Developing Customer Risk Scoreboard

Xerago helped a major Indian bank build a customer-risk scoreboard for personal loans.

A major private sector bank headquartered in India which has 857 branches, 4 service branches,54 ext.counters and 20 Regional Offices spread across more than 27 states and 3 union territories in India. It has set up 1334 ATMs and 42 Bulk Note Acceptor/Cash Deposit Machines all over India.

The bank has been providing personal loans to their customers based on their experiential risk assessment model and they wanted to improve this approach by implementing a robust Predictive Analytics model to assess customer risk. Xerago helped the bank in building a customer risk scorecard for personal loans.


The timeline available for the task was extremely tight (45 days from start to end). Most of the customer demographic data maintained by the bank was incomplete and hence modeling was mostly confined to customer transactions


  • Xerago studied the data sources available with the bank and identified variables that would be helpful in predicting the customer risk.
  • Cleaning up and transformation of data was done to suite the requirements.
  • Using the identified variables and additional extracted features we developed a predictive model that could provide the propensity of a customer to default a personal loan payment.
  • The propensity scores were studied to identify different categories of risk and based on that customers were categorized into three risk categories (Low, Medium & High risk).


The model predicted customers having Non-Performing Assets (NPA) with a high accuracy (around 70%).

On scoring active customers (CASA customers), the model predicted 58% customers as Low risk, 29% customers as Medium risk and 13% as High risk customers.


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