DATA ANALYZE AND PREDICTION USING DATA SCIENCE TECHNIQUE IN BANK LOAN APPROVAL

Anomaly detection relies on individuals’ behavior profiling and works by detecting any deviation from the norm. When used for online banking fraud detection, however, it mainly suffers from three disadvantages. The historical behavior data are often too limited to profile his/her behavior pattern. Due to the heterogeneous nature of transaction data, there lacks a uniform treatment of different kinds of attribute values, which becomes a potential barrier for model development and further usage. The transaction data are highly skewed, and it becomes a challenge to utilize the label information effectively. The three disadvantages result in both poor generalization and high false positive rate of anomaly detection, and we propose a ranking metric embedding based multi-contextual behavior profiling (ReMEMBeR) model to battle them effectively. With the idea of collaborative filtering, for an individual, information from other similar individuals can be used to establish his/her behavior profile. The proposed model can, thus, integrate the multicontextual behavior patterns and allow transactions to be examined under the different contexts. Extensive experiments on a real-world online banking transaction dataset demonstrate that our model not only outperforms benchmarks on all metrics but also can be combined with them to achieve even better performance.