Banco do Brasil SA
Contact: Daniel Ressel Okuyama
Country: Brazil
Award Category: Innovative Problem Solver
Tell us about the business problem you were trying to solve.
At Banco do Brasil, we’ve always been committed to offering our customers credit limits that align with their financial reality. The Maximum Payment Capacity (MPC) calculation is central to this process, ensuring a balance between credit access, risk mitigation, and responsible lending.
For years, we relied on data from Brazil’s official statistics bureau. While this dataset was valuable, it had its limitations. Since the survey is conducted periodically, the information could become outdated, and the segmentation criteria—income brackets, risk profiles, and state of residence—didn’t fully capture each customer’s financial behavior.
That’s when we saw a game-changing opportunity: leveraging advanced analytics to make our MPC calculation more dynamic, precise, and personalized, truly reflecting the spending habits of each individual customer.
What SAS products did you use and how did you use them?
The solution came through technology and data-driven innovation. SAS played a crucial role in building our new Personalized Payment Capacity Model, providing the tools to process massive amounts of data with speed and accuracy.
Using the SAS platform, we analyzed billions of financial transactions over a 12-month period, integrating them with customer profile data from over 60 million accounts. Through advanced clustering techniques, we identified distinct customer behavior patterns, allowing us to generate real-time, hyper-personalized payment capacity estimates.
The result? A highly sophisticated, data-driven model capable of processing billions of possible combinations, ensuring each customer receives a credit limit that truly reflects their financial profile—fairer, more precise, and more adaptive than ever before.
What were the results or outcomes?
The impact of our new model was nothing short of remarkable. With our refined approach, we successfully adjusted the Maximum Payment Capacity for 19 million customers.
This directly translated into $18 billion in additional credit for Personal. On top of that, we project an additional $407 million in annual contribution margin, strengthening both our customers’ financial well-being and the bank’s sustainable growth.
This transformation proves the power of data-driven innovation: a smarter, fairer, and more efficient credit model that builds customer trust and drives the future of lending.
Why is this approach innovative?
What makes this solution truly groundbreaking is its ability to move beyond traditional credit assessment methods and embrace real-time, data-driven decision-making. Instead of relying on outdated statistical surveys, we harnessed the power of advanced analytics and machine learning to build a model that dynamically adapts to each customer’s financial behavior. By processing billions of data points and identifying behavioral patterns, we created a hyper-personalized approach that not only enhances risk management but also expands responsible credit access. This innovation represents a major shift in how financial institutions can leverage technology to drive smarter, fairer, and more inclusive lending practices—benefiting both customers and the banking industry as a whole.
