  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.
