Risk Profiling of Customers for Installment Loans in Lending Business

About Client

The client is Canada’s largest consumer finance retailer, operating hundreds of stores nationwide and offering a wide range of financial services. The organization serves a diverse customer base and manages high-volume lending operations across multiple locations. Risk control and portfolio health are critical to sustaining growth in such a competitive lending environment.

The Background

Credit risk analysis is a core function for financial institutions and plays a critical role in maintaining operational stability. Unsecured short-term loans are among the most widely used credit products globally, increasing competition within the lending space. As portfolios grow, customer risk profiling in banks becomes necessary to assess creditworthiness accurately, monitor portfolio health, and ensure informed lending decisions through advanced analytics.

The Challenge

As part of its strategy to expand revenue and market presence, the client planned to launch a new installment loan product. To succeed, the client required an analytics solution that could address multiple risk and performance concerns, including:

  • Predicting defaulters and non-defaulters with high accuracy.
  • Identifying insolvency risk early in the customer lifecycle.
  • Reducing Non-Performing Assets (NPAs) while improving approval rates.
  • Managing risk effectively for new customers and thin credit files.

Existing methods lacked the analytical depth required to support these objectives at scale.

Our Solution

We implemented an advanced analytics framework centered on customer risk profiling in banks, tailored specifically for installment lending.

Key components of the solution included:

  • Conducting exploratory data analysis and thin-file analysis for new and underbanked customers.
  • Consolidating customer data from Credit Unions, customer application forms, and other external and internal sources.
  • Identifying over 500 potential variables impacting credit risk and repayment behavior.
  • Applying Machine Learning algorithms to filter and prioritize high-impact variables for improved predictive accuracy.
  • Building multiple Machine Learning models to predict delinquency and insolvency.
  • Using feature extraction techniques to select important variables based on weightage.
  • Leveraging data visualization to uncover trends in customer behavior, product performance, and store-level outcomes.
  • Analyzing delinquency patterns by geography using Power BI, SQL Server, and MS Excel.
  • Flagging high loan balances as potential indicators of fraud or elevated risk.
  • Incorporating competitor data from Credit Bureaus to extract additional customer insights.
  • Segmenting customers using Machine Learning-based risk scores.
  • Performing impact analysis to evaluate installment loan performance against other products and ensure optimal revenue generation.

Outcome

The client recorded

47%
increase in Loan book within a quarter as their approval rates improved.

They were able to identify the best and worst performing loan products.

They were able to predict defaulters, non-defaulters, and insolvency which significantly reduced their NPA.

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