Starting with “small data”: Predictive analytics for Small Banks and Credit Unions

Today’s world of big data and predictive analytics can prove intimidating to a small bank or credit union. We don’t need statistics analyses to know that small banks and credit unions are traditional laggards with new technology adoption. Smaller banks typically have very little in-house capability, expertise, resources and more importantly, the will to dabble in esoteric technologies. Rightly so, since they need to use their limited resources to focus on their core business i.e. revenue generation, risk management, and regulatory compliance.

In our post-financial crisis world, banking has grown in both complexity and pace. Smaller banks are faced with competition from not only the traditional large corporations, but also online banks, P2P networks and every flavor in between.

Predictive analytics is the art and science of understanding the nature of the environment based on the past, and using the garnered insights to provide a forecast of future likely events. The big banks and online banks are using these capabilities with increasing efficacy.

Often big data and predictive analytics are used in the same context and erroneously tied together. With this perspective, we are inclined to think that Big Data = Big $$$$$. In fact, big data has little to do with predictive analytics. Big or small, data is simply the enabler. Predictive analytics may well be used with “small data”. In some contexts it is actually more advantageous to use small data for analysis.

For smaller banks and credit unions that use a core service provider, a data warehouse solution is a relatively low-cost investment. This infrastructure can be bought or leased, installed, setup and maintained very inexpensively. You now have the ability to archive your data for predictive analytics, among other things.

Learn more - find the costs to data warehousing

The value of predictive analytics can be realized quickly and the results can be put to business use within a few weeks. For example, in a recent study of a consumer loan portfolio for a client, we were able to collect and analyze the transactional history of 5000 loan accounts for a small lender and generate a list of 52 loans that had a high potential of delinquency. The client was able to use this information to monitor the loans closely, communicate with the customers proactively and create a win-win for the customers and the bank. The cost of the solution was marginal compared to downstream collection costs, potential charge-off costs of a few hundred thousand dollars, and loss of customer loyalty, to name a few.

In summary, with the right partner, predictive analytics can yield solid, actionable results with a short turnaround time, relatively little effort and low cost. Small banks and credit unions can leverage their data with few resources and little investment to maximize profits, lower risk and ensure regulatory compliance. In short, we don’t need BIG DATA for BIG RESULTS.

We believe in starting small and showing value in meaningful baby steps in which results are immediately actionable and relevant. Here are some ideas that could work for you.

  1. Identify problem loans for a class and type of loans
  2. Predict when an individual customer is likely to leave
  3. Target your product marketing. Find the next product that your existing customer is likely to buy, by product or by customer
  4. Create custom loan offerings based on the individual customer attributes
  5. Optimize your loan portfolio. Balance your product mix based on predicted outcomes

These ideas present several roll-up opportunities that are self-evident. For example identifying problem loans helps manage default risk and ensure regulatory compliance. Optimizing your loan portfolio offers a similar advantage. When used in combination the results can be used for more accurate allowances for loan losses (ALLL).

The future of banking relies on the smart use of data. The CECL guidelines expected in Q1’2016 require lending institutions to predict the outcome of loans over the entire term. The widespread adoption of advanced analytics is a certainty. For small banks and credit unions it is more than a question of keeping up with the Joneses, it is a question of survival of the fittest.

In her article “Bank CEO’s Fear the data-driven decision”, Penny Crosman provides us some good insights into some of the challenges that banks face with the adoption of predictive analytics. Though the article is a year old, most of the issues are still relevant. A testimony to the pace of adoption.

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