Marcel Ambrus

Marcel Ambrus currently serves as a Manager at KPMG, where he leverages his extensive expertise in insurance actuarial science to deliver innovative solutions for clients in the life insurance sector. His role involves leading complex projects that focus on the development and validation of biometric assumptions, including mortality, disability, and lapse rates, utilizing advanced statistical techniques such as Generalized Linear Models (GLM) and Generalized Additive Models (GAM). Marcel’s proficiency in R enables him to perform credibility-based analyses of biometric assumptions, ensuring robust and data-driven insights that support strategic decision-making.

In addition to his core responsibilities, Marcel specializes in applying stochastic mortality models and Bayesian methods, including Markov Chain Monte Carlo (MCMC) techniques, to update and refine biometric assumptions, thereby enhancing the accuracy of actuarial valuations and risk assessments. His expertise extends to writing and reviewing complex SQL queries to extract and manipulate biometric policy data, facilitating comprehensive analyses for actuarial models and MCEV calculations.

Marcel’s industry experience is complemented by his skills in risk management, solvency, and the use of Prophet for actuarial modeling. His background in physics and statistics provides a strong analytical foundation, enabling him to approach challenges with a rigorous and quantitative mindset. As a member of the insurance actuarial community, Marcel continuously seeks to incorporate cutting-edge methodologies into his work, ensuring KPMG’s clients benefit from innovative, compliant, and forward-looking actuarial solutions. His leadership in projects related to stochastic simulation and risk modeling underscores his commitment to advancing actuarial practices within the evolving landscape of insurance regulation and market dynamics.