Credit-fraud detection model train method, credit-fraud detection method and device

Abstract

The invention provides a lending fraud detection model training method, a lending fraud detection method and a lending fraud detection device. The lending fraud detection model training method comprises the following steps: obtaining identity information of a plurality of sample users, user bank pipeline information and fraud label information corresponding to each user; Constructing identity feature vector and user behavior pattern vector according to identity information; According to the user behavior pattern vector, the second vector transformation matrix and the user bank pipeline information, the pipeline characteristic vector is constructed. Generating a target feature vector according to a user behavior pattern vector and a pipeline feature vector; The target eigenvector is inputted into the target neural network, and the fraud detection result of the target eigenvector is obtained. Then the target neural network, the first vector transformation matrix and the second vector transformation matrix are trained to obtain the debit and credit fraud detection model. The application can improve the identification efficiency and the identification accuracy of the fraudulent user bythe credit platform, and greatly save the human cost.

Description

technical field [0001] The present application relates to the technical field of information processing, in particular to a loan fraud detection model training method and device. Background technique [0002] With the rapid development of Internet finance, the incidence of Internet fraud and credit risk has increased. According to statistics, in recent years, the non-performing asset rate of the consumer finance industry in China has been on the rise. China's Internet fraud risk has ranked among the top three in the world. Cybercrime brings economic losses of up to 445 billion US dollars to the world every year. Penetration of different industries. [0003] Internet financial risks include credit risk and operational risk. Credit risk means that customers have no intention of repaying the loan when they initiate a loan request. Among them, fraud is the highest credit risk, and more than 50% of the bad debt losses of consumer finance come from fraud. [0004] In order to a...

AI Technical Summary

Problems solved by technology

[0006] When the credit platform conducts credit review on loan users, it basically relies on the expert experience of business personnel to make judgments. Since the bank’s historical flow information is often complicated and the amount of information involved is relatively large, it is not only inefficient to manually review , and it is difficult to draw an overall correct conclusion by combining bank flow data and user personal information

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