In 2002, Billy Beane was the manager of the Oakland Athletics in Major League Baseball. Oakland was a small market club with a similar sized budget and it struggled to be competitive.
Because Oakland didn’t have the money of big market teams like the New York Yankees or Los Angeles Dodgers, Beane knew he couldn’t hope to attract the high-priced talent – the superstars – to play in Oakland.
Enter Paul Depodesta, aged 27, an economics graduate from Harvard, with an analytical mind and a love of baseball. His arrival on the doorstep of the Oakland A’s gave birth to data analysis in professional sports.
He analyzed player stats, using computer algorithms, and his results allowed Oakland to sign inexpensive players that other teams dismissed. The A’s were propelled into the stratosphere of success, thanks to big data.
This is the “secret sauce” in data analytics: the ability to take substantial amounts of information – in the case of Oakland, endless baseball player statistics – look for patterns and capitalize on what is found.
Credit Unions, Machine Learning and Data Analytics
Credit unions in Canada are rapidly embarking on the same exploration. Using machine learning and data analytics, these financial firms are finding ways to improve service to their clients while, at the same time, discovering nuggets of information from the vast amounts of data they collect, that can then be turned into business opportunities.
Virtually every customer transaction within a credit union is electronic, and the amounts of data being collected are staggering. The need to analyze this information is what drives credit unions today to embrace machine learning and data analytics.
Matthew Maguire is the Chief Data Officer at Co-Op Financial Services, a California-based company that operates an interlinked system of ATM machines throughout the U.S. and Canada. He argues that machine learning and data analysis are critical for mid-sized credit unions as they work to reinforce current customer relationships and build new ones.
“Data is coming in from different places and the challenge is… how do you make it all connect?[i]” he said.
Credit unions are moving quickly into data analysis. Through machine learning, which unearths customer transaction patterns by using algorithms, credit unions are learning a great deal about their customers and are designing strategies to capitalize on that in order to drive sales.
But, for credit unions, data enables other capabilities. Patterns of fraud can be easier to spot and shut down through data analysis.
When a client invests with a credit union, regulations require the client to complete what’s called a Know Your Client form, which essentially draws a profile of risk tolerance and investment objectives. If the client’s portfolio strays from that profile and becomes riskier, big data can alert the financial institution and the problem can be corrected before any monetary loss accrues to the client – or to hundreds of thousands of clients.
Chris Catliff is the president and CEO of Blueshore Financial, a B.C.-based credit union with more than $3 billion in assets. His vision of the future of credit unions is predicated on the power of data analytics in combination with machine learning.
He envisions the day very soon when a client approaching a branch receives a text message saying the client is already checked in at the branch. As they walk through the door, their customer profile and picture pop up on a screen [ii] at a concierge desk and they’re greeted by name.
Blueshore’s ATM machines will respond to a customer’s biometrics and offer a transaction based on a pattern of previous transactions. Up-sell opportunities will present themselves, so staff can suggest options – situations that might never occur without data analysis.
Service, he said, “has to be electronic transactions with the introduction of superior, human touch at various critical points. It’s high tech and high touch.”
Explore Your Data Potential
Like the members they serve, every credit union is unique. It is imperative for a credit union to work with data specialists who can marry the individual needs of each credit union with high levels of expertise across big data, data analysis and machine learning.
One of our strengths here at Optimus is our track-record in the areas of data gathering, analysis, machine learning, dashboarding and data visualization, through which we help our clients tailor data mining and analysis to their business goals.
At the end of the day, it’s all about staying competitive and, like the Oakland Athletics, reaching the pinnacle of success by embracing and employing new strategies to achieve that success.