Name: App Development & Digital Transformation Company
Brand: OrangeMantra
Rating: 4.8 (5600 reviews)

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.

Industry

BFSI

Services

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.

The Problem: Traditional Scoring Models Couldn’t Stop Rising Defaults

The bank was stuck in the past. They used old-school systems and rigid rules to decide who got loans. These outdated methods couldn’t keep up with how people actually handle money today. When facing borrowers with non-traditional incomes or thin credit histories, the system just froze. Applications piled up while the bank manually reviewed edge cases. Good customers got stuck waiting. The result was frustrated customers and unnecessary risk.

Our Role: Full AI Development for Smarter Lending

Built AI credit scoring model

Used XGBoost for accuracy

Deployed secure API

Ensured compliance

Project Challenges: Addressing Bias and Data Imbalance

Fairness & Compliance

Financial institutions are held to strict regulatory standards. We worked with the client’s compliance team so that the model meets Fair Lending and GDPR norms. We implemented feature audits and bias testing pipelines during development.

Data Quality & Class Imbalance

The dataset showed an imbalance. Less than 15% of cases were defaults. We tackled this with a combination of SMOTE (Synthetic Minority Over-sampling Technique) and custom class-weighting. We made sure the model didn’t unfairly favor majority class predictions.

Results Delivered by Our BFSI AI Agent

Our finance AI agent became a central part of the client’s loan origination strategy. Real impact of our finance AI agent is:

40% Faster Loan Approvals

Loan teams were able to pre-approve applications in minutes rather than days with real-time scoring. This made things faster and easier for everyone.

25% Reduction in Default Rates

The model spotted early signs of risk. This helped lower defaults during a 6-month pilot. Risk tiering also helped the team make smarter and quicker decisions.

Scalable and Secure Integration

The API architecture made it simple to connect the model to the client’s loan system. Authentication and audit logs added strong security and clear tracking.

How a Mid-Sized Bank Cut Defaults by 25% with our AI solution