At Regions, our first priority is always the customer – meeting their needs and serving them in ways that help them reach their financial goals. A decade ago, you could say that service differentiated companies, especially in banking. Today, service would still be the differentiator, but how a customer – and how we – define “service” has evolved.
In that same past, service was equated with smiles and being greeted when you visited a retail location. And it still is. But it’s also defined today by anticipating needs, trends and knowing our customers so we can meet their financial objectives efficiently, safely and in a personalized manner.
This is where advanced analytics, data and machine learning come into play. For a company like Regions, how can an enterprise operationalize innovative learning? How could we leverage AI in a way that is repeatable, sustainable and, perhaps most importantly, trusted? How can you make it work for the benefit of our customers and the bank? We faced these questions when standing up our data and advanced analytics capabilities.
Over the past couple of years, we’ve transformed advanced analytics using modern tools and new, open and transparent methodologies. After creating an analytics Center of Excellence, we’ve brought data into a centralized data lake environment, rolled out a data governance framework, applied machine learning and AI techniques, and, above all, adopted an end-to-end business value approach that emphasizes value delivered by the products we create.
The result has been trusted analytical solutions that help reduce risk, detect fraud, assist commercial relationship managers and private wealth advisors, and provide insights into consumers so we can better meet their needs.
A repeatable development process for data products
We call such solutions “data products,” and we build them using an agile, repeatable approach that brings disciplines from software engineering to data management and advanced analytics.
After we identify a problem, we devise a business case for solving it. Next, we lay out a roadmap and assemble a multidisciplinary development team. The team applies agile methods to ingest relevant data, build the analytical model and user interface (UI), roll out the solution, evangelize adoption, assess the impact, and monitor and measure performance of the data product.
Creating a data product is never a one-and-done effort, however.
The team keeps improving it by working through a backlog of features, fixes, maintenance items and new releases.
Trust requires strong AI ethics and governance
Still, we need to ensure that our AI models are fair and ethical. Regions prides itself in open and trusted customer relationships. Our values involve doing the right thing and improving the lives of our communities, customers and associates. That’s why we bake AI ethics oversight into our development methodology.
Ethical AI requires data completeness, accuracy and quality, and the data underlying the models must be representative of the data used to make the decisions. Plus, the models must be “explainable,” meaning their decision-making process is clear.
To keep our solutions on point, a variety of stakeholders provide oversight into the data and the transparency of our models. An internal oversight team helps ensure the fairness, safety and soundness of our solutions, as do risk management, audit partners and government regulators.
Synergy and alignment with data tools and personnel
We have been both intentional and fortunate in our data journey in that we’ve worked with some of the best vendors and suppliers to help us grow and mature our data approach. These groups – many with names that you would recognize as leaders in the industry – have helped us catalog and analyze data, assess data drift, measure model performance and keep our personnel informed.
Because of these working arrangement, we can stay ahead – of customer needs and competitors – because we are not simply building a model. We’re building a product, and many different sequences are needed to get from the business problem to measuring the solution’s impact. And then we repeat the cycle.
We also get our oversight partners involved early to understand the business case and requirements, along with the code and the developers’ intent throughout every sprint, so they can provide faster feedback while maintaining the required independence. This accelerates development of the high-quality, trusted AI solutions that Regions Bank strives for.
To go back to where we started, prudent use of data should almost be synonymous with service – providing more meaningful personalized recommendations, anticipating needs, spotting fraud patterns, keeping communities safer, helping ensure that we continue to improve, and more. Data, in and of itself, is only potential energy. It represents what we could find out and do for customers. What we do – the application of that kinetic force – is how we take that data, analyze it, model it, apply it, test and repeat that shows its true power to make banking easier for our customers.