Deep model tackles claim size modeling with quantile-based regression.
problem Actuarial claim size modeling difficulty with no simple distribution.
method Deep composite regression model with quantile splicing point.
result Deep neural network regression models show superiority over classical approaches.
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.
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.
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.
The paper introduces a new class of multivariate mixtures for actuarial applications.
problem Developing a new class of multivariate mixtures for actuarial calculations.
method Proposed a class of multivariate matrix-exponential affine mixtures with matrix-exponential marginals.
result Explicit calculations of actuarial quantities are possible due to the proposed class's properties.
This guide clarifies techniques for assessing and comparing model calibration and performance.
problem Assessing and comparing the calibration and performance of predictive models in insurance and actuarial practice.
method Clarifies statistical techniques for assessing model calibration and comparing models, emphasizing the importance of specifying the prediction target functional and choosing the appropriate scoring function.
result Provides guidance for the practical choice of scoring functions and illustrates results with real data case studies.
Unified comparison of gradient boosting algorithms for insurance claims.
problem Improving predictive accuracy and computational efficiency in insurance claim prediction.
method Unified notation and comprehensive numerical study comparing 12 gradient boosting algorithms on 5 datasets.
result No trade-off between model adequacy and predictive accuracy.
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.
CANN models improve insurance claim count predictions using telematics data.
problem Improving insurance claim count predictions with telematics data.
method Combining classical actuarial models with neural networks for telematics data.
result CANN models outperform traditional models in predicting insurance claims.
New method simplifies individual claims reserving.
problem Insufficient flexibility and robustness in existing methods.
method Building on classical chain-ladder method, introduces new perspective.
result Advances toward a new standard for micro-level reserving.
Study classifies liability insurance policies using machine learning.
problem Classifying liability insurance policies with or without claims.
method Used machine learning models like nearest neighbour and logistic regression on Actuarial Challenge dataset.
result Models accurately classified policies into claims and non-claims groups.
Reinforcement learning improves insurance claims reserving by learning from all claim trajectories.
problem Traditional reserving models learn only from settled claims, missing valuable data from ongoing claims.
method Formulated as a Markov decision process, uses reinforcement learning to update OCL estimates sequentially.
result Soft Actor-Critic implementation achieves competitive claim-level accuracy and strong aggregate performance.
Study improves motor insurance claim prediction using geographic data.
problem Limited location identifiers in public actuarial datasets.
method Zone-level modeling framework with environmental and orthoimagery data.
result Geographic information improves MTPL claim prediction accuracy.
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.
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.
Let Xλ1,…,Xλn be dependent non-negative random variables and Yi=IpiXλi, i=1,…,n, where Ip1,…,Ipn are independent Bernoulli random variables independent of Xλi's, with E[Ipi]=pi, i=1,…,n. In actuarial sciences, Yi corresponds to the claim amo…
This paper aims to review the methodology behind the generalized linear models which are used in analyzing the actuarial situations instead of the ordinary multiple linear regression. We introduce how to assess the adequacy of the model which includes comparing nested models using the deviance and the scaled deviance. …
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.
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.
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.
This paper suggests claim history will be deprecated in future auto insurance rates.
problem The role of historical claim records in auto insurance rates.
method Proposes a new risk variable elimination method and real-time road risk model design.
result Claim history will be considered a 'noise' factor and deprecated in Pay-How-You-Drive models.
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.
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.
Paper compares MICE-based methods to deep generative models for synthetic data in ratemaking.
problem Limited access to high-quality data for actuarial ratemaking.
method Benchmarked MICE-based models against VAEs and CTA-GANs.
result MICE-based models preserve marginal distributions and multivariate relationships better than deep models.
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.
Let Xλ1,…,Xλn be a set of dependent and non-negative random variables share a survival copula and let Yi=IpiXλi, i=1,…,n, where Ip1,…,Ipn be independent Bernoulli random variables independent of Xλi's, with E[Ipi]=pi, i=1,…,n. In actuarial scie…
The tail of the distribution of a sum of a random number of independent and identically distributed nonnegative random variables depends on the tails of the number of terms and of the terms themselves. This situation is of interest in the collective risk model, where the total claim size in a portfolio is the sum of a …
This article focuses on the mathematical problem of existence and uniqueness of BSDE with a random terminal time which is a general random variable but not a stopping time, as it has been usually the case in the previous literature of BSDE with random terminal time. The main motivation of this work is a financial or ac…
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.
A new method for modeling insurance claim frequencies using random proportions.
problem Inaccurate fitting of classical distributions to insurance claim frequency data.
method Modeling claim frequencies using random proportions of insurance contracts and applying goodness-of-fit tests.
result A new statistical approach for better modeling insurance claim frequencies.
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.
Approximations to utility indifference prices are provided for a contingent claim in the large position size limit. Results are valid for general utility functions on the real line and semi-martingale models. It is shown that as the position size approaches infinity, the utility function's decay rate for large negative…
The net-premium principle is considered to be the most genuine and fair premium principle in actuarial applications. However, an insurance company, applying the net-premium principle, goes bankrupt with probability one in the long run, even if the company covers its entire costs by collecting the respective fees from i…
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.
Study optimal investment and reinsurance for insurance companies in a dynamic market model.
problem Optimal investment and reinsurance strategies for insurance companies in a regime-switching market model.
method Forward dynamic exponential utility, value function construction, proportional reinsurance optimization.
result Characterization of optimal investment strategy and proportional reinsurance level.
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.
Within the Solvency II framework the insurance industry requires a realistic modelling of the risk processes relevant for its business. Every insurance company should be capable of running a holistic risk management process to meet this challenge. For property and casualty (P&C) insurance companies the risk adequate mo…
Neural networks improve loss reserving with case estimates and transaction data.
problem Improving loss reserving accuracy using neural networks.
method Comparison of feed-forward and recurrent neural networks trained on case estimates and transaction data.
result Case estimates significantly improve predictions, but memory-equipped neural networks offer minimal additional benefit.
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.
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.
We investigate, focusing on the ruin probability, an adaptation of the Cramer-Lundberg model for the surplus process of an insurance company, in which, conditionally on their intensities, the two mixed Poisson processes governing the arrival times of the premiums and of the claims respectively, are independent. Such a …
Novel GLMMNet model tackles high-cardinality categorical features in actuarial applications.
problem Inadequate encoding methods for high-cardinality categorical features in actuarial data.
method Generalised Linear Mixed Model Neural Network (GLMMNet) integrating a generalised linear mixed model in a deep learning framework.
result GLMMNet often outperforms or performs comparably with entity embedded neural networks, providing transparency.
Paper proposes optimal investment and reinsurance strategies considering financial and insurance risks dependence.
problem Optimal investment and reinsurance strategies under dependent financial and insurance risks.
method Stochastic control approach to maximize expected exponential utility of terminal wealth.
result Minimal dependence between financial and insurance risks significantly impacts investment and reinsurance strategies.
We consider a classical risk process with arrival of claims following a non-stationary Hawkes process. We study the asymptotic regime when the premium rate and the baseline intensity of the claims arrival process are large, and claim size is small. The main goal of the article is to establish a diffusion approximation …
DRN improves actuarial distributional forecasting with interpretable neural networks.
problem Challenges in modeling loss distributional properties with classic methods.
method Combines GLMs with a modified DDR method to flexibly refine baseline distribution.
result DRN improves predictive performance while maintaining interpretability.
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
In this paper we provide a valuation formula for different classes of actuarial and financial contracts which depend on a general loss process, by using the Malliavin calculus. In analogy with the celebrated Black-Scholes formula, we aim at expressing the expected cash flow in terms of a building block. The former is r…
Financial undertakings often have to deal with liabilities of the form 'non-hedgeable claim size times value of a tradeable asset', e.g. foreign property insurance claims times fx rates. Which strategy to invest in the tradeable asset is risk minimal? We generalize the Gram-Charlier series for the sum of two dependent …