Problem:

A large national bank wanted to increase its lending revenue by offering loans and mortgages to existing deposit clients who have a high propensity to borrow. However, the bank did not have a reliable way of identifying which clients are most likely to need a loan or mortgage in the near future, and how to best communicate with them. This leads to missed opportunities and lower customer satisfaction.

Solution:

The solution was to develop a data-driven solution that leverages the bank’s historical transaction data, as well as external data sources, to segment the deposit clients based on their borrowing propensity and preferences. The solution used machine learning techniques to analyze the clients’ past and current behavior, such as spending patterns, income level, credit score, life events, and feedback, to identify the most likely candidates for loans and mortgages. The solution will also recommend the optimal products and channels for each client segment, such as email, phone, or in-person, to maximize the conversion rate and customer satisfaction. The solution was scalable, robust, and compliant with the bank’s policies and regulations.

Outcome:

The outcome of the solution was a significant increase in the loan and mortgage revenue for the bank, as well as an improved customer retention and loyalty. The bank was able to target the right clients with the right offers at the right time, using the most effective communication channels. The solution also enhanced the bank’s reputation and trust among its deposit clients, who appreciated the personalized and tailored service. The solution proved to be adaptable and flexible, as it could incorporate new data sources and feedback loops, and adjust to changing market conditions and customer needs.