Problem

One of the major challenges faced by banks in the competitive lending market is to retain their existing borrowers, who may be tempted to switch to other lenders that offer lower interest rates or better terms. This can result in a loss of revenue and market share for the bank, as well as a decrease in customer satisfaction and loyalty. To prevent borrower attrition, banks need to identify the clients who are most likely to refinance their loans or mortgages with other lenders, and proactively offer them incentives to stay with the bank. However, this task requires advanced analytics and predictive modeling, as the factors that influence borrower behavior are complex and dynamic.

Solution

The solution used predictive lending models to identify the borrowers who were most likely to refinance their loans or mortgages with other lenders, based on their personal, financial, and behavioral data. The solution leveraged machine learning techniques to estimate the probability of refinancing for each borrower, and the expected loss for the bank if they did so. The solution also provided actionable insights and recommendations for the bank to retain their existing clients, such as offering them lower interest rates, flexible repayment options, or loyalty rewards. The solution helped the bank reduce borrower attrition, increase customer satisfaction and loyalty, and improve their competitive advantage in the lending market.

Outcome

The outcome of this solution was that the bank was able to retain more of its existing borrowers, who would otherwise have switched to other lenders, by offering them personalized and competitive deals. The bank also improved its customer satisfaction and loyalty metrics, as well as its market share in the lending sector.