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This is our story of co-creating a solution with a leading Australian bank

As we know ...

It’s imperative for banks to retain their mortgage customers and take preventive measures to curb attrition as a result of re-financing.

However, understanding when and how to target the right customers at the right time requires sophisticated predictive analytics models and effective retention strategies.

The challenge for the bank was...

The attrition rate among the bank’s home loan customers had been steadily increasing over a period of three years. The runoff rate of the bank’s loan portfolio hovered at 13 percent while the industry average was nine percent during this time.

The bank leveraged WNS’ analytics expertise and experience in regional banking to identify customers who were likely to attrite, and design effective campaign strategies to target them. This was the bank’s first retention campaign.

Here’s what we co-created as a solution…

WNS built two sophisticated attrition propensity models to accurately segment customers who were likely to re-finance with another bank. As a first step, the bank’s home loan database that consisted of 80,000 customers was analyzed.

It entailed the following:

  • 500+ variables were analyzed (such as customer and branch information, transaction attributes, products and mobile banking, customer complaints and risk factors)

  • 10 million rows of transactions were studied to capture data patterns

  • The models were built on the basis of logistic regression and Cox hazard techniques

  • Siloed data sets and data marts were integrated to create a unified view and provide access to all relevant stakeholders

The attrition propensity models helped identify 3500 customers who posed the highest risk of opting for external re-financing. Other insights included the following:

  • Whenever the client reduced the interest rates while competing with rival banks, it resulted in some mortgage customers looking for better interest rates in the market and consequently re-financing with another bank

  • The propensity of attrition was lower among customers with salaried accounts or standing instructions for direct debits with respect to bill payments, or customers who had higher balances in their savings accounts

  • The highest risk of attrition was among mortgage customers with accounts that were 2-5 years old

WNS segmented the customers into six clusters and in collaboration with the client devised strategies to retain them. Each cluster was targeted with the right promotional activity that included offering discounts, loyalty points and vouchers.

Our learnings and outcomes from the process of co-creation are…

That the analytics models helped target the right group of customers who were likely to re-finance.

The retention campaign strategies enabled the client to:

  • Reduce the attrition rate of home loan customers by 10 percent

  • Enable cross-selling of products to 10 percent of mortgage customers

A comprehensive document that details the models and algorithms created by WNS is now a ready reckoner for the bank. The bank’s strategy team leverages this for day-to-day operations.

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