Identification and Prediction Methods of Financial Anti-fraud

Abstract

Nowadays, evaluating and identifying the potential fraud risk of borrowers effectively and calculating the fraud probability of them are the basis and significant steps of credit risk management in modern financial institutions before issuing loans. This paper mainly studies the statical analysis of the historical loan data of financial institutions based on the idea of unbalanced data classification and establishes the prediction model of loan fraud through random forest, decision tree and regression algorithm. The prediction performance of random forest algorithm is better than the other two mentioned methods. Additionally, it may obtain the feature that have a remarkable impact on the final fraud by ranking the importance of those features, which leads to a more effective judgment on the credit risk in the financial field.