- The Story
A rapidly growing fintech startup offering personal and business loans struggled to maintain profitability as loan defaults increased by 23% over six months. Their manual underwriting system couldn’t efficiently assess risk or personalize loan offers. To sustain growth and improve credit accuracy, they needed a data-driven transformation.
Our team partnered with the company to rebuild its credit scoring and loan distribution process using predictive analytics, machine learning, and intelligent automation.
Mapping Out a Complex Financial Challenge
The company’s challenge was rooted in three key issues:
Inconsistent loan approval patterns.
High default rates in specific customer segments.
A lack of integration between marketing, credit analysis, and customer data.
To address this, we created a transformation roadmap focused on three major pillars:
Credit Risk Optimization
Introduced an AI-powered credit model that analyzed applicant data from multiple sources — income, payment history, digital footprints, and spending behavior — to predict default probability with 92% accuracy.Data Unification
Consolidated fragmented data from CRM, application forms, and financial partners into a unified dashboard, allowing real-time visibility into portfolio performance.Process Automation
Integrated automated workflows for loan approvals, document verification, and repayment tracking, cutting manual efforts by 70%.
The Power of Data in Decision-Making
By implementing predictive analytics, the company not only improved operational efficiency but also unlocked new financial potential:
Reduction in loan defaults: 38% within four months.
Approval speed: From 3 days to 6 hours on average.
Customer satisfaction: 4.7/5 average rating post-transformation.
This new ecosystem helped the fintech confidently scale its portfolio while maintaining strong compliance and customer trust.
A Closer Look at the Transformation
The success of the project came from merging data science with human expertise. By setting up a Financial Intelligence Team, we helped the company continuously train its predictive models based on real market feedback.
This iterative improvement cycle enabled more precise risk segmentation and optimized pricing for different borrower types. The result was a 50% increase in profit margin per approved loan and sustainable long-term growth.
The Outcome
Today, the fintech stands as one of the region’s top 10 emerging lenders. The transformation not only strengthened their financial stability but also redefined their value proposition as a data-first, customer-trusted digital lender.
