Neural Networks for Actuarial Reserving

Neural Network Basics

A feedforward neural network consists of an input layer, one or more hidden layers, and an output layer. Each neuron computes a weighted sum of inputs plus a bias, then applies a nonlinear activation function (ReLU, sigmoid, or tanh). The network learns by minimizing a loss function using backpropagation and gradient descent. Key hyperparameters include the number of hidden layers and neurons, learning rate, batch size, and regularization strength. Dropout randomly sets neurons to zero during training to prevent overfitting. Early stopping halts training when validation loss stops improving.

Reserving Applications

Neural networks can replace or augment traditional reserving methods by learning development patterns from loss triangle data. The network takes accident year, development period, and potentially additional features as inputs and predicts incremental or cumulative losses. Advantages include the ability to capture nonlinear development patterns and incorporate external data. Challenges include the need for sufficient training data, interpretability concerns, and regulatory acceptance. The DeepTriangle and similar architectures have shown promise. Exam MAS-II covers neural network fundamentals, their advantages and limitations compared to chain ladder and BF methods, and practical considerations for actuarial use.