Selective Multi-source Transfer Learning with Wasserstein Domain Distance for Financial Fraud Detection

Abstract

As financial enterprises have moved their services to the internet, financial fraud detection has become an ever-growing problem causing severe economic losses for the financial industry. Recently, machine learning has gained significant attention to handle the financial fraud detection problem as a binary classification problem. While significant progress has been made, fraud detection is still a notable challenge due to two major reasons. First, fraudsters today are adaptive, inventive, and intelligent, making their fraud characteristics are too deep stealth to be detected by simple detection models. Second, labeled samples for training the detection models are usually very few as collecting large-scale training data needs a certain performance-time and is costly. To address the two problems, we propose a novel multi-source transfer learning approach with self-supervised domain distance learning for financial fraud detection problems. The core idea is to transfer relevant knowledge from multiple data-rich sources to the data-poor target task, e.g., learning fraud patterns from several other related mature loan products to improve the fraud detection in a cold-start loan product. Specifically, since the feature distribution discrepancy across domains may cause useless or even negative knowledge transfer, we propose self-supervised domain distance learning under the Wasserstein metric to measure the domain relevance/relationships between target and source tasks. The learned Wasserstein distance helps in selectively transferring most relevant knowledge from source domains to target domains. Thus it reduces the risk of negative transfer as well as maximizes the multi-source positive transfer. We conduct extensive experiments under multi-source few-shot learning settings on real financial fraud detection dataset. Experimental analysis shows that the inter-domain relationships learned by our domain distance learning model align well with the facts and the results demonstrate that our multi-source transfer learning approach achieves significant improvements over the state-of-the-art transfer learning approaches.