What Banks Should Consider When Adopting Analytics

| November 2, 2019


By Tim VanTassel  There’s an iconic moment in Jurassic Park when discussing the value of creating genetically engineered dinosaurs, Jeff Goldblum’s skeptical Dr. Ian Malcolm says: “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”

I think with a slight tweak, today’s bank executives would be well advised to heed Dr. Malcolm’s advice: “Your data scientists are so preoccupied with whether or not they can predict something, they don’t stop to consider what will happen if they are successful.”

According to research from The Digital Banking Report, the “use of Big Data, AI, Advanced Analytics, Cognitive Computing” are considered to be the hot trends in banking in 2019 – and with good reason! Every part of the business, from marketing and customer retention to collections and compliance, can theoretically benefit from applying data science tools and techniques to the modern bank’s ever-expanding repository of customer and activity data.

And yet, whenever I ask executives what their business’s actually plan to do with predictive analytics, the answers are often frustratingly vague.

Phrased another way, banks focus so much on how to predict something (or improve an existing prediction’s accuracy), they forget to think about how their operations – and people – will need to adapt if they succeed. I saw this vividly illustrated during the Great Recession, when the analytics question du jour was “Can we predict strategic defaults?” It turned out that with the right data, you could – and with a startling degree of accuracy.

The problem was, most lenders found that adopting a strategic default prediction model didn’t do them any good. They could identify a specific borrower, living on a block of homes that were all underwater, choosing to default on their mortgage in order to keep paying other bills such as credit cards or auto loans. However, they couldn’t offer that borrower a restructured payment plan without triggering additional strategic defaults across the rest of the block.

Simply put, these strategic default models were a waste of time and money, not because they were inaccurate, but because the parties involved failed to anticipate how offering one customer an attractive loan restructure would influence other customers who hadn’t yet defaulted. The analytics didn’t fail. The implementation strategy did.

How to Get It Right

Now that I’ve told you my cautionary tale, let’s explain how to get analytics right. Here are three examples of banks succeeding with analytics – in each case, by aligning their goals with operational realities.

  1. Anti-money laundering (AML). Today, the vast majority of suspicious activity reports (SARs) are generated through manual transaction monitoring with scenario-based rules – and more than 90% of them could be generated automatically, without any human assistance, through the use of machine learning and predictive analytics. Paired with a sound implementation strategy that both executives and regulators are comfortable with, this advance in AML detection can not only capture new money laundering events missed by current transaction monitoring, but free existing compliance officers to focus on higher-value alerts.
  2. Collections. Banks can use advanced analytics to divide delinquent borrowers into multiple – and extremely specific – segments. And by identifying certain segments as accounts likely to resolve on their own or with an automated reminder, they can improve their operational efficiency by not assigning those accounts to human collection agents. Again, a sound implementation strategy is critical. As with their AML counterparts, human collection agents can be reassigned to where their empathy and communication skills will make the largest difference.
  3. Attrition. Every telecommunications provider struggles with customer attrition – which is why one of our clients responded by creating the Attrition Intervention Group, a “tiger team” of customer service specialists who are remarkably effective at convincing customers not to leave. The team’s secret? Analytics, which they use to focus on the right customers. In this case an investment in more accurate attrition prediction models paid off handsomely because not only did our client improve their ability to predict which customers could be convinced, they assigned their best specialists to them, ensuring those predictions were converted into “saves” at an impressively high rate.

Learn from Your Mistakes

I would, of course, be remiss if I didn’t acknowledge that I have been as guilty as anyone of lionizing the role of analytics in business transformation, which is also why I strive to emphasize that analytics are a tool. An extremely powerful tool, but like any tool its effectiveness depends a great deal on how it’s used – and it’s used most effectively by a wielder with an end goal in mind.

Financial executives deciding whether and how to invest in analytics would be especially well served to remember this.

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