arXiv research

The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.

Study optimal hedging for claims with random weights in discrete time.

Reinforcement learning improves insurance claims reserving by learning from all claim trajectories.

State variables are easily the most subtle dimension of sequential decision problems. This is especially true in the context of active learning problems (bandit problems") where decisions affect what we observe and learn. We describe our canonical framework that models {\it any} sequential decision problem, and present…

Improved batched SH algorithm maintains original performance.

First, we consider the problem of hedging in complete binomial models. Using the discrete-time Föllmer-Schweizer decomposition, we demonstrate the equivalence of the backward induction and sequential regression approaches. Second, in incomplete trinomial models, we examine the extension of the sequential regression app…

System detects financial misinformation and generates clear explanations.

A new model calculates optimal clearing payments in dynamic financial networks.

Bayesian framework reduces online optimization regret.

There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimization. We focus on two of the most important fields: stochastic optimal control, with its roots in deterministic optimal control, and reinfo…

New method constructs confidence sets for GLMs via game theory.

Enhanced loss function boosts fraud detection in auto insurance claims.

Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize t…

The study analyzes how bonus-malus systems and delayed claims settlement affect insurance companies' financial stability.

Deep Claim predicts payer responses from claims data using deep learning.

New method for individual claims reserving using machine learning.

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 …

Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.

Optimizes insurance processing capacity to minimize costs.

New model bridges pricing and reserving for insurance claims.

Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourci…

This study compares the largest claims from two insurance portfolios using stochastic orderings.

Model detects insurance fraud using social network analysis.

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.

Model predicts individual insurance claim reserves using activation patterns.

BERT learns claim descriptions to identify 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.

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…

A new method for modeling insurance claim frequencies using random proportions.

In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation. Patent claim language itself has rarely been explored in the past and poses a unique ch…

FiNCAT tool automatically identifies financial numerals in documents.

LLMs help automate extraction of actuarial variables from unstructured claims data.

New method simplifies individual claims reserving.

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…

Study tackles imbalanced data in car insurance claims prediction.

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…

Time-aware fact-checking improves veracity predictions for time-sensitive claims.

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 …

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 …

Unified methods for hedging impermanent loss in decentralized exchanges.

Wojciech Kamiński disproved a spiral claim for conformal geodesics.

Audit shows risk claims from distributional reinforcement learning agents are often false.

In this paper, we solve the arms exponential exploding issue in multivariate Multi-Armed Bandit (Multivariate-MAB) problem when the arm dimension hierarchy is considered. We propose a framework called path planning (TS-PP) which utilizes decision graph/trees to model arm reward success rate with m-way dimension interac…

Study uses SVM to predict weather-induced home insurance claims and losses.

Paper proposes deep learning for fake claim detection on social media.

The paper deals with bonus-malus systems with different claim types and varying deductibles. The premium relativities are softened for the policyholders who are in the malus zone and these policyholders are subject to per claim deductibles depending on their levels in the bonus-malus scale and the types of the reported…