  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.
