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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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48 results for insurance regulation

Study aims to measure and mitigate biases in motor insurance pricing.

problem Ethical biases in motor insurance pricing that affect fairness and regulatory compliance.
method Statistical methodologies and data analysis to measure and mitigate biases.
result Developed tools to measure and mitigate ethical biases in motor insurance pricing.

We show that any objective risk measurement algorithm mandated by central banks for regulated financial entities will result in more risk being taken on by those financial entities than would otherwise be the case. Furthermore, the risks taken on by the regulated financial entities are far more systemically concentrate…

2010-04-10abs ↗pdf ↗

New method reduces indirect discrimination in insurance risk models.

problem Indirect discrimination in insurance risk models using machine learning.
method Mathematical concepts of linear algebra to reduce indirect discrimination.
result Demonstrated promising performance in a concrete case of risk selection in life insurance.

The European insurance sector will soon be faced with the application of Solvency 2 regulation norms. It will create a real change in risk management practices. The ORSA approach of the second pillar makes the capital allocation an important exercise for all insurers and specially for groups. Considering multi-branches…

2015-06-12abs ↗pdf ↗

New fairness criterion for risk-sensitive decisions in regulated industries.

problem Ensuring equitable outcomes in risk-sensitive decision-making.
method Marginal fairness for generalized distortion risk measures, two-step decision-making process.
result Ensures fairness in decision-making under risk measures, regardless of protected attributes.

The article proposes a method to make valid insurance claim predictions without relying on specific models.

problem Prediction of insurance claims using statistical models can be unreliable due to model misspecification, selection effects, and lack of finite-sample validity.
method The article employs conformal prediction, a machine learning strategy that is model-free and tuning-parameter-free, ensuring finite-sample validity.
result The proposed method guarantees valid predictions at a pre-assigned coverage probability level and performs well in insurance applications, including meeting Solvency II requirements.

Deviance Voronoi residuals improve earthquake insurance risk assessment.

problem Assessing earthquake insurance risk using spatio-temporal point process models.
method Extended Voronoi residuals and created simulation-based approach.
result Proposed formula for country-wide minimum capital test.

The study examines how different interpolation methods affect the decomposition of life insurance surplus.

problem The impact of different interpolation methods on the decomposition of life insurance surplus.
method The study uses the IASU decomposition method to analyze the effects of different interpolation methods (Lee-Carter and linear) on the surplus decomposition.
result Lee-Carter and linear interpolation yield almost identical decompositions, while constant approximations result in different decompositions.

Mack-Net model combines Mack's model with RNNs for better insurance liability estimation.

problem Accurate estimation of insurance liabilities for better financial decision-making.
method Integrates Mack's reserving model with Recurrent Neural Networks (RNNs).
result Improves accuracy of general insurance liability assessment.

A novel multi-objective optimization framework improves insurance pricing fairness.

problem Exacerbated trade-offs between competing fairness criteria in insurance pricing using machine learning.
method Proposes a novel multi-objective optimization framework using NSGA-II to jointly optimize accuracy and fairness criteria.
result Consistently achieves a balanced compromise between accuracy and fairness, outperforming single-model approaches.

Research simulates Lloyd's of London's specialty insurance market dynamics.

problem Quantitative study of complex market phenomena in Lloyd's of London.
method Discrete Event Simulation (DES) framework for Lloyd's of London specialty insurance market.
result Model shows sophisticated exposure management reduces syndicate insolvency, and syndication enhances actuarial price accuracy.

Framework for realistic insurance liability valuation.

problem Economic realism in insurance liability valuation.
method Replication approach of no-arbitrage theory, considering capital and fulfillment conditions.
result Identifies conditions for market price recovery and extends production for insolvency.

The paper examines the unexpected losses and risk ratios for co-monotonic alternatives in large portfolios.

problem Understanding the unexpected losses and risk ratios for large portfolios with co-monotonic alternatives.
method Analyzes the asymptotic behavior of unexpected losses and risk ratios for co-monotonic alternatives using monotone cash-additive risk measures and Choquet insurance premia.
result Unexpected losses of large weighted portfolios are of order o(nλn)o(n\overlineλ_n), where λn\overlineλ_n is the average weight.

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk measurement related optimization problem is robust, which we call "robustness against optimization". The new notion is studied for various classes of risk …

2018-09-25abs ↗pdf ↗

DAISYnt evaluates synthetic data quality and privacy in regulated domains.

problem Balancing data quality and privacy in regulated domains.
method Developed a suite of advanced tests (DAISYnt) to evaluate synthetic data quality and privacy.
result DAISYnt sets a de facto standard for synthetic data evaluation in regulated domains.

A new method for calculating ES from VaR under Solvency II.

problem The need for a more appropriate risk measure (ES) than VaR.
method Developed PELVE method for multiple insurers, analyzing existence, uniqueness, and expressions for different payoff distributions.
result The choice of method is crucial when payoffs are from different distribution families.

New risk measure improves creditor protection in financial regulation.

problem Current solvency requirements fail to control the size of recovery on creditors' claims.
method Developed Recovery Value at Risk (Recovery VaR) to control recovery on creditors' claims.
result Recovery VaR flexibly controls recovery on creditors' claims and integrates protection needs into management incentives.

GenAI improves actuarial practices through case studies.

problem Improving actuarial practices using AI.
method Four case studies using LLMs, Retrieval-Augmented Generation, and vision-enabled LLMs.
result GenAI enhances claim cost prediction, market comparisons, and car damage classification.

FL improves insurance claims loss prediction without sharing data.

problem Limited data volume and variety due to privacy concerns.
method Federated Learning (FL) to update a global model using local data insights.
result Improved claims loss forecasting compared to individual models.

Develops a transparent surrogate model for complex data.

problem Balancing accuracy and transparency in complex decision-making models.
method Partial dependence effects for feature engineering, smart segmentation, and GLM fitting.
result The maidrr GLM closely approximates a black box model and outperforms benchmarks.

This article examines methods to interpret complex models in insurance.

problem Ensuring transparency and understanding of data-driven decisions in insurance.
method Inventory of methods to interpret models, focusing on insurance applications.
result Increased interest in model transparency due to complex algorithms and GDPR requirements.

A new framework combines multiple loss reserving models for better predictive performance.

problem Combining multiple loss reserving models to improve predictive performance.
method Systematic framework that considers full distributional properties and features of reserving data.
result Optimized ensemble outperforms traditional methods and captures relevant quantiles.

Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.

problem Understanding the dependence structure between insurance and financial risks.
method Dynamic equilibrium analysis of insurance pricing with worst-case beliefs.
result Correlation ambiguity does not necessarily increase insurance prices or reduce insurers' utility.

Paper proves Pareto efficient insurance for multiple entities.

problem Optimizing insurance for multiple policyholders and insurers.
method Sum-minimization characterization and pairwise implementability analysis.
result Characterization of Pareto efficient insurance arrangements.

Study on systemic risk in European insurance sector, showing insurer connections during stress.

problem Understanding systemic risk connectedness in European insurance sector.
method Common connectedness framework applied to returns, volatility, value-at-risk, and expected shortfall.
result Insurers are a significant component of systemic risk connectedness, especially during stress episodes.

The paper examines how risk reduction and insurance choices interact under convex premium principles.

problem Interaction between self-protection and insurance demand under convex premium principles.
method Investigates optimal prevention efforts and insurance shares using distortion risk measures.
result Self-protection and insurance are complementary, but ex ante moral hazard can turn this into a substitution effect.

Parametric insurance offers better risk-sharing in high-risk settings than traditional indemnity insurance.

problem High-risk environments where traditional indemnity insurance is unaffordable or ineffective.
method Comparison of excess-of-loss indemnity insurance and parametric insurance within a mean-variance framework, considering fixed costs and binding budget constraints.
result Parametric insurance yields higher welfare for risk-averse individuals, especially when indemnity insurance is impractical.

Paper models demand and solvency for index insurance, combining traditional and measurable index-based coverage.

problem Reducing protection gaps for emerging risks.
method Develops a model for demand and solvency conditions, combining traditional and index-based insurance.
result Deduces a product that benefits from both traditional and index-based insurance approaches.

Reinsurance can help life insurers maintain higher capital guarantees without losing utility.

problem Decreasing capital guarantees in life insurance products.
method Dynamic investment-reinsurance optimization problem with simultaneous Value-at-Risk and no-short-selling constraints. Introduced guarantee-equivalent utility gain for comparison.
result Optimally managed reinsurance allows insurers to offer higher capital guarantees without reducing expected utility.