The world of predictive analytics is changing quickly—both the technology itself, as well as the practical and ethical issues surrounding it. No industry is likely to be more affected by these shifts than banking, where predictive modeling is so deeply entrenched.
I recently offered 5 predictions for what will be shaping predictive analytics in 2013 across all industries. Many of these very same trends will have an especially strong impact in banking next year—with a few unique twists. Here’s what rises to my top 5 trends in banking:
1. Customers Take Center Stage—Many of us have had a credit card transaction declined at some point, often for no clear reason. That’s an example of an embarrassing and inconvenient customer experience that is frequently avoidable. Analytics gives banks the ability to use a scalpel more often than a hammer—in other words, to understand the context of a situation and act in a more personalized manner. Banks will utilize that scalpel more in 2013 as they search for ways to build relationships by offering more convenient and tailored services, and attempting to avoid driving away customers who don’t feel valued.
2. Analytics as Referee in the Capital Management/Profitability Fight—For more than four years now, there has been a tension in many banks between the need for responsible capital management and the fundamental need for banks (and every other business) to make money. In 2013, predictive analytics will play an increasingly important role in helping banks find the right balance—a topic my colleague David Molyneaux frequently blogs about here.
3. Privacy and Discrimination Become Hotter Button Issues—This is certainly not a new trend in banking. Still, the more data we collect, the more consumer groups and regulators will take an active interest—and thus, the more banks must work to stay ahead of this. It includes maintaining strong consumer-focused privacy policies, as well as ensuring your offers (e.g., credit card reward programs) aren’t kept out of reach of certain demographic groups. Banks must make sure such disparate impacts are not an unintentional byproduct of analytic modeling.
4. Realization that Humans Have a Big Role to Play—Those of you who follow this blog regularly have heard me say this before: in predictive modeling, people matter. It’s the analytic scientist who needs to frame questions properly, identify the relevant data and interpret findings; without that, your results may be useless. In banking, we need such human expertise, for instance, to mitigate the dueling priorities between risk management and profitability that I mention above. To solve this and other pressing challenges, finding the best analytic talent will be critical in 2013.
5. Talent Shortage—With demand for analytic talent growing, experts expect a shortage of 100,000-200,000 analytics positions in the U.S. alone within five years. I think the problem will be particularly acute in the banking sector, and it will be a global shortage. New regulations are forcing banks to modify business practices, restless investors are demanding a higher return on capital, and an unsettled economic picture is making it difficult to anticipate future market conditions. The need for analytic modeling in banking has never been greater.