AI|ML Product Engineering
AI powered automated credit scoring for loan approvals
NutaNXT built a customized AI/ML based customer qualification and scoring model to make real-time decisions for underwriting new loans within seconds.
A B2C Fintech lending platform company aimed to change the way loans are granted, and wanted to bring together new credit scoring systems for superior customer profiling.
Technologies Used By NutaNXT
- AI & ML Solutions
- Predictive Analytics
- Amazon Web Services
Use Case Applicable To Verticals
- Real Estate
Banks, Credit cards and other lending financial institutions use credit scores to evaluate potential customers for risk assessment when lending money or providing credit. Traditional credit scoring methods typically need various data such as credit scores, payment history, debt to income ratios, etc. While the largest growth opportunity for credit may be lending to the middle to low-income segments, access to quality credit information on consumers is very limited for this segment, which made it important for the client to build a new automated credit scoring model.
Key challenges with the existing model were:
- In the absence of quality data, they typically denied credit to a large low income segment of consumers without much consideration of information or specific credit situation.
- While in some areas, they had overexposure to high risk borrowers, in some ways, our client missed out on business opportunities by not disbursing loans to large numbers of quality borrowers.
Team we built
- Data Scientists
- Cloud Architect
- Data Engineers
- DevOps Engineer
NutaNXT built an AI/ML based credit process and scorecard to determine creditworthiness of new customers factoring large amounts of data enabling the client to run sophisticated models that predict loan default risk and underwrite new loans within seconds.
- We engineered a variant form of data into a new credit scoring pipeline, and iterated the ML pipeline with distinct algorithms.
- We formulated feature engineering, calibrated hyperparameters and deployed appropriate ML models for credit scoring.
Technology Stack Used
Loan Default Rate
Business Impact and Results
Using AI, the loan process was streamlined, which helped improve customer experience, through an advanced credit score.
- 30% REDUCTION IN DEFAULT RATE
- 42% INCREASE IN LOAN VOLUME
- SIGNIFICANT REDUCTION IN PORTFOLIO RISK
Achieved through improving the volume and quality of Data for credit underwriting decisions.
By implementing an automated, Data-based loan approval process to make underwriting decisions within 8 seconds per loan.
Driven by lower customer dropout rates and significantly lower delinquency rate for first time borrowers.
From MVP to end-to-end digital products, our culture of intelligent innovation and engineering excellence have solved complex Digital Product Engineering challenges and delivered maximum business ROI for our clients.