Our client, a leading NBFC in Sri Lanka, partnered with NeenOpal to enhance its gold loan disbursement process by building a machine learning-based customer scoring model. Instead of traditional credit checks, the model enabled smarter loan-to-value (LTV) decisions, allowing the client to confidently offer higher LTVs to trustworthy customers based on historical gold loan and transaction behavior.
The client struggled with outdated, manual processes for risk assessment and loan approvals. This led to inefficiencies, inconsistent decisions, and missed opportunities for personalization.
The client relied on static rules and manual judgment to assess customer eligibility, leading to delayed approvals and inconsistent turnaround times.
A few high-risk customers received excessive loans, while some loyal borrowers were underfunded, impacting portfolio quality and growth.
All customers were treated with uniform risk criteria, missing opportunities to tailor LTV offerings based on individual behavior and history.
NeenOpal developed a customized, end-to-end machine learning–based credit scoring system specifically designed for the client’s gold loan operations. The solution aimed to automate and enhance risk evaluation, improve loan approval efficiency, and enable data-driven, personalized LTV decisions.
A comprehensive data pipeline was built to extract and cleanse historical loan data, including ticket-level gold loan transactions, repayment timelines, pledged gold quality, previous LTV ratios, and customer demographics. This structured dataset formed the foundation for building reliable predictive credit scoring models.
Key behavioral and financial indicators were extracted and engineered into meaningful features. These included metrics such as loan application frequency, overdue duration trends, timely loan closures, gold valuation consistency, and borrower repayment patterns, all contributing to predictive accuracy.
A linear regression model was implemented to derive a weighted scoring equation that balanced multiple customer attributes. This scoring mechanism provided each borrower with a numerical risk score, enabling objective and scalable evaluations.
Based on the generated scores, customers were segmented into risk tiers—low, medium, and high. This allowed the client to implement differentiated loan strategies such as offering higher LTV ratios and faster approvals for low-risk customers, while maintaining caution with high-risk segments through stricter approval criteria or lower loan amounts.
NeenOpal brings deep expertise in financial services, specializing in machine learning solutions for credit risk modeling, customer scoring, and loan optimization. Our team has built predictive models using complex datasets, including gold loan portfolios and repayment patterns. With strong capabilities in supervised learning, feature engineering, and model interpretability, we help financial institutions automate risk assessment, reduce defaults, and make smarter lending decisions, while staying aligned with regulatory and business goals.
The solution helped the client to make faster, smarter, and more personalized lending decisions. By embedding data science into core operations, it improved efficiency, consistency, and customer satisfaction across the gold loan business.
The deployment of a machine learning–powered customer scoring engine transformed our client’s gold loan operations. By leveraging predictive analytics, the model enabled more precise LTV decisions and significantly reduced approval turnaround time by 40%. This led to faster, more tailored disbursals, improved portfolio quality, and a scalable foundation for long-term, data-driven lending growth.
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