Bank Account Abnormal Transaction Recognition Based on Relief Algorithm and BalanceCascade

Abstract

With the rapid development of the banking industry, the number of transactions is exponential growth.At the same time, abnormal transactions are also increasing, causing immeasurable losses and risks.In terms of how to accurately identify suspicious transactions from massive customer information and bank account transaction data, this paper adopts the BalanceCascade algorithm based on Relief to solve the problem of unbalanced data in the identification of abnormal transactions in bank accounts, and proposes an effective abnormal transaction identification model.At the same time, the AUC and K-S index as the unbalanced data classification standards of performance evaluation, and finally to Kaggle data platform of bank accounts abnormal transaction data set, the results show that the proposed identification model of performance evaluation index of AUC 0.90 KS value at the same time also is as high as 0.64, shows that the model in as much as possible to reduce the rate of false positives and has high ability of classification and recognition, the method of bank accounts abnormal transaction identification has a certain reference value, enhance rapid response and improve the level of customer service for Banks have certain effect.