Personal Loan Cross Sell

Client Background

Client is one of India’s leading financial services company focused on lending, asset management, wealth management and insurance. The company through its joint ventures and subsidiaries employs over 20,000 employees and has established a nationwide presence across over 1400 locations.

Business Need

Personal loans are unsecured loans generally offered at higher interest rates; in cases higher than interest rates offered by banks for similar loans. Traditional customer segment approaching the client for a personal loan has been one not being able to secure a similar loan from a bank. This inherently increases the risk of lending to a new applicant. At the same time, high interest rates make this product attractive from ROI standpoint. Already existing customer base with previous payment track record represents more lucrative & less risky segment for such unsecured loans.

Client ran regular email marketing campaigns across its existing customer base for personal loan cross sell. Additional reach out was made through telephonic calls. A high number of existing customer base limited effective call strategy and hence conversions.

It was therefore desired to expand the personal loan penetration across existing customer base and proactively identify customers who are likely to respond to personal loan offers.

Solution

In any lending business, business growth & risk teams need to work together to achieve a healthy portfolio with decent returns. Pro-active solution desired need to be able to identify existing customer who would respond to personal loan offers. At the same time, such an offer need to take into account risk profile of a customer. Our team, therefore, recommended the hybrid approach of marrying cross sell propensity with risk scoring.

Valiance data science team built default propensity and cross-sell propensity models. It took our team two months to come out with accurate & robust models from the start of the engagement.

  1. Default propensity was arrived at based on demographics, product loan details and different RTR parameters that were available viz. a number of cheque bounces, channels of payment, maximum delinquency etc.
  2. For Cross Sell model we profiled the responders to the campaign to understand their affinity towards different offers.
  3. Marketing campaigns were designed by overlaying cross-sell propensity and delinquency risk.
  4. Results of the campaign were evaluated over the six months time frame.
ROI
  1. 10 percent increase in the overall conversion rates compared to baseline conversion rates from previous campaigns.
  2. Portfolio generated from cross sell campaign had 10-20% higher ticket size compared with average ticket size.
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