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.
Mack's estimator improves chain ladder prediction for large exposure insurance models.
A method for analysing the risk of taking a too low reserve level by use of Chain Ladder method is developed. We give an answer to the question of how much safety loading in terms of the Chain Ladder standard error has to be added to the Chain Ladder reserve in order to reach a specified security level in loss reservin…
Chain-ladder reserving is sensitive to outliers, leading to unreliable estimates.
New method simplifies individual claims reserving.
Paper proposes robust methods to detect and treat outliers in multivariate loss reserving.
New method for individual claims reserving using machine learning.
The intention of this paper is to estimate a Bayesian distribution-free chain ladder (DFCL) model using approximate Bayesian computation (ABC) methodology. We demonstrate how to estimate quantities of interest in claims reserving and compare the estimates to those obtained from classical and credibility approaches. In …
The article derives a formula for predicting claims uncertainty using the GCC method.
Paper proposes a new reserving model using machine learning techniques.
LLMs help automate extraction of actuarial variables from unstructured claims data.
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…
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…