Credit Scoring

Automatically predict the credit score of your customers by using to generate and deploy highly accurate credit scoring models at scale.


Traditional Credit Scoring

Today, banking institutions are confronted with numerous payment defaults by their customers. They implement credit risk assessment models to help them make decisions when lending money or providing credit. Models are based on the credit lender’s information (age, number of previous loans, etc.), and then correlated with the pre-defined risk behavior to distinguish the 'good' from the 'bad' candidate. However, such traditional method is based on broad segments and does not always consider clients situations or external factors, resulting in models’ lack of accuracy.


Improving Risks Models with Machine Learning

Machine learning helps analysts improve their credit scoring models by using more data to provide individualized credit scores. It can take into account new factors such as employment opportunity or recent credit history to more effectively predict the score of each customer. This more granular approach to risk assessment allows banks and personal loan companies to upgrade their credit scoring techniques and provide more personalized services to their customers.

Using for Credit Scoring provides teams with an automated platform to quickly build and deploy machine learning models according to your enterprise data and target. Start using our automated platform to boost your credit scoring models. will help your consumer banking organization make smarter decisions and empower your analysts' teams with predictive intelligence.

This tool is very effective in identifying risk behavior models but in our case it has also allowed us to improve the user experience by adapting loan amounts according to the score.

Banking Establishment - Ile de France Region

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