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 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 purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...

Banks play a vital role in strengthening the financial system of a country; hence, their survival is decisive for the stability of national economies. Therefore, analyzing the survival probability of the banks is an essential and continuing research activity. However, the current literature...

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by...

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic...

We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs. We validate the models on loss reserving data across lines of business, and show that they...

The purpose of this study is to find the most important variables that represent the future projections of the Bank of International Settlements' (BIS) capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive and important measure of capital adequacy. This...

While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine...

Pension plans play a vital role in the economy by impacting savings, consumption, and investment allocation. Despite declining mortality rates and increasing life expectancy, pension enrollment remains low, affecting the long-term financial stability and well-being of populations. To address...