The 2018 World Series starts tomorrow. What do Major League Baseball and the banking industry have in common?
Consider this: of the five American League teams who made the playoffs, only two of them are considered Goliath or top-market teams: the Boston Red Sox and the New York Yankees. The others are considered mid-market teams, or Davids. Without getting too deeply into what defines top-market vs. mid-market (market size, total revenues and how much you can spend on players), the mid-market guys are more than holding their own against better-heeled competition. Why?
Consider this: the Houston Astros were something of a major league joke from 2011-13, when they lost almost twice as many games as they won. But around 2014 they started doing something pioneered by the Oakland Athletics in 1998: using data analytics to determine what talent to hire and what talent to let go. As with the Athletics, the rebuilt, analytics-driven Astros started winning more games and continued using analytics to build a championship-caliber team. Unlike the Athletics, the Astros won a World Series (in 2017) featuring six draft picks, nine trade acquisitions and only five (more expensive and risky) free agent signings—hired in part thanks to analytics.
But data analytics did more than help the Astros pick top talent.
It helped deploy players on the field in “defensive shifts.” It helped identify players’ strengths and weaknesses as fielders and hitters. It helped set their starting pitching rotation and identified which relief pitchers should be called into which games in which situations.
What does that have to do with community banks? More than you think.
While about half of the largest Goliath banks — $50 billion in assets or more — use data analytics for fraud detection, compliance, cyber-security, hiring, marketing and outreach — only about 9 percent of institutions with assets less than $1 billion have invested in advanced analytics.
More importantly, many Goliath banks are now going after the community banking market. Simply put, they’re using data analytics to play “small ball” and beat community banks on their own turf at their own game.
The lesson is obvious: keep up with the competition or else.
Just as the Astros beat the Goliaths by using data analytics to determine which players to choose and how to improve and deploy them, community banks can beat banking’s Goliaths by using data analytics to determine how much of their tech budget to deploy on hiring, marketing, fraud detection, compliance, overall efficiency and the ability to react quickly to any situation.
Quickly is the operative word. Just as quickly as a baseball game can change with a stolen base that leads to runs scored and ultimately, a loss for the team on the field, that’s how quickly a bank’s fortunes can change when a single act of fraud leads to losses in the millions.
For example, the CEO of Florida-based First Farmers Financial LLC orchestrated a sale of 26 fake loans to Milwaukee investment firm Pennant Management. By the time the fraud was uncovered, Pennant’s woes had spread to other institutions like the Illinois Metropolitan Investment Fund, which lost nearly $50.4 million; the University of Wisconsin Credit Union, which lost $52.9 million; and a smaller Illinois bank, which closed.
Could data analytics and fraud detection have prevented this? Analytics employed by small- to mid-sized organizations have more than paid for themselves in terms of fraud detected or prevented. One mid-sized U.S. organization realized an ROI of 702 percent including recovery of $2.9 million in vendor overpayments, prevention of $250,000 in purchase card fraud and over $360,000 savings in labor costs from improved vendor reporting.
Investing in analytics can make a winner of any David organization no matter how much they’re being outspent by the Goliaths. All it takes is a commitment, an all-in effort from your team and a willingness to trust the process no matter where it takes you.
Kris Hutton is director of product management at ACL, a provider of enterprise governance SaaS powered by data automation. Kris can be reached via ACL at www.acl.com or https://www.acl.com/industry-solutions/banking-and-lending/