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 Prevision.io for Credit Scoring
Prevision.io 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. Prevision.io will help your consumer banking organization make smarter decisions and empower your analysts' teams with predictive intelligence.