Abstract
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 reserve 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 to 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 covers common weaknesses and their solutions and suggestions of a framework in which to apply validation tools. The impacts of the COVID-19 pandemic on reserving validation is 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.
Highlights
1.1.1 This paper is a key read for those who work in reserving and who work around reserving teams
In the experience of the authors, many companies lack an embedded reserve validation framework and reserve validation can appear piecemeal and unstructured. 1.1.2 the paper is aimed at practitioners in the field of general insurance, the validation approaches outlined in section 3 may well be relevant to actuaries working in other fields and in particular those working in health insurance who may well apply general insurance reserving techniques as part of their work
The application of machine learning fitting techniques such as those described in section 6 of this paper may well aid validation in other areas where sufficient data are available, such as to the fitting of mortality curves to crude mortality data or the extrapolation of past mortality improvements for modelling future improvements
Summary
1.1.3 The paper outlines a case study demonstrating how successful machine learning techniques will become and goes on to discuss the implications of machine learning to the future of reserving departments, processes, data and validation techniques. 1.1.7 The final section of the paper is focused on a case study on machine learning together with discussion of the implications of this technology for reserving departments, processes and validation techniques, we discuss data implications for these processes. The results of the reserve review form a key component of the management and statutory accounts. They form the starting point for capital calculations and a crucial feedback loop into the pricing and planning process. The area of reserve risk and validation within the process is of great importance to both regulators and to Boards – a focus which has been increased by the COVID-19 pandemic and consequent recession
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