Algorithmic insurance tackles financial risks from AI errors, proving CVaR-optimal thresholds reduce tail risk.
problem High-stakes AI errors lead to heterogeneous losses, challenging traditional insurance assumptions.
method Analyzed binary classification performance to tail risk exposure, using CVaR to quantify extreme losses.
result CVaR-optimal thresholds reduce tail risk up to 13-fold compared to accuracy maximization.
Optimal insurance policy for exponential utility maximization with convex premium calculation.
problem Maximizing terminal wealth utility with exponential utility function and convex premium formula.
method Necessary condition for optimal indemnity, numerical algorithm to compute it, convergence proof.
result Numerical algorithm converges to unique optimal indemnity.
Deep neural nets solve complex insurance math equations.
problem Optimal control problems in insurance math.
method Deep neural network algorithm for elliptic PDEs.
result Solves high-dimensional semilinear elliptic PDEs.
New methods improve insurance data quality for catastrophic events.
problem Improving precision and size of insurance data for catastrophic events.
method Bootstrap, bootknife, and GAN algorithms.
result Compared MSE and MAE of simulated outputs, direct algorithm for fuzzy expert opinion.
Study deep neural nets for solving complex insurance equations.
problem Solving linear and semilinear parabolic PIDEs in high dimensions.
method Deep neural network algorithms for integro-differential equations.
result Viability of deep learning for solving high-dimensional PIDEs.
Develops a Bonus-Malus model for cyber risk insurance to incentivize cybersecurity.
problem Lack of effective insurance strategies to incentivize cybersecurity.
method Proposes a Bonus-Malus model and a mathematical model with a numerical algorithm.
result Demonstrates how a Bonus-Malus system resolves moral hazard and benefits the insurer.
Quantum computing promises faster insurance contract valuation.
problem Computational intensity of insurance contract valuation.
method Investigation of quantum computing's applicability for insurance contracts using Amplitude Estimation.
result Quantum computing can significantly speed up insurance contract valuation.
Detects organized fraudsters in insurance claims with high precision.
problem Fraudulent insurance claims lead to heavy financial losses.
method Developed a novel data-driven procedure using graph learning algorithms.
result Achieves more than 80% precision in fraud detection.
This paper explores how machine learning can improve life insurance risk assessment.
problem Limited use of machine learning in life insurance due to statistical models' efficiency.
method Review and extension of traditional actuarial methodologies with machine learning techniques.
result Developed Python library for life insurance data, improving risk modeling.
This contribution is concerned with price optimisation of the new business for a non-life product. Due to high competition in the insurance market, non-life insurers are interested in increasing their conversion rates on new business based on some profit level. In this respect, we consider the competition in the market…
Study tackles imbalanced data in car insurance claims prediction.
problem Predicting rare events (claims) in car insurance with imbalanced data.
method Various machine learning techniques (logistic-regression, decision tree, random forest, xgBoost, feed-forward network) applied to imbalanced dataset.
result Comparison of machine learning algorithms' performance in claim occurrence prediction.
Study models weather index insurance pricing by insurers and farmers, finding flexible pricing kernels boost profits.
problem Monopoly pricing of weather index insurance with risk and flexibility considerations.
method Bowley-type sequential game with insurer and farmer, using neural networks for farmer's payoff.
result Flexible pricing kernels increase insurer profits closer to indemnity insurance levels.
The study tackles indirect discrimination in insurance pricing models.
problem Indirect discrimination in insurance pricing models.
method Presented a statistical model free of proxy discrimination.
result The canonical price in the model does not satisfy group fairness axioms.
We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive …
Study compares non-parametric models for predicting medical insurance reimbursement delays.
problem Estimating the time-lapse between medical insurance reimbursement.
method Comparative study of four non-parametric regression models (KNNs, SVMs, Decision Trees, Random Forests) using R-squared metric.
result Each model's performance varies with training data size, feature space, and hyperparameters.
InfDetect detects e-commerce insurance fraud using graph analysis.
problem Detecting fraudulent claims in e-commerce insurance with multiple parties involved.
method Developed a large-scale fraud detection system InfDetect using graph-based approaches.
result InfDetect successfully detected thousands of fraudulent claims and saved money daily.
New formulas estimate life insurance benefits with less computation.
problem Estimating future discretionary benefits in life insurance.
method Derive analytic formulas for lower and upper bounds of FDB.
result Simple estimator for FDB with average of lower and upper bounds.
EBM improves car insurance claim severity and frequency prediction while maintaining interpretability.
problem Balancing predictive accuracy and interpretability in insurance claim modeling.
method Combines GAM and cyclic gradient boosting, providing interpretable predictions.
result EBM outperforms benchmark models in claim severity and frequency prediction.
Study mutual insurance market dynamics using mean field games.
problem Understanding strategic interactions and wealth distribution in mutual insurance companies.
method Extended mean field game framework, mean field forward-backward stochastic differential equations (MF-FBSDE), deep BSDE algorithm.
result Established global-in-time existence and uniqueness of Nash equilibrium strategy.
Bayesian CART models improve insurance claims frequency prediction and interpretation.
problem Improving accuracy and interpretability in insurance pricing models.
method Introducing Bayesian CART models for claims frequency, implementing MCMC algorithm for posterior tree exploration, and using DIC for model selection.
result Bayesian CART models can better classify policy-holders into risk groups.
This paper explores NLP techniques for insurance, detailing methods and applications.
problem Extracting value from insurance reports using complex text data.
method Detailed explanation of NLP methods and their implementation in insurance.
result Enhanced risk monitoring and policyholder benefits through NLP.
New algorithm improves insurance company's asset allocation decisions.
problem Strategic asset allocation with multiple objectives (risk, return, solvency, distance to current portfolio).
method Exact multi-objective optimization algorithm incorporating four objectives.
result Significant improvement in portfolio quality and decision-making process.
This paper emphasizes model transparency and interpretation in insurance.
problem Ensuring models do not discriminate and are explainable.
method Exploring tools to control actuarial models using machine learning.
result Interpretability methods can adapt explanations to different audiences.
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.
Paper uses RL to optimize insurance pricing on PCWs, improving efficiency and adaptability.
problem Optimizing pricing on price comparison websites while balancing competitiveness and profitability.
method Integrates model-based and model-free reinforcement learning methods for dynamic pricing.
result Hybrid RL agent outperforms existing methods in sample efficiency and cumulative reward.
An importance sampling approach for sampling copula models is introduced. We propose two algorithms that improve Monte Carlo estimators when the functional of interest depends mainly on the behaviour of the underlying random vector when at least one of the components is large. Such problems often arise from dependence …
New algorithm tackles optimization problems with discontinuous gradients in finance and insurance.
problem Optimization problems with discontinuous stochastic gradients in finance and insurance.
method Langevin dynamics based algorithm e-THεO POULA. result Non-asymptotic error bounds and expected excess risk estimates for e-THεO POULA. The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many…
This paper develops a valuation model for private companies.
problem Lack of pricing and hedging models for private companies.
method Dynamic Gordon growth model, Maximum Likelihood (ML) estimators, Expectation Maximization (EM) algorithm.
result Closed-form pricing and hedging formulas for private companies.
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.
Method proposed for pricing insurance products covering both foreseeable and unforeseeable risks.
problem Pricing insurance products that include unforeseeable risks.
method Mixed Poisson process with Bayesian setup and linear exponential family distributions.
result Bayesian premiums are more reactive to claim trends than traditional ones.
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.
New method for insurance valuation combining hedging and risk minimization.
problem Current insurance valuation methods do not reflect regulatory risk measures.
method Two-step hedging procedure using generalised regression.
result The method produces portfolios neutral to risk measures like VaR or expectiles.
Model detects insurance fraud using social network analysis.
problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.
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.
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.
The paper examines insurance market dynamics and optimal regulation.
problem Equilibrium outcomes in dynamic insurance markets.
method Analyzes three equilibrium outcomes: positive, zero, and market failure.
result Insurers may accept underwriting losses by investing profits, especially with negative correlations.
Enhanced loss function boosts fraud detection in auto insurance claims.
problem Class imbalance in auto insurance fraud detection.
method Structured three-stage training framework integrating convex surrogate, non-convex intermediate, and standard focal loss.
result Improves minority-class F1-scores and AUC compared to baseline methods.
We consider an investor who wants to select her/his optimal consumption, investment and insurance policies. Motivated by new insurance products, we allow not only the financial marke but also the insurable loss to depend on the regime of the economy. The objective of the investor is to maximize her/his expected total d…
Optimal insurance contract limits insurer's risk exposure variance.
problem Designing an optimal insurance contract limiting insurer's risk exposure variance.
method Derive optimal policy semi-analytically, focusing on actuarially fair case.
result Expected coverage is larger for wealthier insured, indicating normal good.
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.
mSHAP explains predictions of two-part models, improving fairness and interpretability.
problem Interpreting predictions from two-part models, especially in insurance.
method mSHAP: a method for computing SHAP values of two-part models using the SHAP values of individual models.
result mSHAP is exponentially faster than kernelSHAP for computing approximate SHAP values.
Two pension funds mutually insure against longevity risk.
problem Mutual insurance against systematic longevity risk for pension funds.
method Mathematical demonstration and market clearing condition.
result Insurance provides little benefit when fund preferences are similar, but can be beneficial when preferences vary significantly.
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.
Insurance firms use RL to optimize customer offers for desired target portfolios.
problem Optimizing insurance offers to achieve a desired customer portfolio.
method Developed a novel reinforcement learning algorithm.
result The RL algorithm outperforms traditional methods in a synthetic market.
Synthetic telematics dataset created from insurance claims data.
problem Creating a reliable synthetic dataset for usage-based insurance models.
method Three-stage process using machine learning: binary classification, regression, and synthetic oversampling.
result Synthetic dataset remarkably similar to real data in risk assessment models.
The study examines how formal index insurance compares to informal risk sharing in managing natural disasters.
problem The challenges of natural disasters and the effectiveness of index insurance in risk management.
method A three-strategy evolutionary game model to analyze the competitive relationship between formal index insurance, informal risk sharing, and non-insurance.
result Basis risk and loss ratio significantly impact the adoption rate of index insurance, with different strategies preferred under varying conditions.