Why Financial Institution Executives Who Adopt Analytics Will Win
December 2015

It’s been a dozen years since renowned author Michael Lewis chronicled baseball executive Billy Beane’s revolutionary use of data analytics, and the volume of “Moneyball” spinoffs has grown into a cottage industry akin to the (Fill in the Subject) for Dummies series.

Experts in hundreds of diverse industries have trotted forward examples of the value of analytics, with case studies tailored to the nuances of their field.

For the nonbelievers and skeptics in financial services, this deluge of content has to be a bit annoying. But if you continue to dismiss the power of this tool as nothing more than a cyclical trend — or, on the other hand, recognize its value in other sectors but question its application for credit unions and banks — a recent Ernst & Young survey might convince you of two truths.

One might be intimidating: Data analytics is powering the rise of the most successful financial institutions. The other is reassuring: If you’ve yet to hop fully on board, you’re not too late to the game.

High-growth financial services companies — those achieving earnings before interest, taxes, depreciation, and amortization (EBITDA) growth of 15% or more in each of the last two years — are significantly more likely than their competitors to be able to extract value from their data, the survey indicates.

According to “The Science of Winning in Financial Services,” 27% of upwardly mobile companies describe themselves as “excellent” at extracting useful insights that improve overall performance or competitiveness, more than twice as many (12%) as their lower-growth peers.

More than a third (35%) of the high-growth companies say they’re highly mature in this capability, compared with 23% of the rest.

But lest you believe that it’s too late to enter the data analytics arms race, think again. That same Ernst & Young report notes that just 28% of companies say their business functions have begun to recruit more data analysts. Similarly, just 13% of companies expect to increase spending on data-related change management programs by more than 20% over the next two years.

How does data analytics make a difference?

In a broad sense, the most cited answer is decision making. That’s the response from nearly half of C-suite respondents to a 2013 Deloitte Analytics Advantage Survey survey on the power of analytics. And that’s probably a low estimate, as the report notes many of those organizations wish they had better access or more resources to utilize their data in decision making circumstances.

That’s not surprising, for one basic reason: Who wants to determine their company’s course with less than a full set of facts?

Notably, only 1% of respondents to that Deloitte survey believe the greatest benefit of using data analytics is identifying and creating new product and service revenue streams. This serves as a call to action for organizations to better manage and integrate data analytics into their operations.

Specific to financial services, data analysis systems save time, assure data accuracy and integrity, produce comprehensive and segmentable reports, provide the opportunity for ongoing analysis and strategy shifts, and offer flexibility to quickly change reporting standards and processes, internally and for examiners.

There are many ways that using data analytics can put your organization in a strategically advantageous position. Here are four examples:

1. Capitalizing on cross-sell opportunities. By piecing together the wealth of data now available, you can discern with a good degree of statistical certainty what products each of your members would find most appealing. Failing to enhance existing relationships that you’ve worked so diligently to build is a cardinal sin of business.

2. Understanding lifetime value and risk of the member. Maintaining nonproductive relationships can be an anchor on a credit union’s ledger. To assure that you move all members toward practices that benefit them and the credit union, you must develop a system that closely monitors their behavior and prompts both cross-selling opportunities and interventions.

3. Realizing the power of derivative metrics. Strong correlations exist between a high number of variables, indicating increased chance of delinquency, product purchases, or profitability. These correlations don’t always buttress conventional wisdom and often don’t involve pure financials. They can relate to geography, age, gender, profession, education, and more. Only when this data resides in one pool can you paint a well-rounded, predictive picture of the member.

4. Commanding comparative internal metrics. Quantifying profitability at all levels of the organization has become the norm. Are certain branches carrying their weight, and why or why not? Are various loan types performing well among specific demographic groups in specific areas, but falling short among a similar cohort elsewhere? Why are some loan officers “closing machines” while other, equally talented staff slog in mediocrity? If your organization siloes data it stands a fair chance of forgoing the comparative analysis that sheds light on these answers, or forming incomplete or incorrect conclusions.

 

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