Modelling CLV in the Insurance Industry Using Deep Learning Methods

Sammanfattning: This paper presents a master’s thesis project in which deep learning methods are used to both calculate and subsequently attempt to maximize Customer Lifetime Value (CLV) for an insurance provider’s customers. Specifically, the report investigates whether panel data comprised of customers monthly insurance policy subscription history can be used with Recurrent Neural Networks (RNN) to achieve better predictive performance than the naïve forecasting model. In order to do this, the use of Long Short Term Memory (LSTM) for anomaly detection in a supervised manner is explored to determine which customers are more likely to change their subscription policies. Whether Deep Reinforcement Learning (DRL) can be used in this setting in order to maximize CLV is also investigated. The study found that the best RNN models outperformed the naïve model in terms of precision on the data set containing customers which are more likely to change their subscription policies. The models suffer, however, from several notable limitations so further research is advised. Selecting those customers was shown to be successful in terms of precision but not sensitivity which suggest that there is a room for improvement. The DRL models did not show a substantial improvement in terms of CLV maximization.