Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network
This addresses the need for more accurate loss reserving in general insurance by leveraging granular data, though it is an incremental improvement over existing micro-level approaches.
The authors tackled the problem of ignoring individual claim details in aggregated insurance loss reserving by introducing a micro-level framework using an LSTM neural network to predict payments and recoveries, achieving improved predictive estimates compared to the chain-ladder method and adjusting for extreme payments with a generalized Pareto model.
Detailed information about individual claims are completely ignored when insurance claims data are aggregated and structured in development triangles for loss reserving. In the hope of extracting predictive power from the individual claims characteristics, researchers have recently proposed to move away from these macro-level methods in favor of micro-level loss reserving approaches. We introduce a discrete-time individual reserving framework incorporating granular information in a deep learning approach named Long Short-Term Memory (LSTM) neural network. At each time period, the network has two tasks: first, classifying whether there is a payment or a recovery, and second, predicting the corresponding non-zero amount, if any. We illustrate the estimation procedure on a simulated and a real general insurance dataset. We compare our approach with the chain-ladder aggregate method using the predictive outstanding loss estimates and their actual values. Based on a generalized Pareto model for excess payments over a threshold, we adjust the LSTM reserve prediction to account for extreme payments.