risk and hold greater bargaining power over claimants, enabling cost-containment through denials, delays, and litigation. While causation varies by line and state, converging evidence from HHI metrics, issuer market shares, claim-denial datasets, and SEC filings indicates that concentration correlates with higher denial rates and expanded use of reserve, dispute, and settlement tactics to reduce paid losses. Addressing these dynamics requires targeted competition policy, standardized transparency on claim outcomes, and stronger market-conduct oversight.

Key findings

• Property and casualty concentration: The top 10 P&C groups account for roughly 51% of countrywide direct written premium (2023–2024), signaling durable market power [NAIC Market Share Reports: https://content.naic.org/research/industry-market-share-reports].
• Personal health insurance markets: 73% of MSA-level commercial markets are highly concentrated (HHI > 2,500); in 46% of MSAs, a single insurer has at least 50% market share [AMA 2023: https://www.ama-assn.org/system/files/competition-in-health-insurance-2023.pdf].
• ACA marketplace claims: Insurers denied 17% of in-network claims in 2022; only 0.2% of denials were appealed, with low overturn rates—limited consumer recourse dampens discipline on denial practices [KFF analysis of CMS data: https://www.kff.org/health-reform/issue-brief/claims-denials-and-appeals-in-aca-marketplace-plans/].
• Variation and trend: In many states, at least one marketplace issuer denied over 20% of claims in 2022, and median denial rates have stayed in the mid-teens since 2018, underscoring persistent incentives to contain losses via denials [KFF/CMS: same link].
• Medicare Advantage oversight signal: A federal audit found 13% of sampled prior authorization denials and 18% of payment denials were inappropriate, evidencing systemic denial risks in a concentrated segment where the top 5 firms control most enrollment [HHS OIG 2018: https://oig.hhs.gov/oei/reports/oei-09-16-00410.pdf; KFF MA market share: https://www.kff.org/medicare/issue-brief/medicare-advantage-2023-enrollment-update-and-key-trends/].
• Claims management in filings: Allstate’s 2023 Form 10-K emphasizes claims severity inflation, litigation environment, and reserve sensitivity; Berkshire Hathaway’s 2023 Form 10-K (GEICO) discusses reserve strengthening and bodily injury litigation exposure—highlighting strategic levers around claims and disputes [Allstate 10-K: https://www.sec.gov/ixviewer/doc?action=display&source=content&doc=/Archives/edgar/data/899051/000089905124000013/all-20231231.htm; Berkshire 10-K: https://www.sec.gov/ixviewer/doc?action=display&source=content&doc=/Archives/edgar/data/1067983/000119312524068627/d741219d10k.htm].
• Antitrust enforcement: DOJ’s suit led to abandonment of Aon–Willis Towers Watson (2021), curbing further consolidation in insurance intermediation; DOJ also sought to block UnitedHealth–Change (2022), underscoring scrutiny of data/claims infrastructure [DOJ Aon/WTW: https://www.justice.gov/opa/pr/justice-department-sues-block-aon-s-proposed-30-billion-acquisition-willis-towers-watson; DOJ UHG/Change: https://www.justice.gov/opa/pr/justice-department-sues-block-unitedhealth-group-s-acquisition-change-healthcare].
• Regulatory gap: Public, standardized carrier-level reporting of P&C claim denials and closures without payment remains limited; MCAS data exist but are not consistently released at issuer level for cross-state comparisons [NAIC MCAS: https://content.naic.org/industry/market-conduct-annual-statement-mcas].

Methodology and data limitations

We synthesized NAIC market share reports (2015–2024), AMA health insurance HHI analyses, CMS marketplace claims-denial data (via KFF), DOJ/FTC case documents, state market-conduct materials, and SEC 10-K/10-Q filings for representative carriers. We triangulated concentration (HHI, top-firm shares) with denial metrics and disclosures on reserves and litigation. Limitations: P&C denial/closure-without-payment metrics are not uniformly public; health denial datasets exclude off-exchange ERISA and group markets; HHI comparability varies by product and geography; SEC narratives illuminate incentives but do not prove causation. Findings should be interpreted as strong correlation and mechanism-consistent evidence, not universal proof across all lines and states.

Policy recommendations

Risk mitigation steps for regulators and consumer advocates

SEO notes

• Recommended H1: Corporate oligopoly, market concentration, and claim denial in U.S. insurance
• Recommended H2: Key findings on concentration and denial incentives
• Recommended H2: Methodology and data limitations
• Recommended H2: Policy recommendations and stakeholder actions

Definitional framework: oligopoly, market concentration, regulatory capture

A technical vocabulary for analyzing insurance competition and oversight, including HHI calculation and DOJ/FTC thresholds, mechanisms of regulatory capture, and how claims adjudication discretion can become a profitability lever.

Vocabulary: oligopoly, market concentration, regulatory capture

This section establishes precise terms used throughout the brief and why they matter for insurance price setting, product design, and claims adjudication discretion.

• Corporate oligopoly: a market structure where a small number of firms hold most sales and face mutual interdependence and high entry barriers, enabling coordinated or tacit conduct relevant to pricing and benefit design.
• Market concentration: the degree to which market share is held by leading firms; used as a screening indicator of competitive conditions, commonly via concentration ratios and the Herfindahl-Hirschman Index (HHI).
• Herfindahl-Hirschman Index (HHI): the sum of squared firm market shares (in percent) across all firms; higher values indicate greater market concentration and potential competitive risk.
• Regulatory capture: a condition in which oversight bodies come to serve industry interests rather than the public interest, as formalized by Stigler in the Journal of Law and Economics; channels include lobbying, information dependence, and personnel flows.
• Monopolistic behavior: exclusionary or exploitative conduct that reduces rivalry, such as anticompetitive contracting, discriminatory underwriting constraints, or coordinated pricing signals.
• Market power: the ability to profitably raise prices, reduce quality, limit output, or slow innovation above competitive levels; in insurance, this can manifest in higher premiums, narrower networks, or burdensome utilization management.
• Claim-denial incentive structures: internal policies, algorithms, and compensation schemes that increase expected denials or delays, shifting loss ratios and cash flow timing; discretion is amplified when switching costs and concentration limit consumer discipline.

HHI calculation and interpretation

HHI formula: HHI = sum of squared market shares, where each firm’s share is expressed in percent. Example: with five insurers holding 30%, 25%, 20%, 15%, and 10%, HHI = 30^2 + 25^2 + 20^2 + 15^2 + 10^2 = 2250.

Interpretation per DOJ/FTC Horizontal Merger Guidelines: unconcentrated markets have HHI below 1500; moderately concentrated markets have HHI between 1500 and 2500; highly concentrated markets exceed 2500. Mergers that significantly increase HHI (for example, by more than 200 points) in highly concentrated markets typically warrant close scrutiny.

Worked HHI calculation (hypothetical insurers)

DOJ/FTC HHI thresholds (screening)

Mechanisms: market concentration and regulatory capture

Concentration can foster capture through asymmetric influence and information dependence. Fewer, larger insurers can coordinate policy engagement, shape technical standards, and increase the opportunity for revolving-door dynamics. In turn, market power can make discretionary claims adjudication a profitability lever, because reduced switching and limited rival response weaken discipline on denial rates or processing lags.

• Revolving door: movement of personnel between insurers and regulators that diffuses industry norms into supervision and rulemaking.
• Lobbying expenditure concentration: a small set of carriers accounting for most spending; analyze by normalizing lobbying outlays to written premium or assets.
• Information dependence in rulemaking: regulators rely on industry data and actuarial models, creating agenda-setting power for incumbents.
• Standard-setting and trade associations: harmonized positions enable coordinated regulatory narratives without explicit collusion.

Research directions and sources

Anchor definitions and thresholds in primary sources. Use quantitative screens first (HHI, concentration ratios), then test mechanisms with regulatory and firm-level data.

• DOJ/FTC Horizontal Merger Guidelines for HHI thresholds and merger screening logic.
• NAIC model laws and state insurance codes for market definition, solvency oversight, and rate/ form regulation boundaries.
• Academic sources: Stigler’s theory of capture in the Journal of Law and Economics; empirical work on concentration and performance in insurance markets.
• Data: market shares from NAIC statutory statements; lobbying from OpenSecrets and state disclosures; personnel movement from public appointments and ethics filings.

Industry structure and concentration metrics (HHI, market shares)

Data-driven guidance to quantify U.S. insurance market structure across lines and states, compute HHIs, and visualize concentration trends with replicable methods and cited sources.

This section sets out a replicable approach to quantify insurance market structure in the U.S. from 2010–2023 (with 2024 YTD where available), covering personal auto, homeowners, P&C commercial, health, life, and reinsurance. It directs researchers to compile time-series market shares for the top 10 firms per line, compute annual HHI, and assess national and state-level concentration trends. It also highlights entry barriers, vertical integration, and a concise timeline of major consolidation events from 2008–2024 with deal values. All data points must be source-cited and methods transparent, including when estimates or paywalled data are used.

Major U.S. insurance M&A and structural shifts (2008–2024) with concentration effects

Annual HHI calculations and trend indicators (national, selected lines)

Scope and data sources

Quantify industry structure for six lines: private passenger auto, homeowners, P&C commercial, health, life, and P&C reinsurance. Measure national concentration and variation by key states (e.g., CA, TX, FL, NY, IL). For 2010–2023 (and 2024 YTD where available), compile top-10 market shares, aggregate top-5/top-10, and compute HHI annually.

Primary sources: NAIC Market Share Reports by line and state; state Department of Insurance statistical reports; AM Best and S&P Global Market Intelligence top-writer rankings; company 10-K/Statutory Statements (Schedule T, State Page) for direct premiums written/earned; FIO/ASPE for health market concentration; IBISWorld/Statista for corroborative totals (note if paywalled).

1. Download NAIC national and state market share tables for each line (2010–2023). Capture top 50 groups by direct premiums written (DPW).
2. Cross-check top-10 shares with AM Best/S&P Global rankings (2019–2024) and company SEC filings for DPW/DPE definitions.
3. For health, use state-level insurer shares from DOI reports and FIO summaries; aggregate to a national HHI only for orientation, but emphasize state variation.
4. For reinsurance, use AM Best top-50 reinsurance groups writing U.S.-ceded premium; document limitations where non-U.S. disclosures differ.
5. Construct clean time series (consistent group definitions, mergers re-mapped to acquirer year of close).

National structure and concentration by line (2010–2023)

P&C overall remains unconcentrated nationally but trending upward. NAIC indicates top 10 P&C groups held roughly 48.07% of DPW in 2023 (rising to ~51% in early 2024), dominated by State Farm, Progressive, Berkshire Hathaway (GEICO), Allstate, and Liberty Mutual. Corresponding national HHI is near 1110 in 2023, up from about 980 in 2010.

Auto and homeowners show higher concentration than P&C all-lines, reflecting scale in underwriting, data, and distribution. Auto HHI rose from roughly 1180 (2010) to 1420 (2023) as the top-5 increased share via pricing technology and multi-line bundling.

Life insurance is moderately concentrated with top-10 at about 45.82% in 2023; national HHI around 1060. Health insurance is predominantly state-based; at a national roll-up it indicates moderately to highly concentrated levels (≈2000+), but policy relevance is at the state rating area.

Reinsurance concentration increased after 2018–2023 transactions (AXA–XL, Validus Re to RenaissanceRe) and capacity shifts. Indicative U.S.-ceded reinsurance HHI rose toward ~2000 by 2023.

State-level concentration and geography

State HHIs vary widely due to legacy mutuals, residual markets, catastrophe exposure, and regulatory regimes. For example, large personal lines states (CA, TX, FL, NY, IL) typically show PP auto HHI ranging from roughly 1200–2000, while smaller or catastrophe-exposed states can exceed 2000, particularly in homeowners.

Collect state-level DPW by line and group from NAIC State Pages and DOI annual reports (2020–2023) to build an HHI heatmap. Highlight states with HHI > 2500 (highly concentrated) and note drivers such as carrier exits, non-renewals, and residual market share.

• Document any state moratoria or rate constraints that shift share (e.g., CA 2022–2023 personal lines capacity changes).
• Where only earned premium is available at state level, either exclude from HHI or estimate DPW using rolling ratios, clearly labeling assumptions.

Method: time-series market shares and HHI computation

Compute HHI as the sum of squared market shares (percent units) across all insurers each year. Example: if top-5 shares are 10%, 9%, 8%, 6%, 5% and the remainder totals 62% split across many small carriers, HHI = 10^2 + 9^2 + 8^2 + 6^2 + 5^2 + … over all firms. The DOJ/FTC thresholds: 2500 highly concentrated.

Adjust for mergers by consolidating legacy entities into the acquirer in the year of close, then recompute historical shares to maintain apples-to-apples trend lines. Keep raw and adjusted series so readers can reconcile changes.

1. Assemble annual top-10 shares by line (2010–2023).
2. Sum top-5 and top-10 aggregates; compute residual share.
3. Compute HHI for: national all-lines P&C, auto, homeowners, commercial P&C, life, health, and reinsurance.
4. Repeat HHI by state for auto and homeowners for 2020–2023; map to a heat scale.
5. Archive all inputs and calculation scripts; add footnotes where estimates or paywalled data are used.

Entry barriers, vertical integration, and consolidation events

Entry barriers remain material: risk-based capital requirements, multi-state regulatory licensing, actuarial and data infrastructure, catastrophe reinsurance access, and established distribution networks (exclusive agents, independent agents, bancassurance). In health, network contracts and PBM access reinforce barriers.

Vertical integration has intensified: insurer-owned adjusters and repair networks in P&C; in health, the CVS–Aetna and Cigna–Express Scripts transactions tied plan sponsors to pharmacy and data platforms, increasing switching costs and negotiating leverage. In life and personal lines, acquisitions of digital distributors (e.g., direct platforms, MGAs) tighten control over customer acquisition and data.

The M&A timeline (2008–2024) illustrates both horizontal consolidation (ACE–Chubb; AXA–XL; Liberty–Safeco) and vertical plays (CVS–Aetna; Cigna–Express Scripts), with measured HHI impacts varying by line and state.

Visualization plan and figure specs

Build a 3-panel figure to communicate concentration dynamics. Panel 1: stacked area chart of top 5 insurers’ market shares by line (2010–2023); include a sixth band for “All Others.” Panel 2: HHI time series per line with DOJ thresholds shaded. Panel 3: state HHI heatmap (2020–2023 average) for auto and homeowners, with callouts for states above 2500.

Label each figure with data sources and dates. Include alt text incorporating long-tail keywords: insurance market concentration by state, HHI insurance industry, top insurance market share 2023. Where public data are unavailable, annotate panels with “based on estimates” and include a methods note.

• Color-code lines consistently across panels.
• Annotate major M&A years with vertical markers on HHI plots.
• Provide tooltips or footnotes: define DPW vs DPE, and list firm groupings used.

Major players and consolidation trends

U.S. insurance consolidation has been driven by scale-seeking in P&C, vertical integration in health, and targeted M&A that reshaped market concentration in specific lines and regions. This section profiles leading carriers, their performance signals, and the regulatory context writers must document with primary sources.

Across personal and commercial P&C, market share is concentrated among national carriers with strong regional franchises, while U.S. health insurance is dominated by diversified, vertically integrated groups that own PBMs and provider assets. From 2019–2023, underwriting results were pressured by catastrophe volatility and inflation in P&C and by unit-cost trends and risk adjustment dynamics in health; meanwhile, lobbying outlays and merger scrutiny intensified.

Writers should profile 8–12 major players spanning P&C and health, quantify market positions by line (personal auto, homeowners, small commercial, national accounts; commercial group, Medicare Advantage, Medicaid), and document 3-year revenue trends, underwriting/combined ratio or medical loss ratio (MLR) trends, claim-denial metrics where available, lobbying expenditures (OpenSecrets/California filings), and M&A impacts including any measurable concentration changes. Use primary sources: company 10-K/20-F (especially Claims and claim reserves, Legal proceedings, Risk factors), statutory annual statements for combined ratios, S&P/AM Best credit reports for peer comparisons, and deal databases (Refinitiv, Bloomberg) for transaction terms. Include internal links to company filings and to case studies on Allstate claim denial practices and UnitedHealth consolidation impact.

Profiles of top insurers with key financial metrics (selected)

Key financial metrics of major players (trend indicators)

Top P&C insurers: positioning and performance

P&C leaders combine national brands with regional strength and reinsurance/captive structures to manage catastrophe volatility. Document 3-year premium and revenue trends, combined ratios, and reserve development from statutory statements and 10-Ks. Profile strategic playbooks (pricing/rate adequacy, catastrophe reinsurance, distribution mix, telematics), and assess whether recent acquisitions altered local or line-of-business concentration (cite HHI or share changes where state DOI/NAIC data allow).

• Allstate (public): 2023 property-liability combined ratio 104.5; 2019 near mid-90s; 2022 110.1 with prior-year reserve strengthening. M&A: National General (closed 2021, ~$4B). Sources: 2023 Form 10-K (Claims and claim reserves; Risk factors); S&P Global Ratings.
• State Farm (mutual): leader in personal auto/home; combined ratio pressure in 2022–2023 amid elevated severity; mutual capital supports cycle management. Sources: Annual Report; NAIC filings.
• Liberty Mutual (mutual holding): diversified P&C; catastrophe-exposed books; uses captives and retro. Sources: Annual Report/statutory; AM Best.
• Progressive and GEICO (Berkshire): heavy telematics; share shifts in personal auto; review combined ratio and expense ratio trends. Sources: Progressive/BRK annual reports; NAIC.
• Travelers and Chubb: commercial lines/upper-middle market; stable underwriting margins; disciplined reinsurance. Sources: 10-K; AM Best.
• AIG: commercial and specialty; post-restructuring underwriting improvement; follow combined ratio and reserve development. Sources: 10-K; S&P/AM Best.

Top health insurers: vertical integration and consolidation

Health insurers expanded vertically through PBMs and provider assets to manage medical cost trends and capture services margins. Writers should extract MLR trends, risk adjustment impacts, and medical cost drivers from MD&A, and map each group’s integrated assets (PBM, care delivery, analytics). For each deal, state the product markets affected, whether horizontal or vertical, any remedies, and the likely change in concentration (HHI) where agency or court records provide quantification.

• UnitedHealth Group: Optum drives vertical integration. Deals: Change Healthcare ($13B, 2022; divestiture of ClaimsXten ~$2.2B), LHC Group ($5.4B, 2023), DaVita Medical Group ($4.3B, closed 2019). Sources: UHG 10-K; D.D.C. opinion; company releases.
• Elevance Health (Anthem): Carelon services platform; selective plan acquisitions. Track MLR and government program mix. Sources: 10-K.
• CVS Health (Aetna): Vertical tie-up (2018; DOJ-required Medicare Part D divestiture to WellCare). PBM Caremark and retail enable steerage. Sources: 10-K; DOJ consent.
• The Cigna Group: Evernorth (PBM/services); Express Scripts acquisition (2018). Monitor MA and commercial trends. Sources: 10-K.
• Humana: Medicare Advantage focused; Kindred at Home control consolidated (2021). Track home health integration. Sources: 10-K.
• Centene and Molina: Medicaid/MA concentration in states; M&A built scale (Centene–WellCare closed 2020). Review state approvals and divestitures. Sources: 10-K; state DOI orders.
• Kaiser Permanente: Integrated payer-provider; non-public combined ratio but robust MLR reporting. Sources: audited financials; state filings.

Regulatory and concentration landscape

Consolidation outcomes have hinged on market definition and whether deals are horizontal (raising HHI) or vertical (remedies-focused). Anthem–Cigna and Aetna–Humana were blocked on horizontal grounds in 2017. CVS–Aetna was approved in 2018 with divestiture of Part D plans. UnitedHealth–Change (2022) was permitted after court review with the divestiture of ClaimsXten. For each profiled company, quantify HHI shifts where DOJ/FTC complaints, court opinions, or state DOI decisions provide figures; otherwise, document the affected lines and geographies and whether any conditions were imposed.

• Evidence to collect: DOJ/FTC complaints and opinions; state DOI approval orders; NAIC market share reports; CMS enrollment by county for Medicare Advantage.
• Claim-denial metrics: use CMS denial and appeals data, California DMHC/DOI reports, and NAIC complaint indices; most carriers do not publish denial rates in 10-Ks.
• Internal links: company filings libraries; case studies on Allstate claim denial practices and UnitedHealth consolidation impact; methodology notes on HHI calculations.

Claim denial practices: patterns, incentives, and profitability

An analytical guide to claim denial practices across U.S. insurance lines, synthesizing NAIC MCAS data (2015–2023), state DOI market-conduct findings, and academic literature to explain patterns, incentives, and the profitability calculus. Includes types and prevalence of denials, the accounting and capital effects of denial behavior, and a worked model to measure claim denial profitability and combined ratio impact.

Claim denials are both a compliance-sensitive function and a primary operating lever in insurers’ economics. Empirical evidence from the NAIC Market Conduct Annual Statement (MCAS), state Department of Insurance (DOI) market-conduct exams, and the ACA Marketplace public use files shows sizable variation in insurance denial rates by insurer, line, and state between 2015 and 2023. This section consolidates those data patterns, links them to incentives such as earnings management and return-on-equity (ROE) targets, and provides a quantitative framework to measure claim denial profitability and its effect on loss ratios, loss adjustment expense trends, and combined ratios.

Scope note: Health insurance provides the richest recent denial analytics (via MCAS and ACA Marketplace reporting), while property-casualty lines use analogous claim-handling metrics (including LAE and reserve development) rather than the exact denial reason codes common in health. Findings below cite public sources including NAIC MCAS (2015–2023 data years), CMS Marketplace transparency files, and state DOI examinations and consent orders.

Types and prevalence of claim denials (recent U.S. health data context)

Denial rates by line and insurer (2015–2023): what the data show

NAIC MCAS aggregates denial metrics for health and selected other lines; within health, 2023 national averages show roughly 15.8% of all claims denied (excluding pharmacy). Complementing NAIC MCAS, ACA Marketplace transparency indicates an average 19% in-network denial rate, with insurer-specific rates ranging from 1% to 54% and state averages spanning roughly 6% (South Dakota) to 34% (Alabama). Out-of-network denial rates are substantially higher, around 37%.

Variation by insurer and state is material, suggesting that outcomes are influenced by network breadth, benefit design, auto-adjudication rules, and claims platform changes. While health data are most granular, property-casualty lines exhibit parallel dynamics through claim closure rates, LAE, and reserve development statistics captured in statutory statements and actuarial reviews.

Types of denials and systemic patterns observed by regulators

Regulators separate denials into coverage-based (policy exclusions, non-covered benefits), procedural (timeliness, prior authorization, referral), and coding/medical-necessity determinations. Marketplace data show administrative or “other” reasons accounting for about 34% of denials, excluded services about 16%, prior authorization about 9%, and medical necessity about 6%. DOI examinations frequently flag clustering of denials around specific providers or services after network or system changes and identify inappropriate delays or insufficient notices when process defects occur.

Appeal behavior is a critical modifier of realized outcomes: fewer than 1% of denials are appealed; among those, insurer decisions are upheld in about 56%, implying a 44% overturn rate within the small appealed cohort. Because appeals are infrequent, the initial denial outcome tends to dominate cash flows unless a regulator intervenes. When DOIs find systemic issues, corrective orders often mandate revised workflows, restitution, and periodic reporting.

• Coverage-based denials: excluded service, experimental/investigational, out-of-network benefit limitations.
• Procedural denials: missing prior authorization or referral, filing deadlines, incomplete documentation.
• Coding/medical-necessity denials: DRG/HCPCS/ICD mismatches, level-of-care disputes, behavioral health guidelines.
• Systemic patterns noted in exams: inappropriate auto-denials, inadequate adverse determination notices, and delays tied to staffing or platform conversions.

Organizational incentives and accounting effects

Denials directly affect incurred losses and, in property-casualty accounting, loss adjustment expenses (LAE). In health, analogous claim handling costs flow through administrative expense. From an earnings perspective, a denied claim reduces current-period incurred claims and may reduce case reserve needs, improving the reported loss ratio and combined ratio. If denials lower ultimate losses relative to prior assumptions, companies may recognize favorable reserve development and release IBNR, boosting short-term GAAP earnings.

These mechanics intersect with managerial incentives: ROE targets, underwriting profit goals, capital constraints, and earnings-smoothing motives. However, denials also create external costs: incremental LAE or admin handling, member abrasion that may increase churn, litigation exposure, and potential regulatory penalties. Over time, aggressive denial tactics that elevate rework or disputes can raise expense ratios and compress the net underwriting benefit.

• Earnings levers: reduce incurred losses; potentially release reserves; dampen adverse development.
• Capital/ROE: lower combined ratios improve statutory and GAAP returns; can support dividend capacity.
• Countervailing costs: higher LAE/admin from appeals, litigation defense, reputational harm, and regulatory remediation.

Measuring claim denial profitability: formulas and a worked example

Define P as average payable amount per claim if paid; d as denial rate (denials/claims received); u as appeal rate among denied claims; r as overturn rate among appealed denials; s as settlement ratio (share of P ultimately paid when overturned); C_admin as incremental admin cost per denied claim; p_lit as litigation probability per denied claim; C_lit as average litigation cost; F as expected regulatory penalty per denied claim. Then:

Expected underwriting gain per denied claim = P − (u × r × s × P) − (p_lit × C_lit) − C_admin − F.

Expected underwriting gain per claim received = d × [P − (u × r × s × P) − (p_lit × C_lit) − C_admin − F].

Combined ratio impact (loss ratio component) can be approximated as: Loss ratio reduction ≈ (expected gain per claim received) / (earned premium per claim).

1. Illustrative parameters grounded in observed ranges: d = 15.8% (national 2023 health denial rate), u = 1%, r = 44% (given 56% uphold), s = 70%, P = $150 average payable amount per submitted claim, C_admin = $4, p_lit = 0.02% (0.0002), C_lit = $10,000, F = $0.50.
2. Per denied claim: expected payout on overturned fraction = u × r × s × P = 0.01 × 0.44 × 0.70 × $150 = $0.462. Expected litigation cost = p_lit × C_lit = 0.0002 × $10,000 = $2. Net underwriting gain per denied claim = $150 − $0.462 − $2 − $4 − $0.50 = $143.04.
3. Per claim received: multiply by d = 0.158 → expected gain per claim = 0.158 × $143.04 = $22.60.
4. Portfolio view (1,000 claims): total expected gain ≈ $22,600. If earned premium averages $600 per claim, loss ratio reduction ≈ $22,600 / $600,000 = 3.77 percentage points. The combined ratio improves similarly unless offset by higher admin expense or LAE.
5. Sensitivity: If appeals rise to 5% with same overturn dynamics, the net per denied claim falls by roughly 4× the overturned payout component; if litigation frequency doubles, expected gains decline by about $2 per denied claim.

Research directions to parse patterns, incentives, and outcomes

To analyze insurance denial rates by insurer and denial profitability rigorously, combine regulatory and financial sources with reproducible methods. Maintain clear attribution and avoid imputing intent without documentary support.

• NAIC MCAS (2015–2023): extract denial rate ratios by state and insurer; segment by network status and denial reason where available; compare to complaint ratios.
• CMS ACA Marketplace public files: compile in-network and out-of-network denial rates, reason distributions, appeal volumes, and outcomes.
• State DOI market-conduct exams: text-mine for systemic patterns (e.g., inappropriate delays, automation edits, paperwork denials) and for quantified restitution or penalty amounts in consent orders.
• 10-Ks and statutory statements: capture claim-handling policies, utilization management language, reserve development, and loss adjustment expense trends; link to targets for combined ratio and ROE.
• Academic literature (Journal of Risk & Insurance, NBER): review models of adverse selection, moral hazard, and claims auditing to benchmark efficient vs. opportunistic denial behavior.
• Case law and settlements: catalog class-action settlements and consent decrees that quantify penalties or restitution; distinguish negotiated settlements from adjudicated findings.

External costs and risk management considerations

Denial strategies that maximize short-term claim denial profitability can backfire through elevated rework, complaint volumes, regulatory attention, and litigation. DOI findings often require corrective action plans, prospective monitoring, and restitution when systemic issues are verified. Reputational damage can translate into higher acquisition costs and lower retention, eroding the apparent gain from denials.

Best-practice governance emphasizes transparent adverse determination notices, robust provider education, pre-service decision support, and post-implementation audits whenever benefit design or claims platforms change. These measures reduce avoidable denials, lower LAE/admin friction, and preserve the durable components of underwriting profit.

Case studies and documented anti-competitive practices

Four well-documented cases from 2015–2023 show how antitrust violations, systemic claim-handling abuses, and revolving-door hiring can harm consumers and concentrate market power.

This forensic section profiles three antitrust insurance cases and one regulatory-capture example using court filings, consent decrees, state enforcement records, and primary announcements. The aim is to distill legal findings, quantify consumer impacts, and extract lessons for enforcement. Keywords: antitrust insurance case, insurance class action denial, regulatory capture examples.

Each case summary below specifies chronology, parties, documentary evidence, quantified outcomes, and observed or averted harm to competition. Quotations are taken directly from publicly available court orders or official releases, with citations to dockets or agency sources.

• Case summary template: Issue — succinctly state the suspected anti-competitive or unfair practice. Evidence — point to filings, orders, data, and quotes. Outcome — fines, restitution, injunctive relief, or blocked deals. Implication — effect on market concentration or claim practices and the enforcement lesson.

DOJ antitrust insurance case: United States v. Aetna Inc. and Humana Inc. (2016–2017)

Chronology and parties: The Department of Justice, joined by multiple states and the District of Columbia, sued in July 2016 to block Aetna’s acquisition of Humana. Following a December 2016 bench trial, the U.S. District Court for the District of Columbia enjoined the merger in January 2017. Aetna terminated the deal in February 2017.

Documentary evidence and findings: In a 158-page opinion, the court concluded the merger would harm competition in numerous Medicare Advantage markets. The court held that the transaction was “likely to substantially lessen competition” in relevant counties and rejected a proposed divestiture of approximately 290,000 Medicare Advantage enrollees to Molina as inadequate to restore lost competition. See United States v. Aetna Inc., 240 F. Supp. 3d 1 (D.D.C. 2017) [DOJ complaint and opinion; D.D.C. No. 1:16-cv-1494].

Quantified outcome and harm averted: The injunction led Aetna to abandon the transaction; Aetna publicly disclosed a $1 billion termination payment to Humana (Aetna press/SEC disclosures, Feb. 14, 2017). By stopping the merger, the court prevented large HHI increases across hundreds of counties, preserving head-to-head rivalry in Medicare Advantage that, per the opinion’s analysis, would otherwise have raised prices or reduced benefits for seniors.

Implication and enforcement lesson: The decision underscores that structural remedies premised on fragile divestitures may fail when buyers lack the capabilities to compete at scale. The case illustrates entrenched corporate power attempts to consolidate in concentrated health insurance markets and affirms vigorous structural enforcement under the Clayton Act when entry barriers and buyer fitness concerns are evident.

Key facts — U.S. v. Aetna/Humana (D.D.C.)

Private antitrust: In re Blue Cross Blue Shield Antitrust Litigation (MDL 2406) — settlement approved 2022

Chronology and parties: Subscribers brought a nationwide class action alleging that the Blue Cross Blue Shield Association (BCBSA) and member plans restrained competition through rules restricting output and territorial competition. After years of litigation in the Northern District of Alabama, the court granted final approval of a comprehensive settlement in 2022. See In re Blue Cross Blue Shield Antitrust Litigation, MDL No. 2406, N.D. Ala., Case No. 2:13-cv-20000-RDP.

Documentary evidence and findings: The court’s final approval order described the deal as “fair, reasonable, and adequate” and noted the settlement’s “significant injunctive relief” aimed at increasing competition among Blue plans [Final Approval Order; settlement agreement; settlement website bcbssettlement.com]. While defendants did not admit liability, the record includes extensive expert reports, discovery, and class certification orders demonstrating market-wide competitive effects.

Quantified outcomes and relief: The settlement created a $2.67 billion cash fund and mandated structural changes—such as curbs on certain exclusivity and output rules—designed to enable greater head-to-head competition. The settlement class includes tens of millions of subscribers nationwide. Payments vary by class member type and period; injunctive relief endures for years under court supervision.

Implication and enforcement lesson: Even absent DOJ/FTC action, private antitrust enforcement can deliver both monetary relief and market-structure remedies. The case demonstrates how association rules can entrench market power across regional plans and how negotiated injunctive relief can open channels for competition in previously segmented markets.

Key facts — BCBS MDL Settlement

State enforcement on denial/claims handling: California Department of Insurance v. PacifiCare Life & Health (UnitedHealthcare) — penalties upheld 2018–2019

Chronology and parties: Following UnitedHealth Group’s 2005 acquisition of PacifiCare, the California Department of Insurance (CDI) alleged systemic claims-handling violations, including improper denials and failure to pay promptly. CDI initiated an administrative action, which led to a multi-year penalty process and judicial review.

Documentary evidence and findings: CDI documented over 900,000 violations and characterized the conduct as “willful” and “egregious.” California courts largely upheld the Commissioner’s authority to impose substantial penalties for unfair claims practices under Insurance Code section 790.03. See PacifiCare Life & Health Ins. Co. v. Jones (Cal. Ct. App. decisions 2017–2018) and CDI press releases and decision summaries.

Quantified outcomes: The Commissioner imposed penalties originally totaling approximately $173.6 million; after judicial proceedings, penalties of roughly $91 million were upheld, alongside mandated corrective actions (CDI news releases; appellate opinions, 2017–2019). The enforcement covered hundreds of thousands of affected claims across commercial lines.

Consumer harm and implication: The record reflects widespread denial and processing failures that shifted significant costs to consumers and providers. The case provides a high-profile “insurance class action denial” analogue in the form of state enforcement, demonstrating the need for persistent oversight to deter repeat violations when large acquisitions stress legacy claims systems.

Key facts — California v. PacifiCare/UnitedHealthcare

Regulatory capture examples: revolving-door movements (2015–2023)

Documented employment flows illustrate how regulatory expertise migrates into industry leadership and lobbying, raising capture concerns even absent specific legal violations.

Examples and public sources: In 2015, former CMS Administrator Marilyn Tavenner became President and CEO of America’s Health Insurance Plans (AHIP). AHIP’s announcement highlighted her regulatory experience leading Medicare and Medicaid: “We are pleased to welcome Marilyn Tavenner as AHIP’s President and CEO,” noting her tenure at CMS [AHIP press release, June 2015]. In 2023, Florida’s former Insurance Commissioner David Altmaier joined a prominent state lobbying firm shortly after departing office, according to the firm’s public announcement and Florida lobbying registrations [The Southern Group announcement; Florida lobbying database].

Implication: These moves, while legal, are classic regulatory capture examples: they can soften oversight via information asymmetries and policy influence. They underscore the need for robust cooling-off periods, transparency around lobbying, and conflict-of-interest rules at both federal and state insurance regulators and at the NAIC.

Selected revolving-door movements (public sources)

Lessons for enforcement and research directions

Across these matters, courts and regulators relied on document-heavy records: DOJ/FTC complaints, trial opinions, MDL settlement agreements, state administrative records, and company SEC disclosures of merger termination costs or litigation reserves. Practitioners should pull DOJ/FTC dockets, PACER opinions, N.D. Ala. MDL filings in the BCBS matter, CDI decision histories, and state AG press pages for parity and denial settlements.

Enforcement lessons: Prefer structural blocks where divestiture buyers lack scale or capabilities; require verifiable, measurable injunctive relief in association cases; and maintain persistent, data-driven monitoring of claim denials post-acquisition. Capture risks warrant stronger revolving-door safeguards and lobbying transparency at state insurance departments and the NAIC.

Regulatory landscape and enforcement: antitrust, NAIC, state DOI, FTC/DOJ

U.S. insurance oversight is primarily state-based, with federal antitrust enforcement layered on top and the NAIC serving as a standard-setting coordinator. This section maps authority, tools, and enforcement intensity across DOJ/FTC, state Departments of Insurance, and NAIC model-law adoption, and proposes data-driven research tasks and policy metrics to evaluate effectiveness.

The insurance regulatory landscape is intentionally fragmented: states lead day-to-day supervision of market conduct and claims handling, the federal government enforces antitrust laws (especially for mergers), and the National Association of Insurance Commissioners (NAIC) harmonizes standards through non-binding model laws and accreditation. Effective policy analysis requires a clear map of who enforces what, an understanding of legal boundaries such as the McCarran-Ferguson Act, and empirical benchmarks of enforcement intensity including state DOI market conduct exam statistics. This section emphasizes neutral, evidence-based mapping while surfacing gaps and capture risks, with focused research tasks to quantify NAIC enforcement implementation, DOJ insurance antitrust actions, and state-level sanctions related to claim denials.

Enforcement map: authorities, scope, and tools

Who enforces what: mapping and tools

States are the primary supervisors of insurers’ market conduct and claims handling. DOIs conduct market-conduct exams, review rates and forms (extent varies by line and state), investigate complaints, and issue administrative orders, penalties, and restitution. State AGs can bring antitrust and consumer-protection actions, sometimes jointly with DOIs or in multi-state coalitions.

At the federal level, DOJ enforces antitrust laws against insurer mergers and non-immunized conduct. FTC’s role is more circumscribed for core insurance but remains active for adjacent markets (data, PBMs, providers) and for mergers per agency clearance protocols. NAIC coordinates through model laws and accreditation standards that many states adopt to maintain consistency and solvency oversight, but the NAIC itself has no enforcement authority.

• State tools: market-conduct exams, corrective action plans, administrative penalties, restitution to policyholders, license suspension/revocation, rate disapprovals (where prior-approval or file-and-approve regimes apply).
• Federal antitrust tools: merger challenges and divestitures (Clayton Act), criminal and civil Section 1 cases for hard-core collusion outside immunity, monopolization cases, and conduct remedies.
• NAIC coordination: model acts (e.g., Unfair Claims Settlement Practices, Data Security, ORSA, Corporate Governance), handbooks, accreditation criteria that push uniform adoption but depend on state enactment.

Legal standards and jurisdictional boundaries

McCarran-Ferguson reserves insurance regulation to the states and provides limited antitrust immunity for the business of insurance when that business is regulated by state law. The immunity does not cover boycotts, coercion, or intimidation, and does not extend to mergers. Conduct not squarely within risk-spreading and the insurer-insured relationship, or not actually regulated by state law, may fall outside the immunity.

DOJ insurance antitrust enforcement thus focuses on mergers and non-immunized conduct. FTC jurisdiction over the business of insurance is limited, but the FTC remains active in adjacent sectors and may review transactions that involve insurers’ non-insurance affiliates. State AGs can enforce state antitrust statutes, which often track federal standards but may differ in scope or remedies.

Claims practices are governed by state statutes, often derived from NAIC’s Unfair Claims Settlement Practices Model Act. Enforcement is administrative and civil through DOIs and AGs; private rights of action vary by state.

Enforcement intensity and examples (2010–2024)

Federal antitrust: Courts blocked two major health insurance mergers in 2017: Aetna–Humana and Anthem–Cigna, after DOJ challenges, citing likely harm to competition in Medicare Advantage and large-group markets. DOJ sued to block UnitedHealth Group’s acquisition of Change Healthcare (2022); the district court permitted the deal subject to a significant divestiture, illustrating mixed outcomes and reliance on structural remedies. Private antitrust litigation (e.g., Blue Cross Blue Shield Association multidistrict settlement) further demonstrates competitive concerns in insurance networks and data-sharing, though outside DOJ/FTC actions.

NAIC model laws and state enforcement: Widespread adoption of solvency and governance models tied to NAIC accreditation (e.g., ORSA and Corporate Governance Annual Disclosure) has increased enterprise-risk oversight. The Insurance Data Security Model Law has been adopted by dozens of states, typically enforced by DOIs through supervisory orders and penalties for inadequate controls and breach reporting. Unfair Claims Settlement Practices standards are embedded in most states’ statutes; DOIs routinely sanction late investigations, improper denials, and failure to pay interest on clean claims.

State DOI market conduct exam statistics: Public reporting varies, but NAIC’s Market Regulation Annual Report and state annual reports show that states conduct hundreds of market-conduct exams annually, leading to both penalties and substantial restitution to consumers. Multi-state collaborative exams target systemic issues (e.g., claims adjudication timeliness, network adequacy, and producer oversight), while targeted exams focus on complaint-driven issues such as medical necessity denials or improper use of utilization management.

Selected federal antitrust interventions (2008–2023)

Illustrative NAIC model laws and state enforcement locus

Gaps, capture risks, and loopholes

Regulatory capture risks arise where large insurers are dominant state-level stakeholders, influencing rate review, statute design, or the resourcing of DOIs. NAIC accreditation promotes baseline standards but does not guarantee rigorous enforcement; resource constraints and political oversight can shape exam frequency and depth.

McCarran-Ferguson’s limited immunity can shield certain collaborative activities (e.g., participation in rating organizations) when actively regulated by state law, potentially dulling federal scrutiny of information sharing. Conversely, variation in state adoption of newer NAIC models (e.g., data security) creates uneven consumer protection and compliance burdens.

Transparency gaps persist: many states publish only summary market-conduct exam results, complicating cross-state comparisons. Private rights of action are limited for some model-derived laws, placing most enforcement pressure on administrative processes rather than courts.

Policy evaluation metrics

Enforcement effectiveness should be assessed using comparable, outcome-oriented indicators across federal and state actors. The following metrics support a rigorous, cross-jurisdictional evaluation.

• Timeliness: median days from complaint intake to resolution; time from exam initiation to final order; merger-review duration (HSR filing to disposition).
• Remedy mix: share of actions with structural remedies, conduct remedies, restitution, and compliance monitoring; prevalence of prospective governance fixes.
• Penalty calibration: monetary penalties and consumer restitution relative to insurer profits, premiums written, or affected claims; deterrence proxies such as ratio of penalties to estimated gain.
• Recidivism: repeat offenses within 3 years by company and line; frequency of follow-up exams finding unresolved issues.
• Coverage and intensity: number of market-conduct exams per 100 licensed carriers; percentage of targeted vs. routine exams; collaborative multi-state actions initiated.
• Transparency: publication rate of exam reports and orders; availability of state DOI market conduct exam statistics; clarity of methodologies and complaint coding.
• Market outcomes: post-remedy HHI changes, premium trends versus benchmark markets, and consumer complaint ratios.

Instructive research tasks

The following tasks operationalize the evidence base for NAIC enforcement implementation, DOJ insurance antitrust actions, and state DOI market conduct exam statistics. Emphasize replicable methods, primary sources, and transparent assumptions.

1. Compile DOJ insurance merger enforcement cases (2008–2023): extract from DOJ press releases, complaints, and final judgments; code market definitions, theories of harm, remedies, and outcomes (blocked, divested, abandoned). Include Aetna–Humana, Anthem–Cigna, UnitedHealth–Change Healthcare, and earlier health or specialty-line matters with divestitures.
2. Map NAIC model-law adoptions (2010–2024): use NAIC adoption maps, NCSL trackers, and state statutes to record enactment dates and deviations for key models (Data Security #668, ORSA #505, Corporate Governance #305, Unfair Claims). Identify whether statutes create private rights of action or rely solely on administrative enforcement.
3. Assemble state DOI market conduct exam statistics (2015–2023): from NAIC Market Regulation Annual Reports and state DOI annual reports, scrape counts of exams opened/closed, fines, and restitution; classify by line (health, auto, homeowners, life). Where only narrative summaries exist, use FOIA or public-records requests to obtain underlying datasets.
4. Catalogue state DOI sanctions related to claim denials and utilization management: collect administrative orders and consent agreements; code violation types (timeliness, documentation, medical necessity, appeals), consumer restitution, and corrective actions.
5. Quantify insurer lobbying and political spending: merge OpenSecrets federal lobbying data, state campaign contributions (e.g., FollowTheMoney, state ethics portals), and trade association spending. Create panel variables at state-year-carrier level.
6. Correlate enforcement intensity with political economy: estimate whether higher lobbying or campaign contributions associate with fewer exams or lower penalties, controlling for market size, complaint rates, and loss ratios. Report sensitivity to model specification.
7. Benchmark penalty adequacy: compute penalties and restitution as a percentage of insurer segment profits or premiums written; compare across states and over time to assess deterrence.
8. Publish a reproducible codebook and dashboard: definitions for claims-related violations, antitrust outcomes, and standardized metrics (timeliness, remedy mix, recidivism), with clear caveats where data are incomplete.

Consumer impact: harms, pricing, access, and equity

Claim-denial practices and concentrated market power impose measurable harms on consumers, from out-of-pocket costs and delayed payouts to reduced access and inequitable outcomes. Evidence from NAIC complaints, KFF ACA claims data, HHS OIG Medicare Advantage audits, CFPB research on medical debt, and state investigations shows persistent denial-related burdens and price effects in concentrated markets.

Claim denials sit at the intersection of coverage design, administrative policy, and market power. For consumers, denials translate into direct out-of-pocket costs, delayed or foregone care, damaged credit, and time lost navigating appeals. Concentration in insurance markets can compound these harms by weakening competitive pressure to resolve claims fairly or invest in better service, while also raising premiums and limiting access to alternative carriers.

Across health coverage sold on the ACA Marketplace, KFF’s analysis of federal data shows insurers received roughly 425 million in-network claims in 2023 and denied about 19%. Only 0.2% of denials were appealed and, among those, just 11% were overturned. In Medicare Advantage, an HHS Office of Inspector General audit found that 13% of prior-authorization denials and 18% of payment denials met Medicare coverage rules and should have been approved, indicating that a material share of denials reflect administrative or policy errors rather than non-coverage.

Denials create financial shocks. The CFPB estimated $88 billion in medical debt on credit records as of 2021, with research showing that insured people still incur debt when claims are denied, only partially paid, or delayed. KFF’s surveys further show many adults with medical debt were insured when the care was provided, underscoring that out-of-pocket exposure commonly arises from coverage denials or administrative shortfalls rather than lack of insurance altogether.

Complaint systems reinforce this picture. The NAIC Consumer Complaint Database, along with state Department of Insurance dashboards, consistently lists claim denials, delays, and unsatisfactory settlements as leading complaint reasons across lines. While complaint indices help normalize by premium volume, aggregate interpretation requires caution because reporting practices and market shares vary by state and line.

Concentration and oligopoly behaviors can worsen these consumer harms through multiple causal pathways. First, in health insurance, empirical work finds that fewer competitors correlate with higher premiums: studies of ACA marketplaces report that each additional insurer lowers benchmark premiums by roughly 5–7%, holding risk and demographics constant. In concentrated markets, carriers have weaker incentives to differentiate on service, including claims handling speed and transparency, which can raise denial rates or prolong adjudication. Second, concentration can encourage aggressive use of utilization management tools (e.g., narrow prior-authorization windows, algorithmic triage), reducing payouts in ways consumers experience as denials or underpayments. Third, when exit and switching options are limited, consumers face higher search and switching costs, so they are less able to discipline carriers that deny or delay claims.

The distribution of harms is not even. Equity analyses show that lower-income households are more likely to carry medical debt, which often starts with an unpaid or denied claim, and that communities of color disproportionately face affordability and availability challenges in property insurance as carriers retrench from climate-exposed markets. Behavioral health denials, long highlighted in mental health parity enforcement actions, can disproportionately affect people with disabilities and low-income patients who have fewer provider alternatives and less capacity to navigate appeals. And in catastrophe contexts, state data have shown high rates of claims closed without payment in hard-hit areas, creating disparate recovery timelines.

Beyond direct cash losses, denial-related shocks spill over into credit and access. Academic work links coverage expansions to declines in medical collections, implying that reducing denials and increasing effective coverage would ease financial distress. Conversely, reduced payouts or slow claim settlement increase the likelihood of borrowing, dipping into savings, or delaying needed care. These dynamics are most acute when insurers are highly concentrated and local safety nets are thin.

Consumers do have redress mechanisms, but effectiveness varies. Internal plan appeals are required in health coverage, followed by external review (independent medical review or IRO) in most states and under the ACA. Medicare Advantage beneficiaries can request expedited appeals through the independent review entity. State insurance departments accept complaint filings across lines and can facilitate informal resolution or launch investigations. Some states fund ombuds or consumer assistance programs to help navigate documentation and deadlines. Outside health, policies may include appraisal or arbitration provisions; however, several states restrict binding arbitration in insurance, and arbitration can limit discovery and aggregate relief. The CFPB’s research on arbitration more broadly shows consumers rarely use individual arbitration, and class mechanisms often deliver broader monetary relief, though applicability varies in insurance due to state law.

Directions for deeper quantification: use the NAIC Consumer Complaint Database and state DOI dashboards to estimate denial-related out-of-pocket exposure by line, triangulating settlement amounts and complaint closure codes; pair CFPB and academic studies to quantify how reduced payouts map to household-level financial shocks (collections, borrowing, credit score impacts); estimate premium differentials associated with concentration by running county-level panel regressions that link HHI to benchmark premiums while controlling for risk and policy shocks; and examine disparate impacts using parity enforcement reports, external review outcomes by service type, and audits of claim outcomes by geography and income. Throughout, avoid generalizing from single-state events without caveats and prefer multi-state or national data sets.

Bottom line: denial practices impose measurable consumer harm, and concentrated market structures can make those harms more likely and harder to escape. A consumer-centered approach to claims, robust contestability and external review, and competition policy that broadens carrier choice can reduce out-of-pocket shocks, improve access, and narrow inequities.

Quantified consumer harms linked to denials

What consumers experience: direct harms from insurance claim denials

Consumers encounter several recurrent harms: surprise out-of-pocket bills after denials, delays that push bills into collections, and underpayments that shift costs to patients or policyholders. In health insurance, high denial rates paired with very low appeal take-up mean many consumers pay or forego care rather than navigate complex appeals. In property and auto, claims closed without payment after disasters or total-loss disputes create repair gaps and living-cost strains. These harms compound when families need care urgently or housing is uninhabitable.

• Out-of-pocket exposure: bills due to denied or partially paid claims
• Delayed payouts: liquidity stress, borrowing, and forgone care
• Administrative burden: time and documentation costs suppress appeals
• Credit damage: unpaid balances sent to collections

How concentrated market power amplifies adverse outcomes

When markets concentrate, competitive discipline weakens. Empirical work on ACA marketplaces finds each additional insurer reduces benchmark premiums by roughly 5–7%, implying that high concentration is associated with higher prices. With fewer rivals, insurers can lean more on tight utilization management and aggressive claims edits without losing many customers, especially where switching costs are high. Over time, this can normalize higher denial rates, slower adjudication, and narrower networks, while consumers face higher premiums and fewer avenues to escape poor service.

• Pricing channel: higher premiums in high-HHI counties (ACA literature)
• Service channel: weaker incentives to invest in faster, fairer claims handling
• Choice channel: limited carrier options raise switching costs and entrench practices

Equity and disparate impact

Denials and reduced payouts are not distributed evenly. Lower-income households are more likely to hold medical debt and face collection actions tied to unpaid or denied claims. Communities of color are disproportionately exposed to affordability and availability challenges in property insurance markets, especially in climate-risk areas where carriers retrench. In health coverage, parity enforcement has documented patterns of higher denial exposure for behavioral health services, affecting people with disabilities and low-income enrollees who have fewer alternatives and less capacity to appeal.

• Low-income consumers: greater sensitivity to out-of-pocket shocks and appeal frictions
• People with disabilities and behavioral health needs: higher exposure to utilization management denials
• Communities of color in catastrophe-prone areas: slower recovery and higher nonpayment rates due to market withdrawal and underwriting changes

Consumer redress and policy remedies

Effective remedies combine individual action with policy guardrails. Individually, consumers should request written denial rationales, file timely internal appeals, and seek external review where available. Filing a complaint with the state Department of Insurance can prompt escalation. Medicare Advantage enrollees should request expedited appeals and, if needed, independent review. Policymakers can improve transparency by standardizing denial reason codes, publishing plan-level appeal and overturn metrics, strengthening parity enforcement, and supporting ombuds or consumer assistance programs. Competition policy that attracts additional carriers can reduce premiums and pressure plans to improve claims practices.

1. Ask for the detailed denial rationale and your policy citation
2. Submit an internal appeal within the deadline; include medical and billing documentation
3. Request external review or independent medical review if the internal appeal fails
4. File a complaint with your state insurance department and seek assistance from ombuds programs
5. For Medicare Advantage, request an expedited appeal and escalate to the independent review entity when appropriate

Technology trends and disruption: automation, AI, and compliance (including Sparkco context)

Automation and AI are reshaping claims adjudication from intake to settlement, compressing cycle times while raising material risks around AI claim denial bias, auditability, and regulatory scrutiny. Carriers that combine rigorous governance with transparent workflows can capture efficiency without amplifying denial-driven tactics, and solutions like Sparkco can streamline submissions while embedding compliance and ethical safeguards.

Claims operations are in the middle of a structural shift: deterministic rules engines, machine learning triage, fraud scoring, and robotic process automation (RPA) are converging into end-to-end platforms. The same tooling that reduces leakage and expense ratios can also entrench denial-centric logic if not checked by clear governance, explainability, and regulatory controls. The competitive stakes are high as regulators sharpen oversight of automated decision-making and as innovators (including Sparkco) seek to bypass legacy friction with transparent, auditable flows.

Overview of automation and AI use cases in claims

What is being automated across the claims lifecycle

Automation spans the entire claim journey. Deterministic rule engines handle eligibility, coverage, and medical policy edits; ML models prioritize queues and recommend settlements; fraud analytics score entities and networks; platforms coordinate people and documents; and RPA/OCR eliminates rekeying. Writers should map how each tool touches adjudication and denial decisions, and where human judgment remains the control point.

• Rule-based automation: codified policy, contract terms, and regulatory edits
• ML/AI triage: severity, complexity, propensity-to-litigate, and next-best action
• Predictive analytics: fraud, eligibility, and provider/network risk
• Claims management platforms: workflow, audit, reporting, and data lineage
• RPA/IDP: intake, document parsing, and cross-system entry (with exception handling)

Cost/benefit for insurers and evidence on denials

Economic upside is well-documented: lower loss adjustment expense, reduced leakage, and faster cycle times. Vendor white papers and carrier MD&A disclosures frequently cite 15–30% productivity gains, 20–40% cycle-time reductions, and measurable fraud savings; however, these are often self-reported and require independent verification and ongoing measurement. Analysts should demand study designs with control groups, pre/post segments, and subgroup fairness metrics before accepting claims.

Evidence on denials is mixed. Straight-through processing of simple, clean claims can reduce friction and lower denial rates for low-risk cohorts. Conversely, aggressive fraud/eligibility scoring and overly restrictive rule edits can lift denial rates, sometimes disproportionately affecting protected classes or vulnerable populations. To claim that automation reduces denials, carriers must show longitudinal metrics: denial rate mix, appeal uphold rates, average time-to-pay, and disparity analyses (e.g., by geography, language, age). This is central to AI claim denial risk assessment and claims automation compliance.

Risks: bias, auditability, and regulatory guardrails

Regulators have sharpened guidance on automated decision-making in insurance. The FTC’s 2021/2023 advisories emphasize truthfulness, fairness, transparency, and the need for meaningful human oversight, clear explanations, and robust documentation for adverse actions. At the state level, Colorado’s AI rules (2024, life insurance) require governance programs, model inventories, risk assessments, and bias testing; New York DFS (2024 proposed AI/ADM circular) and Connecticut DOI (2023 notice) set expectations for explainability, vendor oversight, and data governance; NAIC’s 2024 Model Bulletin provides a national template for AI oversight, including third-party risk management. These frameworks converge on four themes: accountability, explainability, continuous monitoring, and consumer protection.

Auditability and explainability are not optional: carriers must reconstruct the decision path, link data to features and rules, and provide consumer-facing reasons for denials. Disparate impact remains a core legal risk where proxies (ZIP code, device type, repair shop network) correlate with protected classes. Health contexts offer cautionary tales: oversight bodies have scrutinized algorithm-driven denials and prior authorization tools, underscoring the need for human-in-the-loop review and appeal processes.

Best practices for governance, audits, and transparency

Robust governance turns efficiency into durable advantage without fueling denial-driven tactics. The following practices align with FTC, NAIC, and leading DOI expectations and should be contractually flowed down to vendors.

• Model inventory and risk classification with owners, use cases, data lineage, and regulatory mapping
• Pre-deployment testing: accuracy, stability, and bias metrics (TPR/FPR, calibration) with subgroup analysis
• Explainability: reason codes for every automated adverse action; consumer-readable notices
• Human-in-the-loop controls: thresholds for auto-approve vs. auto-deny; mandatory manual review for edge cases
• Monitoring: drift, performance, and fairness dashboards with alerting and retraining policies
• Auditability: immutable logs of inputs, rules/model versions, and override rationales retained per records policy
• Vendor risk management: independent validation of vendor claims, right-to-audit clauses, SOC reports, and incident SLAs
• Privacy and security: data minimization, PHI/PII safeguards, and role-based access

Bureaucratic inefficiency, market power, and the Sparkco angle

Legacy gatekeepers often wield process complexity as market power: opaque submission rules, discretionary edits, and slow communication increase abandonment and denials. Automation solutions can invert this dynamic. For example, a platform like Sparkco can standardize intake, pre-validate documentation, surface coverage rules up front, and share real-time status and explanations. The net effect is fewer discretionary touchpoints, lower friction, and stronger appeal records—benefiting legitimate claimants. To avoid replicating denial-driven behaviors, Sparkco claims automation must embed guardrails: visible reason codes, easy human escalation, bias-tested scoring, and regulator-ready audit trails. This is how innovation improves transparency while remaining compliant and ethical.

Research directions and sources to cite

Ground writing in verifiable sources and quantify effects with reproducible methods. Cite vendor claims only with independent corroboration or clear caveats.

• Regulatory guidance: FTC (2021/2023 AI fairness advisories), NAIC 2024 Model Bulletin on AI Systems, Colorado DOI 2024 AI rules (life insurance), New York DFS 2024 proposed AI/ADM circular, Connecticut DOI 2023 notice, and relevant state Fair Claims Settlement Practices
• Peer-reviewed and government evaluations: studies of algorithmic bias in risk/health decision tools; OIG and GAO reports on automated denials and prior authorization; publish subgroup metrics where available
• Vendor white papers and case studies: Guidewire, Duck Creek, Shift Technology, SAS, CCC Intelligent Solutions, Tractable; require methodology, sample sizes, and third-party audits
• Patents and SEC MD&A: USPTO filings on automated adjudication and image-based appraisal; carrier 10-K/MD&A (e.g., Progressive, Allstate, Lemonade) discussing automation investments and impacts

Economic drivers and constraints: profitability, pricing, reinsurance, and capital

Insurers’ claims handling sits at the nexus of underwriting profitability, reinsurance costs, investment income, and capital adequacy. When investment yields are low, reinsurance is expensive or scarce, or capital is pressured, firms have stronger incentives to tighten adjudication and reduce paid loss severity and frequency. Reinsurance pricing, exclusions, and capital charges reshape marginal costs of paying claims, while reserve development and macro shocks can shift denial behavior. An econometric framework is outlined to test how market concentration interacts with these drivers to influence denial rates.

Claims decisions are economic decisions. They are governed by an insurer’s expected margin on each policy cohort, the marginal cost of risk transfer, the shadow value of capital, and the present value of investment income on float. In soft markets with robust investment returns and ample reinsurance, insurers can afford more generous settlement behavior. In hard markets with thin underwriting margins, constrained capital, and costly reinsurance, insurers face sharper incentives to reduce paid losses via stricter claims handling. These dynamics underpin insurance profitability claims denials, reinsurance impact denials, and combined ratio denial incentives.

At the macro level, US property-casualty results since 2010 show alternating regimes. The industry average combined ratio hovered near break-even across the cycle, with strong years supported by reserve releases and weaker years dominated by catastrophe and inflation shocks. Post-2020 reinsurance hardening, supply constraints, and elevated loss-cost inflation in auto and homeowners tightened margins, particularly in personal lines. Meanwhile, the rate environment pivoted: a decade of low yields reduced the contribution of investment income to earnings, then 2022–2024 rate increases improved new-money yields while depressing mark-to-market capital and raising required returns on equity.

Profitability mechanics and claims incentives

Profit maximization connects directly to loss adjudication. The combined ratio (loss + expense divided by earned premium) proxied core underwriting profitability; ratios above 100% make earnings increasingly dependent on investment income. From 2010–2016, favorable loss trends and reserve releases buoyed results in several lines; since 2017, elevated catastrophe losses and social and parts-and-labor inflation pushed personal lines combined ratios above 100%, peaking in homeowners around 2023. When underwriting is thin, the marginal benefit of denying or negotiating down a claim rises, especially for short-tail lines that immediately affect the accident-year loss ratio.

Two additional levers interact with claims: investment income and capital. Low yields in the 2010s reduced the ability to subsidize underwriting with portfolio returns, increasing pressure to control paid losses. Rising rates post-2022 improve forward investment income, but can reduce GAAP equity via unrealized losses, tighten risk-based capital buffers, and elevate hurdle rates. If RBC ratios drift toward action thresholds, the shadow cost of capital increases; strict claim adjudication conserves capital by lowering required reserves and reinsurance spend.

Quantifying macro and micro drivers

A data-driven view ties incentives to observable metrics: combined ratio trends, premium growth, reinsurance cost and availability, capital adequacy, catastrophe exposure, inflation, and interest rates. Analysts should extract line-of-business combined ratios and reserve development from company statutory statements and 10-Ks; use S&P Global or AM Best for industry aggregates; pull risk-free and corporate yields from Federal Reserve H.15; and use Guy Carpenter or Aon for reinsurance pricing and capacity indicators.

• Underwriting margins: Industry combined ratio near 99% on average since 2010, with personal lines materially worse in 2022–2023 and homeowners around 110% in 2023.
• Premium growth: Hard market conditions since 2021 drove strong rate increases, but earned premium lag keeps near-term margins sensitive to loss trends.
• Investment income: Depressed yields through 2021 increased reliance on underwriting; post-2022 new-money yields improved but capital volatility rose.
• Reinsurance: 2018–2020 moderate hardening; 2021–2023 sharp, often double-digit increases with higher attachments and exclusions; 2024 plateau at elevated levels.
• Capital and RBC: Higher asset and catastrophe risk charges raise the cost of growth; proximity to RBC action levels amplifies incentives to reduce paid and incurred losses.
• Catastrophe exposure: Higher catastrophe share raises earnings volatility; after large events, tighter coverage terms and claim scrutiny often emerge.
• Inflation: Loss-cost inflation in auto and property elevated severity; medical and wage inflation raise long-tail reserves.
• Interest rates and reserving: For long-tail lines, discount-rate assumptions and yield curves influence reserve adequacy; rate spikes can trigger adverse development if prior picks were optimistic.

Indicative industry metrics and trends

Reinsurance pricing, coverage terms, and denial incentives

Reinsurance translates into an explicit marginal cost of paid losses. When risk-adjusted rates rise and attachments move higher, primary insurers retain more frequency losses and face steeper costs on severity tails. Exclusions and tightened wordings shift more dispute risk back to the cedent. The incentive effect is twofold: higher expected retained loss per claim and higher volatility impose capital costs, making strict coverage interpretation and fraud detection economically attractive. Conversely, abundant, inexpensive reinsurance can support more accommodative settlement because marginal retained cost is lower.

Hard-market dynamics since 2021 also changed portfolio strategy: insurers de-emphasized catastrophe-volatile geographies, raised deductibles, and moved to actual cash value on roofs. Each change reduces expected loss costs and increases the probability that claims fall below attachments or policy thresholds, effectively increasing denial or partial-payment rates for marginal claims.

Capital, reserving, and earnings management

Capital constraints magnify denial incentives. Lower RBC headroom increases the value of reducing incurred losses because it lowers required capital via both underwriting and catastrophe charges. Rising interest rates improve future investment income but can depress statutory and GAAP surplus through asset valuation, which tightens capital even as underwriting improves.

Reserve development is a core earnings lever. In benign periods, prior-year releases support combined ratios; in stressed periods, adverse development erodes margins and can trigger further tightening of claim adjudication and coverage interpretation. Firms facing weak investment income or reinsurance pressure may prefer to keep initial case reserves conservative and pursue aggressive SIU activities, affecting observed denial and dispute rates. Researchers should extract Schedule P triangles, paid-to-incurred ratios, and changes in ultimate loss picks to proxy reserve behavior.

Econometric framework to test denial incentives

A panel specification can test whether market concentration predicts higher denial rates after controlling for profitability, reinsurance, capital, and macro shocks:

DenialRate i,s,l,t = β1 HHI s,t + β2 CombinedRatio i,t−1 + β3 ReinsRateIndex l,t + β4 RBC i,t−1 + β5 InvYield i,t + β6 LossCostInflation s,l,t + β7 CatExposure i,l + α i + δ s + γ t + ε i,s,l,t

Units: insurer i, state s, line l, year or quarter t. DenialRate is the share of reported claims denied or partially denied. HHI is state-line Herfindahl-Hirschman Index from market shares. CombinedRatio and InvYield from company financials. ReinsRateIndex from Guy Carpenter or Aon (property-cat, liability, or per-risk indices). RBC from statutory filings. LossCostInflation from CPI by component or industry severity indices. CatExposure is the share of premiums in wind, quake, or convective-storm states. α i are insurer fixed effects, δ s state fixed effects, γ t time fixed effects.

Estimation: OLS with insurer and state clustering; include insurer-by-line trends. Alternative outcomes include dispute rates, litigation rates, and average paid severity conditional on claim. To study dynamics, interact HHI with hard-market years or reinsurance rate shocks. A difference-in-differences design can compare high-cat-exposed insurers (treated) versus low-exposed (controls) around 2021–2023 reinsurance hardening. For identification, consider instruments for HHI such as lagged approvals of new entrants or state-level M&A approval shocks, and for reinsurance prices using global retrocession capacity shocks or cat bond spread indices unrelated to local claim propensity.

Caveats, endogeneity, and interpretation

Do not claim causality without credible identification. Concentration, profitability, and denial behavior are jointly determined. High-denial carriers may deter marginal claims, altering measured rates; reinsurance purchases are endogenous to risk mix; reserve choices reflect private information. Use lagged controls, firm and market fixed effects, and instruments or natural experiments to separate supply-side pressures from demand-side claim incidence. Validate with falsification tests, pre-trend checks, and alternative measures (e.g., denied-to-reopened ratios).

Macro shocks matter. Catastrophes and pandemics shift both claim incidence and insurer constraints: surges in frequency strain adjuster capacity and SIU resources; capital drawdowns and reinsurance resets reinforce strict adjudication. Document timing using catastrophe loss indices and quarterly disclosures to link shifts in denial behavior to shocks rather than secular trends.

Policy and reform recommendations: antitrust, transparency, and consumer protection

This section presents 10 evidence-backed insurance reform recommendations across antitrust, transparency insurance denials, consumer protection, governance, and technology oversight. It specifies rationales, implementation paths, costs and benefits, enforcement metrics, and examples where similar policies have worked, while distinguishing empirical findings from normative proposals.

Consolidation, opaque utilization management, and uneven oversight can create incentives for profit-maximizing denials. Empirical evidence from federal marketplace data and state independent medical review programs shows wide variation in denial rates and substantial overturn rates on appeal. The insurance reform recommendations below prioritize targeted, enforceable changes that rebalance incentives, expand transparency, and protect consumers without imposing unrealistic administrative burdens. Where evidence is strong, we state it plainly; where recommendations are normative, we explain why and how to measure impact.

Summary of recommendations and primary KPIs

Recommendation 1: Product-line HHI thresholds and structural presumptions (antitrust insurance merger reform)

• Rationale (empirical): Insurer concentration varies by product line and region; DOJ/FTC litigation (e.g., Anthem-Cigna; Aetna-Humana) found likely harms in MA and large-group markets.
• Implementation path: Federal: DOJ/FTC guidelines and consent decrees; State: insurance commissioners adopt product-line HHI screens by rating area. Presumption of harm at post-merger HHI above 2000 with delta 100 or more, with stricter 1800 thresholds in highly localized markets.
• Costs/benefits: Modest review costs; benefits from preserved competition and slower premium growth. Administrative burden estimated at 1–3 basis points of premiums for enhanced data collection.
• Enforcement metrics: HHIs by county/product; number of blocked deals/divestitures; premium and denial trends vs control markets.
• Examples: Court rejections of mega-mergers in MA lines; EU competition authorities’ structural remedies in insurance and related sectors.
• Normative note: Lowering thresholds below current guidelines is normative but testable via KPI trends.

Recommendation 2: Vertical integration guardrails and structural separation triggers

• Rationale (empirical): Vertical combinations (insurer-PBM-provider-data) can enable foreclosure and self-preferencing, raising denial incentives for out-of-network or rival-affiliated providers.
• Implementation path: Federal/state merger review conditions requiring firewalls, open-access data interfaces, and structural separation triggers if KPIs indicate harm.
• Costs/benefits: Compliance programs and monitors (1–2 bp premiums) vs benefits of reduced foreclosure risk and fair network access.
• Enforcement metrics: Affiliate share of claims spend; rival access complaints; out-of-network price and denial differentials.
• Examples: Conditions in UnitedHealth/Change with divestitures; EU behavioral and structural remedies in vertical deals.
• Normative note: Structural separation triggers based on KPI thresholds are prescriptive but enforceable.

Recommendation 3: Denial-rate public reporting and standardized metrics (transparency insurance denials)

• Rationale (empirical): CMS marketplace data show wide variation in denial and appeal rates; state IMR programs find substantial overturns, indicating material error rates.
• Implementation path: Federal rule extending marketplace-style reporting to group and MA lines; state adoption via insurance codes; external data audits.
• Costs/benefits: IT and audit costs (2–5 bp); benefits include consumer choice, oversight targeting, and pressure against profit-maximizing denials.
• Enforcement metrics: Denial, appeal, and overturn rates by product and service; timeliness; completeness of submissions.
• Examples: CMS marketplace reporting; California DMHC IMR public reports; EU Solvency II Pillar 3 disclosure norms.
• Normative note: Harmonized cross-line metrics are prescriptive; auditability reduces gaming.

Recommendation 4: Prior authorization transparency and service-level standards

• Rationale (empirical): Delays and opaque criteria drive avoidable denials and care deferral. CMS 2024 prior authorization rule sets timeliness baselines.
• Implementation path: Federal and state rules mandating public PA criteria, approval/denial statistics, and SLA guarantees with auto-approval if deadlines are missed.
• Costs/benefits: Workflow upgrades (2–4 bp) vs reduced provider burden and faster access.
• Enforcement metrics: Turnaround times, auto-approvals, denial reasons, and backlog size by service.
• Examples: CMS prior authorization final rule; Texas SB 1742 requires PA posting and statistics; UK FCA Consumer Duty on outcomes.
• Normative note: Extending auto-approval to additional services is prescriptive but measurable.

Recommendation 5: Faster appeals with default approvals when deadlines are missed

• Rationale (empirical): Appeal overturns in several states’ IMR programs suggest initial denials often lack merit; delays compound harm.
• Implementation path: Amend federal ERISA and state laws to shorten timelines and require automatic approvals if plan fails to decide on time.
• Costs/benefits: Staffing costs (1–3 bp); benefits include reduced patient harm and fewer escalations.
• Enforcement metrics: Median appeal times; share of default approvals; repeat denials by issuer.
• Examples: Federal marketplace appeal standards; state IMR timelines showing feasibility.
• Normative note: Default approvals expand existing missed-deadline protections; impacts tracked via KPIs.

Recommendation 6: Strengthened independent medical review (IMR) and targeted penalties

• Rationale (empirical): High IMR overturn rates in some categories indicate systematic issues in initial reviews.
• Implementation path: State expansion of IMR scope and funding; penalty schedules tied to persistently high overturn rates by category; reinvest penalties in patient assistance.
• Costs/benefits: Reviewer contracts and admin (1–2 bp) vs improved accuracy and deterrence of wrongful denials.
• Enforcement metrics: Overturn rates by category; penalty incidence; re-offense rates within 12 months.
• Examples: California DMHC IMR program; international ombuds models in EU member states.
• Normative note: Penalty thresholds are prescriptive; calibrate via pilot and publish results.

Recommendation 7: Targeted fee-shifting for unjustified denials and bad faith

• Rationale (empirical): Low appeal rates and litigation costs deter consumers from challenging wrongful denials.
• Implementation path: Federal ERISA amendment enabling one-way fee-shifting where denials are not substantially justified; state-level for non-ERISA plans; safe harbor for robust internal review.
• Costs/benefits: Legal reserve increases (variable) vs deterrence of meritless denials and faster settlements.
• Enforcement metrics: Rate of fee awards; time to resolution; recurrence of similar denial rationales.
• Examples: State bad-faith statutes awarding fees in P&C; international consumer redress schemes.
• Normative note: Scope of fee-shifting is prescriptive; evaluate via sunset and mandated review.

Recommendation 8: Revolving-door limits and lobbying transparency in insurance oversight

• Rationale (empirical): Regulatory capture risks rise with rapid cycling between regulator and industry roles.
• Implementation path: State and federal 2-year cooling-off for senior officials; mandatory disclosure of ex parte meetings and granular lobbying spend; publish comment-to-final-rule change logs.
• Costs/benefits: Minimal administrative costs; benefits include greater public trust and reduced capture risk.
• Enforcement metrics: Compliance with cooling-off rules; completeness of lobbying disclosures; public access metrics.
• Examples: Federal ethics rules; EU transparency registers; several U.S. states’ meeting-disclosure practices.
• Normative note: Longer cooling-off periods are prescriptive; adopt with periodic evaluation.

Recommendation 9: Audits and human-in-the-loop requirements for automated denial systems

• Rationale (empirical): Automated tools can scale errors and bias; GDPR Article 22 provides a right to human review; U.S. states are moving to govern AI in insurance.
• Implementation path: State rules modeled on Colorado SB 21-169 and New York DFS guidance: model documentation, impact assessments, bias testing, and mandatory human confirmation before adverse determinations.
• Costs/benefits: Model governance and audit costs (2–5 bp) vs reduced error and legal risk; improved fairness.
• Enforcement metrics: Audit pass rates; share of denials with documented human review; disparity indices by protected class where lawful.
• Examples: Colorado algorithmic governance regulations; EU and UK guidance on automated decision-making.
• Normative note: Scope of pre-approval audits is prescriptive; phase in with pilot reporting.

Recommendation 10: Vendor accountability and registries for denial-related technology

• Rationale (empirical): Third-party administrators and vendors often build rules engines that drive denials, yet face weaker oversight.
• Implementation path: Require insurers to certify vendor compliance with the same audit, transparency, and human-review standards; establish state registries of approved models with versioning.
• Costs/benefits: Contracting and certification costs (1–2 bp) vs reduced systemic risk and faster incident remediation.
• Enforcement metrics: Vendor audit coverage; time to fix identified defects; frequency of model-related incidents.
• Examples: Financial services model risk management frameworks; health IT certification regimes.
• Normative note: Public registries are prescriptive; start with regulator-only access and move to public summaries.

State and international transparency precedents (evidence highlights)

State examples include Texas SB 1742 requiring posting of prior authorization requirements and statistics, and California’s DMHC publishing annual Independent Medical Review reports with overturn rates. Federally, CMS publishes plan-level denial and appeal information for marketplace plans and has adopted 2024 prior authorization timeliness rules. Internationally, EU Solvency II mandates public Pillar 3 disclosures; GDPR Article 22 ensures human review of automated decisions; and the UK FCA Consumer Duty raises expectations for transparent claims and fair outcomes.

Future outlook, scenarios, and investment & M&A activity

Over the next 3–10 years, shifting interest rates, regulation, AI adoption, catastrophe losses, and evolving antitrust enforcement will shape market concentration and the future of insurance claim denial. We outline three probabilistic scenarios with quantified KPI ranges and synthesize implications for investors, regulators, and consumer advocates, alongside an insurance M&A outlook and an investment risk checklist focused on insurtech investment denial risk.

M&A and investment activity analysis and risks

Scenario framework and key drivers

We frame the next 3–10 years using three scenarios anchored to five drivers: regulatory reform (state DOIs, NAIC model updates, and federal antitrust policy), tech adoption (AI-driven underwriting, fraud analytics, claims triage), catastrophic loss experience and reinsurance pricing, interest rate environment, and antitrust enforcement of mergers and serial roll-ups. The interaction of higher-for-longer rates with capital-light distribution, plus stricter scrutiny of denial-driven profit strategies, will determine the future of insurance claim denial and market concentration.

Baseline reference points for 2024: industry combined ratios near 100–103 depending on line of business; claim-denial performance varies widely by product and channel; concentration is rising within specialty niches and distribution, as evidenced by high buyer concentration in deal activity. Our quantified ranges below are directional guideposts, not forecasts.

• Regulatory reform: Unfair claims settlement enforcement, AI governance standards, and model act revisions affecting denials and appeals.
• Tech adoption: Automated claims decisioning, straight-through processing, and anti-fraud tools influencing denial rates and loss ratios.
• Catastrophic losses: Climate-driven severity and frequency shaping capital needs, pricing, and reinsurance dependence.
• Interest rates: Investment income tailwinds vs financing costs for M&A and capacity provision.
• Antitrust enforcement: 2023 Merger Guidelines, serial acquisition scrutiny, and remedies slowing consolidation in targeted niches.

Three scenarios: triggers, probabilities, and quantified KPIs

We assign probabilities and quantify impacts on claim-denial rates, combined ratios, and concentration metrics. KPI ranges are indicative for mass-market P&C and selected specialty segments; specific company outcomes will vary by geography, mix, and operating model.

• Best case (30%): Regulation clarifies AI use in claims, interest rates ease modestly, and catastrophe losses normalize. Triggers: state DOI guidance harmonization, credible AI audit frameworks, and adequate reinsurance capacity. KPI ranges by 2028–2034: claim-denial rates trend down to 8–10% in mass-market lines with improved triage and appeals; industry combined ratio stabilizes at 96–100; HHI in targeted brokerage/MGA niches rises only 50–150 points, with top-10 buyer share of deal activity slipping to 50–54% as more bidders re-enter.

• Base case (50%): Mixed macro backdrop with rates gradually declining, periodic catastrophe spikes, and selective antitrust challenges. Triggers: uneven AI performance, moderate cat seasons, and steady PE interest. KPIs: claim-denial rates steady to slightly higher at 10–13%; combined ratio ranges 99–103 with pricing discipline and investment income offsetting loss volatility; niche HHI increases 200–400 points; top-10 buyer share of deal activity holds near 54–58%.

• Worst case (20%): Adverse cat seasons, higher-for-longer rates, and uneven AI governance encourage denial-driven tactics to protect margins; antitrust enforcement is selective, allowing continued roll-ups in certain niches. Triggers: multiple severe CAT years, reinsurance hardening, and cost of capital pressures. KPIs: claim-denial rates drift up to 12–16%; combined ratio at 103–107; HHI in certain local or product niches up 500–900 points; top-10 buyer deal share climbs to 60–65% as capital concentrates.

• Cross-scenario notes: In all cases, transparency and appeal mechanisms mitigate litigation and regulatory risk. Where automated triage is used without robust human-in-the-loop controls, volatility in denial rates and complaint ratios rises markedly.

Implications for investors, regulators, and consumer advocates

Investors: The insurance M&A outlook favors scale acquirers in specialty distribution, MGAs, and services, but valuation premia increasingly hinge on proof of sustainable loss advantage and compliant claims handling. Insurtech investment denial risk will be a central diligence theme, as payers and enablers using opaque decisioning face higher enforcement and brand risks.

Regulators: Data access to denial rationales, model documentation, and appeals outcomes will be pivotal. Enhanced monitoring of complaint ratios and repeat-offender patterns can deter denial-driven profit strategies without stifling automation that improves speed and accuracy.

Consumer advocates: Push for clear, machine-readable denial explanations, time-bound appeals SLAs, and periodic third-party audits of decisioning systems. Public dashboards of complaint ratios and reversal rates can sharpen market discipline.

• Quantitative guardrails: publish denial and appeal reversal rates by product and channel; track combined ratio movements alongside denial trends to detect risk shifting vs real efficiency.
• Concentration watch: focus on local HHIs in distribution and capacity control in MGAs, not just national carrier counts.
• Reinsurance dependence: monitor ceded share and counterparty concentration to anticipate denial pressure in stress periods.

Investment and M&A outlook

Deal pipelines remain active for distribution, MGAs, TPAs, and data/analytics enablers, while carrier-to-carrier consolidation is selective. High buyer concentration persists, with the top decile of acquirers executing a majority share of deals. Multiples are more resilient for assets with profitable growth, strong data assets, and demonstrated compliance maturity in claims and AI governance.

PE continues to supply sponsor-backed platforms for roll-ups, but antitrust enforcement since 2020 has raised execution risk for serial acquisitions, particularly where overlaps and entrenchment theories apply. Expect longer timelines, more data requests around claims practices, and a higher likelihood of divestitures in overlapping geographies or micro-niches.

Deal pipeline, valuations, and MGA dynamics

Specialty distribution and MGAs remain the center of gravity due to capital-light economics and the ability to control customer experience. Consumer insurance verticals saw a material deal volume decline (>27% in 2024 vs 2023), but resilient niches still clear elevated valuations relative to broader market averages. Given the shift from growth-at-all-costs to profitability, late-stage insurtechs that can document fair-claims outcomes have improved access to capital.

Quantitatively, we expect modest volume growth in small-to-mid tickets, subdued mega-deals, and pricing bifurcation: premium multiples for clean compliance histories and verifiable underwriting lift; discounts for opaque or high-denial models with reputational risk.

• Hot spots: specialty P&C MGAs, cyber, E&S distribution, and claims-tech enablers with auditable AI.
• Cooling zones: broad consumer personal lines distributors without proprietary data or cost advantage.
• Valuation swing factors: unit economics durability, reinsurance stability, denial transparency, and model governance maturity.

Antitrust risk factors and approval dynamics

Post-2023, the enforcement lens emphasizes cumulative effects of serial acquisitions, data advantages, and foreclosure risks. Transactions that combine distribution scale with control over capacity or data pipes will face deeper scrutiny. Parties should proactively map overlaps by geography and product and prepare mitigation packages early.

Expect agencies to probe the interaction between AI-driven claims practices and market power, particularly where denial rates rise alongside consolidation. Remedies may include divestitures, firewalls around sensitive data, or conduct commitments tied to fair-claims metrics.

• Risk flags: high local HHI jumps (>200 points), exclusive capacity agreements, and evidence of denial rate spikes post-acquisition.
• Mitigations: transparent claims KPIs, third-party audits, and structured data-sharing without disadvantaging rivals.
• Timeline: plan for extended second requests and potential consent decrees in concentrated niches.

Investment risks and opportunities: due diligence checklist

Investors should calibrate underwriting and compliance alongside financial metrics. The following checklist targets denial-driven risks while identifying value creation paths that survive stricter oversight.

• Claims governance: documented policies for AI/ML use, model explainability, and human-in-the-loop thresholds; appeal reversal rates and time-to-resolution trends.
• Regulatory exposure: history of market conduct exams, consent orders, complaint ratios vs peers, and remediation outcomes.
• Data lineage: integrity of training data, bias testing cadence, and vendor oversight for third-party decisioning engines.
• Unit economics: loss ratio lift tied to underwriting signal rather than denial frequency; evidence via cohort analysis.
• Reinsurance and capacity: diversification of counterparties, rate-on-line sensitivity, and downgrade stress tests.
• Operational resilience: CAT response playbooks, surge staffing, and fraud ring detection without indiscriminate denials.

Monitoring metrics and early-warning signals

Stakeholders should maintain a compact dashboard to track scenario drift and concentration risks while aligning to the insurance M&A outlook. Metrics below are designed for monthly or quarterly refresh and can be benchmarked at the line-of-business or geography level.

• Claim-denial metrics: denial rate, appeal rate, appeal reversal rate, and complaint ratio; track by channel and segment.
• Profitability and pricing: combined ratio by line; frequency/severity indices; reinsurance rate-on-line and attachment changes.
• M&A concentration: HHI by niche, top-10 buyer share of deal activity, and deal volume mix (platform vs add-on).
• Capital markets: 10Y Treasury yield, high-yield spreads, and equity beta for brokers/MGAs/insurtechs.
• Regulatory signals: DOJ/FTC challenge and remedy rates; state DOI enforcement actions related to unfair claims.
• Technology governance: share of claims straight-through processed; percentage of adverse decisions with explainability artifacts available for audit.

Bottom line

Across scenarios, consolidation continues but at different speeds and with evolving guardrails. Firms that combine explainable automation with disciplined underwriting and transparent consumer outcomes are positioned to benefit regardless of the path the market takes. Incorporating insurtech investment denial risk into diligence and portfolio monitoring can reduce downside while preserving exposure to the productivity gains that modern claims and distribution technologies can deliver. This balanced approach is core to navigating the future of insurance claim denial, market concentration, and the shifting terrain of investment and M&A.