Big data credit is the use of data analysis and modeling for risk assessment, based on the assessment score, to predict the repayment ability, willingness to repay, and fraud risk of the repayer. In the field of financial risk control, big data refers to the full amount of data and user behavior data. Currently used is the data surrounding the customer highly relevant to the customer's credit situation, the use of data to implement scientific risk control.

Secondly, big data credit can incorporate more diverse behavioral data.

In the era of big data, every relevant organization is trying to obtain the data information of the actors to the greatest extent possible, so that the data can cover a wide range to the greatest extent possible, and real-time vivid.

Finally, big data credit has brought about more time-sensitive judging criteria.

Negative credit will affect a person's credit, to know their own debt records and personal credit, in WeChat, on the public ho "check levy division" will have detailed instructions.

Another disadvantage of traditional risk control is the lack of validity data input, its risk control model reflects the results of often lagging data. Utilizing the assessment results of lagging data to manage credit risk inherently creates greater structural risk.

Big Data's data collection and computational capabilities can help organizations build a real-time view of risk management. With the help of comprehensive multi-latitude data, self-learning capabilities of the wind control model, and real-time calculation results, enterprises can improve their quantitative risk assessment capabilities.

However, although big data credit can reduce information asymmetry, provide a more comprehensive understanding of credit recipients, and increase anti-fraud capabilities, as well as more accurate risk pricing, it is not yet a complete replacement for traditional credit. Big data risk control can improve the level of traditional risk control from the data dimension and analysis point of view, is a necessary complement to make the traditional risk control more scientific and rigorous, but at present, due to the coverage rate, matching rate and other issues, can not completely replace the traditional risk control.