An autoencoder is a neural network trained to compress data into a compact summary and then reconstruct the original from it. By forcing information through this narrow bottleneck, the network learns the most essential features of the data. Autoencoders are used to reduce image sizes, remove noise, and detect anomalies: if the network reconstructs a transaction poorly, that transaction probably differs from anything seen in training, which makes autoencoders useful for fraud monitoring in banks and insurers.