Your reserves may be best estimate, but are they valid?

Author/Authors

William Diffey, Laura Hobern, Al Lauder, Malcolm Cleugh, Mark Wu, Satraajeet Mukherjee, Apollos Dan, Param Dharamshi, Rav Atwal, Arun Vijay, Fergal Dolan, Amo Grewal, Ed Harrison

Abstract

This paper outlines frameworks to use for reserving validation and gives the reader an overview of current techniques being employed. In the experience of the authors, many companies lack an embedded reserve validation framework and validation can appear piecemeal and unstructured. The paper outlines a case study demonstrating how successful machine learning techniques will become and then goes on to discuss the implications of machine learning for the future of reserving departments, processes, data and validation techniques. Reserving validation can take many forms, from simple checks to full independent reviews to add value to the reserving process, enhance governance and increase confidence in and reliability in results. This paper discusses common weaknesses and their solutions and provides suggestions for a framework in which to apply validation tools. The impacts of the Covid-19 pandemic on reserving validation are also covered as are early warning indicators and the topic of IFRS 17 from the standpoint of reserving validation. The paper looks at the future for reserving validation and discusses the data challenges that need overcoming on the path to embedded reserving process validation.

Keywords

Reserving validation; Machine learning; Covid-19; IFRS 17; Data management; Risk management; Reserving process; Early warning indicators; Governance; Actuarial best estimate (ABE)