The banking and financial services industry is under pressure and facing changes like never before. With the wide variety of services that today’s financial institutions provide, combined with the move to a digital world, operational complexity is growing. Accepting and managing change is necessary for survival. Just look at how nimble new tech companies have been introducing digital innovation in payments, lending and online banking.
On top of all that, when COVID hit, governments around the world asked banks and credit unions to help make and administer loans to small businesses. In the US, big FIs did about 60 percent of these, with mid-size, regional and community institutions picking up the other 40 percent. It’s created a ton of extra work. According to the US Small Business Administration, Paycheck Protection Loans (PPP) increased small business lending volume for smaller FIs by a factor of 20. Administering these loans has put what is already one of the most highly regulated industries in the world under more regulatory scrutiny than ever.
To help navigate the crisis, better serve their customers, and operate more efficiently, Finserv is looking to data analytics and data science. Fortunately, there is new technology available to help them quickly pull together data—including transaction level data—from all of their different operations in order to address the incredible challenges they face every day. With the ability to answer difficult questions and glean insights that previously would have required time-intensive, cost-prohibitive analysis, their time from decision to action is much lower, while their confidence level is higher level. This is what FIs need to compete today: to make the right moves, and fast.
Beyond managing PPP
Banks and credit unions need to take advantage of data analytics not just to manage the PPP loans, but to help monitor what’s happening in other parts of their business during the crisis and beyond. For example, as account opening moves primarily online, they need to improve fraud prediction capabilities. Insights from analytics can help reduce losses due to increased credit card charge offs, and improve the ability to predict segments most likely to default on loans. Those lead to direct bottom line benefits.
By combining external and internal data sources, the value derived from analytics can be even greater. We saw one client bring in daily Johns Hopkins University data showing US COVID cases and hospitalizations, which they then matched to customers by ZIP code to get an idea where business closures and rising unemployment might affect their business. People in hard hit areas might have trouble paying back loans. You may adjust your sales forecast to accommodate for lower car and home purchases, or fewer opened checking and savings accounts. Analytics helped them get out ahead of rising trends.
What’s historically made this kind of analysis so difficult for FIs is the time, effort, and cost to combine all their internal data sources for meaningful analysis and insight. A typical regional bank, for example, will have somewhere between 15 and 25 separate sources of data. The big problem has been bringing those together to get a complete view of the customer. Simply getting clean data in a single location to do analytics took weeks, or even months. If you wanted to integrate critical outside data sources, the problem gets even bigger. That’s changing because finally we have technology that simplifies the process of bringing all the data together, including transaction level data.
Data everyone wants
In financial services, transaction level data is what everyone wants. In all the years I’ve been helping FIs with analytics, reporting, and dashboards, one guarantee is that someone is going to look at your analysis and say, “I need to see the individual transactions that make up that number.” And in almost all cases, getting to that level of detail is really hard or time consuming.
But when you have that transaction data in the same place as the summary data, and you can just hit a button and see the transactions that make up that number, well that changes things. Now you have the ability to find insights in real time that at best took days before, and at worst, you couldn’t find at all. That adds real value. Beyond helping survive COVID, it can help FIs come up with solutions for some of the longest-standing issues in the industry: pricing, fees and customer retention, to name just a few.
The recent PPP loans are a great example of a customer retention opportunity. There’s a lot of data associated with those loans, and if you can bring all the detailed data around each one of those loans together in one place, and then look at all the other data about those customers at the same time, you can answer questions such as, who needs another loan? Who should we market other products to? Which products?
Discovering the unknown
I’m always excited by the newly found insights business teams uncover when they’ve been able to easily work with data. For example, we were working with a community bank on a project to pull together a 360-degree view of their customers. During that exercise, they discovered they had one branch that was outperforming all other similar-sized branches in different geographies. Customers at this branch had more money in checking and savings, and had more loans with the bank.
It turned out that particular branch had done some targeted marketing to certain groups of people that improved their customer experience. The bank was able to replicate that program and boost the other branches, based on that discovery.
This is what can happen when you put more data in the hands of Finserv professionals to explore. We know what the problems of today are, and the long-standing problems of the industry that analytics can help solve, and new tools can finally help us find answers. But we don’t know what we don’t know, and we can’t begin to know until we see all the data. As we begin to see a more complete picture, exciting breakthroughs are sure to emerge.
Originally posted by Incorta