Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

by   Wei Min, et al.

In e-commerce industry, user behavior sequence data has been widely used in many business units such as search and merchandising to improve their products. However, it is rarely used in financial services not only due to its 3V characteristics - i.e. Volume, Velocity and Variety - but also due to its unstructured nature. In this paper, we propose a Financial Service scenario Deep learning based Behavior data representation method for Clustering (FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the behavior sequence data, we treat click stream data as event sequence, use time attention based Bi-LSTM to learn the sequence embedding in an unsupervised fashion, and combine them with intuitive features generated by risk experts to form a hybrid feature representation. We also propose a GPU powered HDBSCAN (pHDBSCAN) algorithm, which is an engineering optimization for the original HDBSCAN algorithm based on FAISS project, so that clustering can be carried out on hundreds of millions of transactions within a few minutes. The computation efficiency of the algorithm has increased 500 times compared with the original implementation, which makes flash fraud pattern detection feasible. Our experimental results show that the proposed FinDeepBehaviorCluster framework is able to catch missed fraudulent transactions with considerable business values. In addition, rule extraction method is applied to extract patterns from risky clusters using intuitive features, so that narrative descriptions can be attached to the risky clusters for case investigation, and unknown risk patterns can be mined for real-time fraud detection. In summary, FinDeepBehaviorCluster as a complementary risk management strategy to the existing real-time fraud detection engine, can further increase our fraud detection and proactive risk defense capabilities.


TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial

With the explosive growth of e-commerce and the booming of e-payment, de...

Explainable Clustering and Application to Wealth Management Compliance

Many applications from the financial industry successfully leverage clus...

ARMS: Automated rules management system for fraud detection

Fraud detection is essential in financial services, with the potential o...

Financial Forecasting and Analysis for Low-Wage Workers

Despite the plethora of financial services and products on the market no...

Behavioral graph fraud detection in E-commerce

In e-commerce industry, graph neural network methods are the new trends ...

Deep Set Classifier for Financial Forensics: An application to detect money laundering

Financial forensics has an important role in the field of finance to det...

Fraud Detection using Data-Driven approach

The extensive use of the internet is continuously drifting businesses to...

Please sign up or login with your details

Forgot password? Click here to reset