The power of data analytics in transactional banking

Transactional banking is one of the most important areas of the financial sector: millions of cash management, working capital financing, and trade finance transactions that enable a company to operate efficiently are processed daily. The amount of data generated by these transactions is enormous and can be difficult to manage and analyze efficiently. That is why data analytics is a powerful tool for converting data into valuable information to help us make strategic decisions.

It is 8:30 in the morning on a spring day in Madrid, time to leave home for work. Weather apps indicate a clear day and temperatures between 15ºC and 27ºC, which means that we leave our sweaters at home today. I get into the car, and the satnav tells me that the route to my office is 5 minutes late due to an accident on the highway.

Mobile apps or computer programs are designed to use large amounts of data and through algorithms to help us answer simple questions such as what will the weather be like today? (forecasting) or what is the best route to work? (optimizing).

In both personal and work life, informed decision-making is essential for successfully completing our tasks. The ability to gather and analyze relevant information about the market, competitors, customers, and other factors, such as the impact of sustainability on the company's finances, can make the difference between an organization that thrives and one that falls behind.

Some of the most common data use cases in banking today are chatbots that mainly use trained language programs to handle customer requests in retail banking, fraud detection algorithms, robotization of back office processes, and customer segmentation to offer products and services.

However, there is still much to be done. As banks, we can offer our customers access to tools and solutions that enable them to effectively collect, analyze and use data to make informed, strategic decisions that answer more complex questions that a finance team might ask on a daily basis. For example, a treasurer might be interested in finding out what the projected cash flow in his accounts is for this month based on information from previous years; a sustainability manager might be interested in knowing what the emissions are in her supply chain, while a risk manager might want to know what the average payment and collection terms are in a specific industry sector.

As banks, we can offer our customers access to tools and solutions that enable them to effectively collect, analyze and use data to make strategic decisions.

It is essential to understand and identify the information needs of our customers. The first step in arriving at a good definition is to ask specific questions: what information does my customer need? What data might you have to help him? These questions allow us to search for and collect the data required to develop an algorithm or program that transforms that data into quality information for our customers, thus driving their growth.

In the information age in which we live, we have an excellent opportunity to co-create and challenge ourselves to identify and formulate those questions that could be answered with data to deliver more added value to our customers. Questions will need to arise and be built in the vast majority of cases from finance teams and customer relationship business teams to pose use cases to data scientist or software engineering teams.

That is why we must include a space to discuss and ask questions in our business agendas and then challenge the bank to work on use cases to transform data into information that answers those questions.

At BBVA, we are on the way to offering our customers information that will help them make informed decisions on how to manage their company's finances. We want to drive growth, improve profitability and increase our customers' competitive advantage by delivering value through data.