Anomaly Detection with Attribute Conflict Identification in Bank Customer Data

In commercial banks, data centers often integrates different data sources, which represent complex and independent business systems. Due to the inherent data variability and measurement or execution errors, there may exist some abnormal customer records (data). Existing automatic abnormal customer detection methods are outlier detection which focuses on the differences between customers, and it ignores the other possible abnormal customers caused by the inner features confliction of each customer. In this paper, we designed a method to identify abnormal customer information whose inner attributes are conflicting (confliction detection). We integrate the outlier detection and the confliction identification techniques together, as the final abnormality detection. This can provide a complete and accurate support of customer data for commercial bank's decision making. Finally, we have performed experiments on a dataset from a Chinese commercial bank to demonstrate the effectiveness of our method.