Overview

Dr Tarun Israni is a Lecturer in Business Information Systems at Technological University Dublin, with an interdisciplinary research background spanning actuarial science, financial mathematics, statistics, business analytics, information systems, and technology management. His research interests focus on the application of quantitative and data-driven methods to contemporary business, insurance, and organisational challenges.

His research combines statistical modelling, machine learning, optimisation, decision analytics, and data visualisation to address practical problems in areas including non-life insurance pricing, fairness and explainability in machine learning, digital transformation, artificial intelligence adoption, employee wellbeing, and business decision-making. His work particularly explores how advanced analytical methods can support more accurate, transparent, fair, and effective decision-making within organisations.

Dr Israni has research and professional experience across academia, actuarial consulting, and insurance. Prior to joining Technological University Dublin, he worked in actuarial and insurance roles, including personal lines pricing, where he gained practical experience in predictive modelling, insurance pricing, and regulatory considerations. He has also worked as a Senior Actuarial Consultant, supporting the application of quantitative and analytical techniques to business problems.

His current research includes the evaluation of machine learning models for non-life insurance pricing, with particular emphasis on the joint assessment of predictive performance, calibration, and fairness. He is also interested in the organisational implications of artificial intelligence and emerging technologies, including their adoption, impact on employees, organisational change, and business performance.

Dr Israni holds a PhD in Financial Mathematics and Actuarial Science/Statistics, alongside qualifications in Computer Science Engineering and Business Management with a focus on Finance and Economics. His interdisciplinary background enables him to bridge theoretical quantitative research with practical applications in business, technology, insurance, and data-driven decision-making.

Overall, his research aims to develop and apply rigorous analytical approaches that generate meaningful insights for academics, industry practitioners, organisations, and policymakers.