Problem

A national bank faced two main challenges in its commercial card business: fraud risk and customer engagement. Fraud risk refers to the potential losses from unauthorized or fraudulent use of credit cards, which can damage the bank’s reputation and profitability. Customer engagement refers to the degree of loyalty and satisfaction that customers have with the bank’s products and services, which can affect the bank’s market share and revenue growth. The bank needs a way to leverage the data from merchant card usage, which contains valuable information about the patterns and preferences of cardholders, to address both of these challenges.

Solution

The bank implemented a solution that used advanced data analytics and machine learning techniques to integrate and analyze the merchant card data along with its own customer data. This enabled the bank to identify and flag suspicious transactions, reduce fraud losses, and enhance customer trust. The solution also helped the bank to segment and profile its customers based on their spending behavior and preferences, and offer them tailored and timely promotions, rewards, and incentives. This increased customer engagement, retention, and loyalty, and boosted the bank’s commercial card revenue and market share.

Outcome

As a result of the solution, the bank achieved significant improvements in its key performance indicators, such as fraud detection rate, customer satisfaction, card usage frequency, and revenue per customer. The bank also gained a competitive edge in the commercial card market by offering innovative and personalized services to its customers.