Credit scoring helps lenders and banks assess an individual's likelihood to repay credit by evaluating the risk of default. Due to the data involved in determining creditworthiness, these models necessitate an adequate level of data protection.
It's also crucial for the data in these models to maintain its utility and integrity.
When data is not accurate enough, credit scoring models won’t perform adequately to determine risk.
By adopting Anonos’ synthetic data, CRIF, a global company specializing in credit & business information systems, successfully developed credit scoring models that retained the utility of real data, while ensuring privacy.