How a Mid-Sized Bank Cut Defaults by 25% with our AI solution
Overview: Our AI-Powered Solution for Credit Scoring
A mid-sized bank was struggling with rising loan defaults. More and more people were defaulting on their loans, and their old way of checking credit just wasn’t cutting it anymore. The system they used was slow, relied on rigid rules, and couldn’t handle tricky cases. It was frustrating for everyone. The bank was losing money, and good customers were getting turned away unfairly.
So, we worked with them to build something better. OrangeMantra gave them a smart finance AI agent that looks at all kinds of financial details, not just credit history but things like banking habits and spending patterns. The change made a huge difference. Loan approvals got faster, the bank saw fewer defaults, and customers who deserved loans weren’t getting stuck in paperwork limbo.
BFSI
AI Model Development
Our Process of Building AI Agent for Modernizing Credit Scoring
We took a systematic approach starting from data acquisition to deploying a scalable inference system to build a robust and interpretable AI solution.
Data Acquisition & Preprocessing
We worked with the bank’s data team to gather years of loan information. Our team gathered everything from application details to how borrowers actually repaid their loans. We carefully protected privacy by anonymizing all personal information before data cleanup.
Engineering Predictive Features for Smarter Credit Decisions
Our AI agent development company derived advanced features such as income stability, credit utilization trends, spending volatility, and repayment consistency. These behavioral indicators improved the model’s ability to assess new-to-credit and borderline applicants with greater confidence.
Validating Models with SMOTE and SHAP for Reliability
We tested logistic regression, random forest, and XGBoost. After cross-validation and SMOTE, XGBoost performed best with 82% recall and 76% precision. We used SHAP values to explain the predictions.
Deployment & Integration
The final model was deployed as a REST API via Flask and hosted on AWS Lambda. It integrated with the client’s internal loan processing system. The setup was secured with IAM-based access control and monitored using AWS CloudWatch.
