This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a...

Two-part models are important to and used throughout insurance and actuarial science. Since insurance is required for registering a car, obtaining a mortgage, and participating in certain businesses, it is especially important that the models that price insurance policies are fair and...

Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the...

The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21-...

We compare parametric and machine learning techniques (namely: Neural Networks) for in-sample modeling of the yield curve of the BRICS countries (Brazil, Russia, India, China, South Africa). To such aim, we applied the Dynamic De Rezende-Ferreira five-factor model with time-varying decay...

The risk-based capital (RBC) ratio, an insurance company's financial soundness system, evaluates the capital adequacy needed to withstand unexpected losses. Therefore, continuous institutional improvement has been made to monitor the financial solvency of companies and protect consumers' rights,...

The need to assess the risks of the trustworthiness of counterparties is increasing every year. The identification of increasing cases of unfair behavior among counterparties only confirms the relevance of this topic. The existing work in the field of information and economic security does not...

We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data are mapped to quantum...

The growing trend in the number and severity of auto insurance claims creates a needfor new methods to efficiently handle these claims. Machine learning (ML) is one of the methodsthat solves this problem. As car insurers aim to improve their customer service, these companieshave started adopting...