Enhances non-life insurance pricing models using transformer models.
problem Improving predictive power of non-life insurance pricing models.
method Enhances actuarial non-life models with transformer models for tabular data.
result Transformer models outperform benchmark models in claim frequency prediction.
New model bridges pricing and reserving for insurance claims.
problem Incomplete claim data due to reporting and settlement delays.
method Develops an occurrence and development model to estimate both claims and premiums.
result Effective resolution of pricing and reserving inconsistencies.
Framework monitors insurance pricing models for drift and recalibration.
problem Maintaining predictive performance of pricing models in evolving insurance portfolios.
method Formalizes deviance loss and Murphy's score, studies Gini score, develops monitoring framework.
result Framework guides decisions on refitting or recalibrating pricing models.
Graphical models improve actuarial judgment in insurance claims analysis.
problem Improving actuarial judgment in insurance claims analysis.
method Using graphical models to represent complex inter-dependencies and incorporate qualitative knowledge.
result Graphical models can be used to express and analyze non-life insurance claims data.
TabPFN doesn't outperform GLM and XGBoost for motor insurance pricing.
problem Improving insurance pricing models using Tabular Foundation Models (TFMs).
method Pre-training on synthetic datasets and in-context learning for inference.
result TabPFN does not consistently outperform established baselines, has longer inference times, and is sensitive to training set size.
We use the maximum entropy principle for pricing the non-life insurance and recover the Bühlmann results for the economic premium principle. The concept of economic equilibrium is revised in this respect.
The article derives a formula for predicting claims uncertainty using the GCC method.
problem Claims reserving uncertainty in non-life insurance modeling.
method Generalized Cape Cod (GCC) method, derived an analytical formula for MSEP.
result Derives an analytical formula for the mean squared error of prediction (MSEP) of the GCC method.
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.
In this paper we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA). We aim at finding marginal distributions of, and dependence between, losses on the five largest lines of business (LoBs) in order to create models for Solvency Cap…
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…
Paper introduces new actuarial-consistent valuations for insurance liabilities.
problem Valuation of insurance liabilities considering both financial and actuarial risks.
method Proposes two-step actuarial valuations and actuarial-consistent procedures.
result Actuarial-consistent valuations are equivalent to two-step actuarial valuations under coherence.
In this paper we propose a general framework for modeling an insurance liability cash flow in continuous time, by generalizing the reduced-form framework for credit risk and life insurance. In particular, we assume a nontrivial dependence structure between the reference filtration and the insurance internal filtration.…
The balance property is crucial for insurance pricing, ensuring total actuarial price equals loss. Maximum likelihood GLMs fulfill it, but Lindholm-Wüthrich suggests three methods, with constrained GLM being superior.
problem Ensuring the balance property in insurance pricing models
method Using constrained GLM fitting
result Constrained GLM fitting is superior to the two previously discussed balance correction methods
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 optimizes CANN for actuarial tasks using RSM.
problem Optimizing hyperparameters for neural networks in actuarial science.
method Factorial design and response surface methodology (RSM).
result Reduced hyperparameter optimization from 288 to 188, achieving near-optimal performance.
The concordance probability or C-index is a popular measure to capture the discriminatory ability of a regression model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical pricing of a non-life insurance product. Due …
The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.
problem Modeling and pricing cyber insurance policies, especially for systemic risks.
method Distinguishes three types of cyber risks and proposes methods for their valuation.
result Complex methods are needed for systemic cyber risks, including risk-neutral valuation and monetary risk measures.
Machine learning models outperform traditional actuarial methods in predicting health insurance costs.
problem Improving accuracy in health insurance pricing to identify concession opportunities.
method Developed and evaluated two machine learning models at the patient and employer-group levels.
result Machine learning models outperformed traditional actuarial models by 20% in predicting costs.
SynthETIC simulates insurance claims with customizable features.
problem Lack of synthetic data for insurance claim analysis.
method Develops a new simulator for individual insurance claims with customizable features.
result SynthETIC fills a gap in actuarial toolkit for synthetic data.
A new pricing model reduces bias in insurance premiums.
problem Insurance pricing fairness and discrimination.
method Adversarial learning and autoencoders for debiasing multiple pricing factors.
result A single pricing model mitigates bias across geographic and car types.
Recent theoretical results establish that time-consistent valuations (i.e. pricing operators) can be created by backward iteration of one-period valuations. In this paper we investigate the continuous-time limits of well-known actuarial premium principles when such backward iteration procedures are applied. We show tha…
This paper uses information theory to improve risk modeling in big data.
problem Insufficient application of information theory in actuarial science.
method Explores information theory to uncover performance limits of insurance big data systems.
result Guidance for risk modeling and actuarial pricing systems.
Calculation of an optimal tariff is a principal challenge for pricing actuaries. In this contribution we are concerned with the renewal insurance business discussing various mathematical aspects of calculation of an optimal renewal tariff. Our motivation comes from two important actuarial tasks, namely a) construction …
Unified framework for fair pricing in long-term insurance products.
problem Unclear generalization of fair pricing methods to long-term products.
method Reformulate multi-state transition models as Poisson regression problems.
result Direct application of existing fair pricing methods to long-term insurance products.
We consider evaluation methods for payoffs with an inherent financial risk as encountered for instance for portfolios held by pension funds and insurance companies. Pricing such payoffs in a way consistent to market prices typically involves combining actuarial techniques with methods from mathematical finance. We prop…
Analyzes premium data of Indian non-life insurers, finding GEV distribution best fits Lognormal and GEV extremes.
problem Modeling premiums of non-life insurance companies in India.
method Empirical analysis using Lognormal, GEV, and GPD distributions.
result Generalized Extreme Value distribution best fits premium data for ten Indian non-life insurers.
Refundable income annuities offer a money-back guarantee, now the majority of sales.
problem The complexity and market neglect of refundable income annuities.
method Explained the pricing, duration, and money's-worth-ratio of refundable IAs, proving a counterintuitive price behavior.
result The market price of cash-refund IAs is not a declining function of age, and older buyers might pay more than younger ones.
We consider the insurance company as a physical system which is immersed in its environment (the financial market). The insurer company interacts with the market by exchanging the money through the payments for loss claims and receiving the premium. Here in the equilibrium state we obtain the premium by using the canon…
Interpretable deep learning model for insurance pricing.
problem Developing transparent yet accurate insurance pricing models.
method Developed a novel deep learning model with interpretable components.
result The model outperforms traditional methods in prediction accuracy.
In this paper, we propose a stochastic investment model for actuarial use in South Africa by modelling price inflation rates, share dividends, long term and short-term interest rates for the period 1960-2018 and inflation-linked bonds for the period 2000-2018. Possible bi-directional relations between the economic seri…
Framework insures AI actions with reserve capital, preventing loss.
problem Ensuring safety and accountability for AI actions with varying side effects.
method Developed Actuarial Action Interface (AAI) and Authority Frontier to price and gate AI actions.
result Found common refusal and release patterns across domains, with varying required reserve capital.
Insurance contracts for autonomous AI agents must be actuarially sound and resistant to gaming.
problem Designing insurance contracts for autonomous AI agents that are actuarially sound and resistant to gaming.
method Characterizing a five-attack space and proving the actuarial runtime is gaming-resistant.
result An incentive-compatible layer for actuarial control of autonomous-agent side effects.
New method for valuing and hedging credit risk when defaults cannot be hedged.
problem Valuation and hedging of counterparty credit risk when there's no protection available.
method Local risk-minimization approach via BSDE (Backward Stochastic Differential Equation)
result Optimal strategy computed for valuing and hedging credit risk.
Study optimal stopping for variable annuity contracts with discontinuous rewards.
problem Optimal timing to surrender a variable annuity contract with guaranteed minimum benefit.
method Analytical study of an optimal stopping problem with a discontinuous reward function, considering general fee and surrender charge functions.
result Characterization of the surrender region and its interrelation with fee and surrender charge functions.
Paper develops methods for fair insurance pricing without direct access to sensitive attributes.
problem Fairness in insurance pricing with restricted access to sensitive attributes.
method Develops statistical methods for estimating discrimination-free premiums using privatized sensitive attributes.
result The proposed methods enable fair insurance pricing while respecting privacy and regulatory constraints.
Paper aims to make insurance models more understandable.
problem Lack of transparency in machine learning models for insurance pricing.
method Proposes a framework for explaining machine learning models in insurance ratemaking.
result Illustrates the proposed framework with a case study.
This paper introduces a Decision Tree Learner as an early warning system for classification of the non-life insurance companies according to their financial solid as strong, moderate, weak, or insolvency. In this study, we ran several experiments to show that the proposed model can achieve a good result using standard …
This paper proposes a use of an ordinal classifier to evaluate the financial solidity of non-life insurance companies as strong, moderate, weak, and insolvency. This study constructed an efficient classification model that can be used by regulators to evaluate the financial solidity and to determine the priority of fur…
This paper presents the hierarchical generalized linear model (HGLM) for loss reserving in a non-life insurance company. Because in this case the error of prediction is expressed by a complex analytical formula, the error bootstrap estimator is proposed instead. Moreover, the bootstrap procedure is used to obtain full …
The study uses ML and AI to forecast pension fund mortality, outperforming traditional methods.
problem Incorporating longevity risk into pension fund financial assessments.
method Employed actuarial learning with ML/AI techniques (regression trees, random forest, boosting, XGBoost, CatBoost, neural networks) on actuarial data.
result ML/AI algorithms outperform the Lee-Carter model in mortality forecasting for pension funds.
The paper provides approximations for pricing Asian options using a mixed fractional Brownian motion with jumps.
problem Pricing Asian options under a mixed fractional Brownian motion with jumps.
method Approximate closed-form solutions for arithmetic Asian options and power options.
result Analytical formulas for pricing arithmetic Asian options and power options are derived.
Proposes a generalized XGBoost method for nonconvex loss functions.
problem Limited to convex loss functions in XGBoost.
method Extends XGBoost to use nonconvex loss functions and multivariate loss functions.
result Generalized XGBoost method can model multiple parameters in various distributions.
Optimizes insurance pricing by accounting for policyholders' price sensitivity.
problem Traditional insurance pricing does not consider policyholders' price sensitivity.
method Formulates insurance pricing as a decision-making problem and uses off-policy evaluation and stochastic control.
result Neural networks outperform existing techniques for policy optimization.
LLMs help automate extraction of actuarial variables from unstructured claims data.
problem Manual processing of unstructured claims data is time-consuming and inconsistent.
method Two-stage processing architecture using LLMs, modular Python pipeline.
result LLM-based extraction achieved high accuracy and practical actuarial value.
This paper discusses different classes of loss models in non-life insurance settings. It then overviews the class Tukey transform loss models that have not yet been widely considered in non-life insurance modelling, but offer opportunities to produce flexible skewness and kurtosis features often required in loss modell…
Study finds actuarial unfairness in China's pension system, proposing income-dependent annuitization rules.
problem Actuarial fairness in China's NDC pension system when mortality differs across income groups.
method Developed a mortality-differentiated Lee-Carter framework with group-specific baseline mortality schedules and a common period effect, estimated using national and subgroup data.
result Substantial actuarial unfairness in the current age-only divisor, with a reverse transfer from poorer to richer retirees.
Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.
problem Balancing public benefits and private insurance for loss of earning capacity in Denmark.
method Innovative approaches from researchers and practitioners, leveraging actuarial expertise.
result Development of equitable, data-driven solutions to mitigate risk and enhance societal well-being.
Study optimal reinsurance and investment strategies under common shocks affecting financial and actuarial markets.
problem Maximizing expected exponential utility of terminal wealth in a company facing both ordinary and catastrophic claims.
method Modeling common shocks affecting financial and actuarial markets, using stochastic control and Hamilton-Jacobi-Bellman equations.
result Characterization of optimal reinsurance and investment strategies under common shock dependence.