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ASTIN Webinar: Multi-state Modeling of Customer Churn

Customer churn, which insurance companies use to describe the non-renewal of existing customers, is a widespread and expensive problem in general insurance, particularly because contracts are usually short-term and are renewed periodically.

Traditionally, customer churn analyses have employed models which utilise only a binary outcome (churn or not churn) in one period. However, real business relationships are multi-period, and policyholders may reside and transition between a wider range of states beyond that of the simply churn/not churn throughout this relationship.
To better encapsulate the richness of policyholder behaviours through time, we propose multi-state customer churn analysis, which aims to model behaviour over a larger number of states (defined by different combinations of insurance coverage taken) and across multiple periods (thereby making use of readily available longitudinal data). Using multinomial logistic regression (MLR) with a second-order Markov assumption, we demonstrate how multi-state customer churn analysis offers deeper insights into how a policyholder’s transition history is associated with their decision making, whether that be to retain the current set of policies, churn, or add/drop a coverage.

Applying this model to commercial insurance data from the Wisconsin Local Government Property Insurance Fund, we illustrate how transition probabilities between states are affected by differing sets of explanatory variables and that a multi-state analysis can potentially offer stronger predictive performance and more accurate calculations of customer lifetime value (say), compared to the traditional customer churn analysis techniques.

30 November, 2022 /16:00 PM (EST)
01 December 2022 / 08:00 AM (AEDT)


Mercredi 30 novembre 2022
22h00 (GMT +1)
L'événement est organisé en ligne
Fei Huang
Senior Lecturer

Fei Huang is a Senior Lecturer in Risk and Actuaries Studies and Deputy Director (Data and AI Tech) of the UNSW Business AI Lab. Her research focuses on applications of statistical machine learning and ethical AI in both life and general insurance. She has published papers in top-tier actuarial journals and received the inaugural Carol Dolan Actuaries Summit Prize in 2022. Fei teaches statistical machine learning and data science subjects for risk and actuarial applications. She has received multiple education awards including the UNSW John Prescott Award for Outstanding Teaching Innovation and ANU Vice Chancellor’s Award for Teaching Excellence. Fei is a Senior Fellow of Advance HE (SFHEA).

Yumo Dong

Yumo Dong is a Ph.D. student in the Research School of Finance, Actuarial Studies and Statistics (RSFAS) of the Australian National University. Yumo's research interests focus on multi-state modeling, dependence modeling, and applications of statistical machine learning in insurance.

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Mercredi 30 novembre 2022
22h00 (GMT +1)
L'événement est organisé en ligne
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