A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
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…
Paper studies second order tail probabilities in risk models.
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 …
We consider a large, homogeneous portfolio of life or disability annuity policies. The policies are assumed to be independent conditional on an external stochastic process representing the economic-demographic environment. Using a conditional law of large numbers, we establish the connection between claims reserving an…
It is well known that the minimal superhedging price of a contingent claim is too high for practical use. In a continuous-time model uncertainty framework, we consider a relaxed hedging criterion based on acceptable shortfall risks. Combining existing aggregation and convex dual representation theorems, we derive duali…
Reinforcement learning improves insurance claims reserving by learning from all claim trajectories.
New financial dataset and model detect claims affecting market returns.
New method reconstructs data subsets from limited published statistics.
We consider a financial contract that delivers a single cash flow given by the terminal value of a cumulative gains process. The problem of modelling and pricing such an asset and associated derivatives is important, for example, in the determination of optimal insurance claims reserve policies, and in the pricing of r…
Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract info…
The paper analyzes systemic risk in an insurance model with multiple business lines and heterogeneous claims.
In this paper, we obtain the finite-horizon and infinite-horizon ruin probability asymptotics for risk processes with claims of subexponential tails for non-stationary arrival processes that satisfy a large deviation principle. As a result, the arrival process can be dependent, non-stationary and non-renewal. We give t…
Latent truth discovery, LTD for short, refers to the problem of aggregating ltiple claims from various sources in order to estimate the plausibility of atements about entities. In the absence of a ground truth, this problem is highly challenging, when some sources provide conflicting claims and others no claims at all.…
The paper examines the feasibility of managing aggregate cyber-risk in IoT environments.
Enhanced Tweedie model for insurance claims using CatBoost.
We develop a class of non-life reserving models using a stable-1/2 random bridge to simulate the accumulation of paid claims, allowing for an essentially arbitrary choice of a priori distribution for the ultimate loss. Taking an information-based approach to the reserving problem, we derive the process of the condition…
Synthetic telematics dataset created from insurance claims data.
Estimates fat-shattering dimension of aggregated function classes.
Study optimal reinsurance and investment strategies under common shocks affecting financial and actuarial markets.
Optimizes insurance pricing to minimize ruin probability under various claim dependencies.
FL improves insurance claims loss prediction without sharing data.
Develops a method to model multivariate count processes with Cox processes and shot noise intensities.
In this paper we introduce a new coherent cumulative risk measure on , the space of càdlàg processes having Laplace transform. This new coherent risk measure turns out to be tractable enough within a class of models where the aggregate claims is driven by a spectrally positive Lévy process. Moreover, w…
Pari-mutuel markets are trading platforms through which the common market maker simultaneously clears multiple contingent claims markets. This market has several distinctive properties that began attracting the attention of the financial industry in the 2000s. For example, the platform aggregates liquidity from the ind…
Triangulation filters spurious circuits in multilingual models.
The paper develops new methods to approximate ruin probabilities in a perturbed risk model.
Corrects an earlier theorem, establishing new facts about information structures and non-anticipative aggregation.
New model improves inference on asset market durations.
Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.
New model bridges pricing and reserving for insurance claims.
Model detects insurance fraud using social network analysis.
The study analyzes how bonus-malus systems and delayed claims settlement affect insurance companies' financial stability.
Paper explores statistical and computational limits of estimating low-rank Gaussian mixtures.
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a…
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…
Model predicts individual insurance claim reserves using activation patterns.
Recently, several new pari-mutuel mechanisms have been introduced to organize markets for contingent claims. Hanson introduced a market maker derived from the logarithmic scoring rule, and later Chen and Pennock developed a cost function formulation for the market maker. On the other hand, the SCPM model of Peters et a…
A new method for modeling insurance claim frequencies using random proportions.
The introduction of CCPs in most derivative transactions will dramatically change the landscape of derivatives pricing, hedging and risk management, and, according to the TABB group, will lead to an overall liquidity impact about 2 USD trillions. In this article we develop for the first time a comprehensive approach fo…
Deep Claim predicts payer responses from claims data using deep learning.
ValueBlindBench tests LLM-generated investment rationales for validity before returns are known.
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 …
FiNCAT tool automatically identifies financial numerals in documents.
This study compares the largest claims from two insurance portfolios using stochastic orderings.
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…
We start by showing that the finite-time absolute ruin probability in the classical risk model with constant interest force can be expressed in terms of the transition probability of a positive Ornstein-Uhlenbeck type process, say X. Our methodology applies to the case when the dynamics of the aggregate claims process …
A non-parametric method for evaluation of the aggregate loss distribution (ALD) by combining and numerically inverting the empirical characteristic functions (CFs) is presented and illustrated. This approach to evaluate ALD is based on purely non-parametric considerations, i.e., based on the empirical CFs of frequency …