Incremental Neural-Network Learning for Big Fraud Data

Fraud detection systems aim to process a massive amount of data at high speed. To address the issues of data scalability, we introduce a chunk-based incremental classification approach based on a neural network (MLP) and a memory model to tackle the stability-plasticity dilemma. The incremental approach adapts the fraud model sequentially with incoming data chunks and retains past chunks a little more. We employ a large-scale credit-card fraud dataset that we organize into initial and incremental chunks for training and testing. Using data sampling, we solve the data skew problem, a critical issue in fraud detection. After each incremental phase, we evaluate the performance of the adjusted MLP classifier using the testing chunk. The experimental results demonstrate the effectiveness and efficiency of our incremental method and its superiority to the non-incremental MLP.