The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
Models use embeddings and attention for better claim severity prediction.
problem Improving predictive modeling of claim severity with categorical data.
method Developed neural networks and attention-based architectures with embeddings.
result Attention-based models enhance predictive performance with contextual augmentation.
A new method for predicting insurance claims with statistical guarantees.
problem Creating accurate prediction intervals for insurance claims.
method Model-agnostic framework using split conformal prediction for frequency-severity modeling.
result Shows effectiveness on simulated and real datasets using various models.
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.
We derive asymptotic expansions for the prices of a variety of European and barrier-style claims in a general local-stochastic volatility setting. Our method combines Taylor series expansions of the diffusion coefficients with an expansion in the correlation parameter between the underlying asset and volatility process…
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.
It is illustrated a methodology to compute the pure premium for the automobile insurance (claim frequency and severity) using generalized linear models. It is obtained the pure premium for the partial damage loss cover (PPD) using a set of automobile insurance policies with an exposition of a year. It is found that the…
Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.
problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.
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.
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.
Traditional non-life reserving models largely neglect the vast amount of information collected over the lifetime of a claim. This information includes covariates describing the policy, claim cause as well as the detailed history collected during a claim's development over time. We present the hierarchical reserving mod…
Using a suitable change of probability measure, we obtain a novel Poisson series representation for the arbitrage- free price process of vulnerable contingent claims in a regime-switching market driven by an underlying continuous- time Markov process. As a result of this representation, along with a short-time asymptot…
This paper deals with the super-replication of non path-dependent European claims under additional convex constraints on the number of shares held in the portfolio. The corresponding super-replication price of a given claim has been widely studied in the literature and its terminal value, which dominates the claim of i…
Reflected geometric Brownian motion models are not arbitrage-free.
problem No-arbitrage condition violation in financial markets.
method Analysis of reflected geometric Brownian motion models.
result Models violate even the weakest no-arbitrage condition.
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.
Study uses healthcare claims data to identify Covid-19 risk factors without prior selection.
problem Identify risk factors for severe Covid-19 cases.
method Fine-grained hierarchical information from medical classification systems used to analyze over 33,000 covariates.
result Method has better predictive ability than pre-specified morbidity groups.
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.
Enhanced Tweedie model for insurance claims using CatBoost.
problem Accurately modeling aggregate claims with zero-inflated data.
method Refined Tweedie model with boosting methods in CatBoost.
result Marked improvement in model performance for insurance analytics.
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.
The paper extends portfolio theory to include contingent claim functions for option pricing.
problem Developing a method to price options using portfolio generating functions.
method Extending portfolio theory to include contingent claim functions and applying partial differential equations.
result A method to price options using portfolio generating functions and replicable contingent claim functions.
Generalization error defines the discriminability and the representation power of a deep model. In this work, we claim that feature space design using deep compositional function plays a significant role in generalization along with explicit and implicit regularizations. Our claims are being established with several im…
This paper addresses the identification of insurance models with multidimensional screening where insurees have private information about their risk and risk aversion. The model includes a random damage and the possibility of several claims. Screening of insurees relies on their certainty equivalence. The paper then in…
New methods for quantifying insurance claim cost uncertainty using LightGBM and GLMs.
problem Quantifying prediction uncertainty in insurance claim costs.
method Proposed non-conformity measures for GLMs and GBMs with Tweedie loss.
result Locally weighted Pearson residuals outperform other methods in maintaining nominal coverage with smallest average width.
Study identifies risk factors for subsequent suicide attempts in youth.
problem Uncertainty in suicide attempt identification from medical claims data.
method Integrative Cox cure model with regularization for survival analysis with uncertain events.
result Identifies risk factors for subsequent suicide attempts and distinguishes susceptibility from timing.
Study assesses health plan risk measures for Solvency Capital Requirement.
problem Assessing risk measures for health plans to meet Solvency Capital Requirement.
method Three-part regression model with three GLMs for claim counts, episode allocation, and severity.
result Reduction in regression models compared to traditional methods.
Study shows subordinated Cramér-Lundberg model increases ruin probability.
problem Analyzing the impact of subordinated time-changed claims on insurance ruin probability.
method Examined a compound Poisson process modified by a Lévy subordinator.
result Probability of ruin decreases slowly with initial capital, despite unchanged total claim amount.
The paper proposes an original methodology for constructing quantitative statistical models based on multidimensional distribution functions constructed on the basis of the insurance companies' data on inshurance policies (including policies with deductible) and claims incurred. Real data of some Russian insurance comp…
We propose a flexible framework for hedging a contingent claim by holding static positions in vanilla European calls, puts, bonds, and forwards. A model-free expression is derived for the optimal static hedging strategy that minimizes the expected squared hedging error subject to a cost constraint. The optimal hedge in…
Optimal dividends for insurers with climate tipping point consideration.
problem Natural catastrophe claims and climate tipping point impact insurance profits.
method Two-dimensional stochastic control problems with Erlang distribution for tipping point timing, numerical approximation.
result Non-stationary climate tipping point can benefit shareholders by optimizing dividends.
We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distribution class used for the observation model.A first theoretical and experimental contribution of the paper is to establish that even in the large sample limit with arbitrarily powerful neural architectures and latent spac…
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.
The study analyzes how bonus-malus systems and delayed claims settlement affect insurance companies' financial stability.
problem Analyzing the impact of bonus-malus systems and delayed claims settlement on insurance companies' financial stability.
method Examined a discrete-time risk model with time-varying premiums, evaluating two types of claims and settlement delays.
result Delayed settlement of by-claims leads to lower ruin probabilities under specific assumptions.
Deep Claim predicts payer responses from claims data using deep learning.
problem Predicting payer responses from claims data to improve healthcare performance.
method Learning complex dependencies in claim inputs to create a compact representation, then using deep learning to predict responses.
result Deep Claim improves claim denial prediction by 22.21%.
Study claims trading to rescue banks in distress.
problem Rescue a bank in distress using claims trading.
method Formalize claims trading in financial networks, study decision and optimization problems, provide efficient algorithms for creditor-positive trades.
result No trade can strictly improve both banks' assets; provide efficient algorithms for creditor-positive trades.
New method for individual claims reserving using machine learning.
problem Traditional claims reserving methods are limited in individual claim prediction.
method Restructured data utilization for CL prediction, using multi-period factors.
result Neural networks applied for individual claims reserving.
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 …
Optimizes insurance processing capacity to minimize costs.
problem Processing delays and backlogs in insurance claims.
method Optimal capacity selection to minimize delay-adjusted and fixed costs.
result Minimizes claims costs by balancing processing capacity and delays.
This paper considers the problems of modeling and predicting a long-term and ``blurry'' relapse that occurs after a medical act, such as a surgery. The relapse is observed only indirectly, in a ``blurry'' fashion, through longitudinal prescriptions of drugs over a long period of time after the medical act. We introduce…
Study predicts doubling of U.S. maize insurance claims due to climate change.
problem Climate change increases U.S. maize loss probability, impacting insurance claims.
method Neural Network Monte Carlo simulations to predict crop loss metrics.
result Doubling of annual probability of maize Yield Protection insurance claims by mid-century.
This study compares the largest claims from two insurance portfolios using stochastic orderings.
problem Comparing the largest claims from two heterogeneous insurance portfolios.
method Used various stochastic orderings and established sufficient conditions associated with model parameters.
result Established sufficient conditions for comparing the largest claims from two insurance portfolios.
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.
We consider trading in a financial market with proportional transaction costs. In the frictionless case, claims are maximal if and only if they are priced by a consistent price process--the equivalent of an equivalent martingale measure. This result fails in the presence of transaction costs. A properly maximal claim i…
Investor maximizes utility from an unknown claim using robust optimization.
problem Maximizing utility from an unknown contingent claim.
method Robust optimization with quantile formulation and variational inequalities.
result Optimal trading strategy and utility indifference price determined.
Model predicts individual insurance claim reserves using activation patterns.
problem Accurately predicting individual claim reserves in insurance contracts.
method Multinomial logistic regression to model claim activation and development.
result The model generates accurate predictions of total and per coverage reserves.
BERT learns claim descriptions to identify patent novelty.
problem Identifying novel patent claims among existing documents.
method Training BERT on concatenated claims and descriptions, scoring BERT's output.
result BERT identifies relevant X documents for patent novelty.
Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are we…
Paper introduces EEMs for pricing contingent claim returns.
problem Computing expected future prices of contingent claims.
method Dynamic change of measure approach to construct EEMs.
result EEMs provide physical and pricing expectations of contingent claim prices.
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.