As the financial services industry advances, managing the inherent complexities of annuities requires sophisticated risk management in software testing. Traditional methodologies are insufficient to address the multi-dimensional challenges posed by evolving regulatory landscapes, intricate financial models, and system integration. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) to enhance risk mitigation across critical testing domains, including compliance automation, financial accuracy, data security, and performance optimization. AI/ML technologies introduce advanced automation, predictive analytics, and anomaly detection, elevating (...) the precision and efficiency of the testing lifecycle. Through continuous learning models and adaptive testing frameworks, AI/ML streamlines legacy system integrations and dynamically scales performance testing. This article establishes the strategic imperative for insurers to integrate AI/ML into software testing frameworks, ensuring a proactive, data-driven approach to risk management and future-proofing their technological ecosystems. (shrink)

In actuarial parlance, the price of an insurance policy is considered fair if customers bearing the same risk are charged the same price. The estimate of this fair amount hinges on the expected value obtained by weighting the different claims by their probability. We argue that, historically, this concept of actuarial fairness originates in an Aristotelian principle of justice in exchange (equality in risk). We will examine how this principle was formalized in the 16th century and shaped in life insurance (...) during the following two hundred years, in two different interpretations. The Domatian account of actuarial fairness relied on subjective uncertainty: An agreement on risk was fair if both parties were equally ignorant about the chances of an uncertain event. The objectivist version grounded any agreement on an objective risk estimate drawn from a mortality table. We will show how the objectivist approach collapsed in the market for life annuities during the 18th century, leaving open the question of why we still speak of actuarial fairness as if it were an objective expected value. (shrink)

How were reliable predictions made before Pascal and Fermat's discovery of the mathematics of probability in 1654? What methods in law, science, commerce, philosophy, and logic helped us to get at the truth in cases where certainty was not attainable? The book examines how judges, witch inquisitors, and juries evaluated evidence; how scientists weighed reasons for and against scientific theories; and how merchants counted shipwrecks to determine insurance rates. Also included are the problem of induction before Hume, design arguments for (...) the existence of God, and theories on how to evaluate scientific and historical hypotheses. It is explained how Pascal and Fermat's work on chance arose out of legal thought on aleatory contracts. The book interprets pre-Pascalian unquantified probability in a generally objective Bayesian or logical probabilist sense. (shrink)