Risk Profiling of Customers for Installment Loans in Lending Business

Our Solution

• In order to meet the objective, the situation called for exploratory data analytics and thin file analysis for new customers and identifying necessary variables from the ocean of customer data procured from Credit Unions, forms filled by the customers, and more.
• As a result, over 500 variables were identified. For high accuracy, Machine Learning algorithms were deployed to filter the key variables from the dataset.
• Multiple Machine Learning models were built to predict delinquency and insolvency.
• Feature extraction mechanism was used to select important variables based on weightage.
• Various Data Visualization techniques were tested and reports generated for discovering trends in customer behavior, product performance, store performance and to identify customer delinquency patterns.
• Delinquency trends were identified based on locations using Power BI, SQL server, and MS-Excel
• High loan balances were marked as threat to identify fraud or high-risk customers
• Competitors data were obtained from Credit Bureaus for reverse engineering and extracting more information on customers
• Machine Learning algorithms were deployed for segmenting customers based on their scores
• As a final step, impact analysis of a product against the performance of other products for ensuring optimal revenue generation