A Memory-Enhanced Framework for Financial Fraud Detection
Kunlin Yang · 2018
The rapid development of electronic financial services brings significant convenience to our daily life. However, it also offers criminals the opportunity to exploit financial systems to do fraudulent transactions. Previous studies on fraud detection only deal with single type transactions and cannot adapt well to evolving environment in reality. Here we propose a novel fraud detection algorithm called FraudMemory. It adopts state-of-art feature representation methods to better depict users and logs with multiple types in financial systems. Our model innovatively uses sequential model to capture the sequential patterns of each transaction and leverages memory networks to improve both the performance. Also, with the incorporation of memory components, FraudMemory possesses high adaptability to the existence of concept drift. The empirical study proves that our model is a potential tool for financial fraud detection.