Big data credit reporting is to use data analysis and models to conduct risk assessment, and predict the repayment ability, repayment willingness and fraud risk of the repayment person according to the evaluation score. In the field of financial risk control, big data refers to total data and user behavior data. At present, we use the data around customers that are highly related to the credit situation of customers, and use the data to implement scientific risk control.

1. The big data credit model can make the credit evaluation more accurate: the big data credit model brings massive data into the credit system and conducts multi-angle analysis with various credit models.

Take ZestFinance, an American Internet finance company, as an example. The model basically processed 3,500 data items, extracted nearly 70,000 variables, and used more than ten models, such as identity verification model, fraud model and repayment ability model, which made the evaluation results more comprehensive and accurate and greatly improved the evaluation performance of the model.

2. Big data credit can contain more diverse behavioral data: In the era of big data, every relevant institution tries to obtain the data information of behavioral subjects to the greatest extent, so that the data can be widely covered and broadcast in real time to the greatest extent.

3. Big data credit has brought more timely evaluation criteria: another shortcoming of traditional risk control is the lack of effective data input, and its risk control model often reflects the results of lagging data. Using the evaluation results of lagging data to manage credit risk will produce greater structural risk.

The data collection and computing power of big data can help enterprises establish real-time risk management views. With comprehensive multi-latitude data, self-learning risk control model and real-time calculation results, enterprises can improve their quantitative risk assessment ability.