How does big data risk control improve the anti-fraud ability of financial institutions?

Big data risk control can improve the anti-fraud ability of financial institutions by upgrading and enriching the traditional risk control system. The risk control of traditional finance mainly relies on information with strong credit attributes to score credit, identify customers' repayment ability and willingness, and then decide whether to lend. The big data risk control of internet finance enriches the data dimension of traditional risk control, judges the credit situation of borrowers through data association analysis, and predicts the relationship between certain behavior characteristics and credit risk with the help of models. Just like the intelligent scoring product of Huace Mathematics, it is a technical means to measure the risk probability in the form of scores, and different scoring card types can be used according to different scenarios.

In order to improve the anti-fraud ability of financial institutions, the intelligent scoring products of Huace Mathematics Department adopt risk scoring in the pre-loan risk identification stage, which can predict the probability of future default/overdue/loss. Usually, the higher the score, the safer it is. Intelligent scoring product anti-fraud scorecard objectively presents personal credit status through scoring form, and provides risk control support for customers to realize rapid decision-making.

From the intelligent scoring products of Huace Mathematics, it can be seen that the big data risk control of Internet finance supplements the shortcomings of the traditional risk control data dimension to a certain extent, and can identify fraudulent customers more comprehensively. At the same time, it can be applied to the risk control model to evaluate the risk level of customers, improve the decision-making ability of enterprises and enhance the anti-fraud ability of financial institutions.