The credit score prediction model reduces exposure to credit risk and loss by identifying applicants who are at high risk of default and have no credit value but do not have an extensive credit history in case they can cause events (e.g., bankruptcy, default, etc)
Using the collected customer data (the customer's income status, credit history, payment levels, and other metrics) to make a credit score prediction, it is possible to accurately assess risk while simplifying credit approval. It has higher accuracy and recall rates than traditional credit scoring models and will play an important role in financial risk management systems.
|Data||Data type||Content||Use mode|
|Input data||CSV||Customer data : personal information, income status, credit history, payment level, etc.||API|
|Output data||CSV||Customer credit score prediction||API|
||Attached file upon application
||Online||Customer data required for model creation
|API Setting||$1,800 ~|