AI-Powered Risk Engine Reduces Loan Default Rate by 28% for a Mid-Sized Financial Institution
Mid-sized bank with national operations and a growing digital lending portfolio A forward-thinking financial institution sought to modernize its credit risk evaluation process to improve loan approval accuracy, minimize default risk, and accelerate decision-making across consumer and SME lending.
Time
5 Months
Team
7 Members
Platforms
Web, Core Banking Systems
Type
AI-Powered Risk Engine
Industry
Financial Services
IDEA
Revolutionizing Loan Risk Assessment with AI
CHALLENGE
Addressing Inefficiencies and Risks in Traditional Lending Processes
- High loan default rates, especially in unsecured personal and SME lending segments
- Manual credit scoring methods rely on rigid rule-based models and outdated financial indicators
- Slow loan approval processes create friction for customers and internal underwriters
- Limited use of alternative data sources, such as transaction history, behavior analytics, and income patterns
- Growing pressure from digital-first competitors offering faster and smarter loan experiences
SOLUTION
We built and deployed a fully integrated AI-Powered Risk Engine designed to analyze both traditional and alternative data sources for smarter, faster, and more reliable loan decisions.
- Machine learning–based risk scoring models trained on historical default data across borrower types
- Incorporation of alternative data signals like spending behavior, cash flow trends, and repayment patterns
- Real-time loan application scoring, with explainable decisioning to support regulatory and compliance needs
- Automated flagging system for high-risk applications, allowing underwriters to intervene early
- Seamless API integration with the client’s core banking and loan origination systems
What they said is all that
matters to us!
Head of Dept
Credit Risk & Lending Innovation
This risk engine has fundamentally changed our credit operations. We’re making faster, more accurate decisions—while maintaining trust with regulators and customers alike.
Priority Features
Machine Learning Risk Scoring
Accurate borrower evaluation using data-driven risk models.
Alternative Data Signals
Includes transaction history, cash flow, and spending behavior for smarter decisioning.
Automated Credit Scoring
Faster loan approvals with real-time, automated decisioning workflows.
Real-Time Risk Flagging
Identifies high-risk applications early, allowing for timely underwriter intervention.
Tech Stack
RESULTS (Within 6 Months)
28% reduction in loan default rate across personal and SME loans
41% faster loan approval times via automated credit scoring workflows
33% improvement in approval precision, approving more reliable borrowers without increasing risk
20% increase in application throughput, enabling higher loan volumes with the same staff
Enhanced regulatory compliance, with explainability features built into every scoring decision
Sales & Operation
Come to say Hello
C 304, Parshwanath Metrocity, TP44, Nigam Nagar, Chandkheda 382424.Development Center
Come to say Hello
C 304, Parshwanath Metrocity, TP44, Nigam Nagar, Chandkheda 382424.Got ideas? We've got the skills.
Let's talk over some virtual coffee!
sales@madforcoding.com