Through in-depth predicative analytics, utilizing advanced statistics, the website spurious correlations, has determined the U.S. per capita consumption of sour cream has a 99 percent correlation to motorcycle riders killed over that same time period. The strategic initiative is clear, lower our consumption of sour cream and it will be safer for our neighbor to take out his Harley for a joy ride. The data is accurate and the model has been built by a Harvard PhD, but where is the common sense?
This admittedly ridiculous analysis highlights one of the many challenges with banking analytics platforms and big data initiatives. The statistical methods are readily available and the data is growing exponentially by the day but the PhDs typically don’t know what business questions to ask, while bankers often have difficulty interpreting the results or getting their arms around the possibilities.
“We are encouraging bankers to commit to the data gathering process today in order to reap huge benefits in the future”
Identify the Business Issues First
The objective of analytics initiatives is to reveal information (value) from vast, obscure, complex and diverse data sets. The industry spends millions of dollars to implement and maintain enterprise-wide systems, seeking everything from helping the front lines with cross-selling product opportunities to cataloguing customer engagement. From risk management processes to huge marketing breakthroughs, the initiatives appear to “promise the world.” In truth, there is huge potential value in data, but most banks unfortunately do not have the necessary resources to embark on such a broad and encompassing journey.
But How Much Data Do We Really Need?
Customer relationships are the primary differentiator for any community bank. Bankers know their community and their customers, and are thus able to provide value and service far beyond any technology platform. But strong relationships, while essential, should not eliminate or even mitigate the need for data. The best managers in any industry know how to transform the right data into invaluable information—and that information, in turn, into strategic initiatives.
Customer Behavior Revealed Through Data
The primary source of bank earnings is net interest income: the delta between the cost of funds (mainly deposits) and the yield on assets (predominantly loans). Both loans and deposits carry uncertainty, driven by customer behavior patterns. This “behavior” creates risks, such as interest rate, liquidity and credit. Understanding this uncertainty at a more granular level, through data, can provide a competitive advantage. This type of analysis is the primary focus of many successful “common sense” analytics initiatives.
For example, a loan customer can prepay a loan at any time (with or without prepayment penalties), potentially unmasking earnings, vulnerabilities and impacting operating liquidity. Banks that collect the essential data needed to better understand specific customers, such as candidates at risk for refinancing, can stay ahead of the competition and plan accordingly.
Another example would be the customer optionality imbedded in a non-maturity deposit. Of course, your customers can remove their non-maturity deposits at any time (as market conditions or other variables change), invariably impacting liquidity and potential interest rate risks. Understanding customers who are more vulnerable to withdrawals or rate changes—and comprehending the psychology behind these customers’ reasoning (why and how they typically withdraw)—can pay huge dividends in any growth or strategic plan.
A Spectrum of Customers: What Do You Know?
Betty-Ann: 55-year-old Betty-Ann has multiple relationships with the bank. She has a checking account that she opened nine years ago with an average balance of $12,000. She has $150,000 in a three-year CD and a Premium Money Market deposit of $68,000. She financed her house through this bank and has seven years remaining on her mortgage. Betty-Ann is a core customer of the bank.
Frank: 37-year-old Frank has one account with this same bank, a $90,000 Premium Money Market deposit. He opened the account in December 2009, in the midst of the financial crisis. Although the account is currently paying 0.40 percent, it initially was paying 2.00 percent. Frank is at a flight risk.
When interest rates move or market dynamics change, Betty-Ann and Frank will react very differently. In fact, the entire spectrum of your unique customer base—from businesses and municipalities, to retail segments and individual customers—will all have distinct and different behavior patterns. Does your balance sheet, marketing and sales strategy reflect this spectrum and these reactions? Do your interest rate risk and liquidity risk models capture this complex landscape?
In today’s ultra-competitive banking environment, what opportunities would become available if you could delineate and segment your customer base?
Transforming Information into Strategy
Once data is transformed into information, management can discuss and implement strategies accordingly. For example, we have seen lenders leverage historical prepayment data and win more deals through targeted marketing. By isolating deals won and lost, lenders are able to stay ahead of the market and their competitive forces.
We have also seen banks leverage deposit observations and subsequently implement deposit gathering strategies through cross-selling and improved retention. For example, many have been successful at selectively introducing new deposit specials requiring multiple relationships for premium pricing. With the introduction of the Liquidity Coverage Ratio (LCR), multiple relationship retail accounts are more valuable today than ever before. These analytics are becoming pivotal in the quest to improve net interest margins and combat large banks that must comply with the LCR.
Gather the Data
Do you have the data sets to identify these types of trends within your institution? Regrettably, many banks are unable to gather basic fields needed for this type of analysis. For this very reason, we are encouraging bankers to commit to the data gathering process today in order to reap huge benefits in the future. While resources are understandably tight, it is an investment that must not be underestimated. Many institutions are being proactive and creative, not only hiring Chief Data Officers but also having interns manually key the missing data into the core processing systems—uncovering some of the most valuable information available.
Last But Not Least: Implementation
Banks of all sizes are learning that there is an intersection point between data, analytics and implementing effective strategic plans. Data analysis does not have to be highly complex or expensive. In fact, oftentimes the complex analysis results in strategies focused on “sour cream minimization techniques.” But, once you have gathered the data, there are cost-effective tools that can help you quickly and easily identify trends, outliers and opportunities. Fusing data analysis and customer knowledge with common sense, will inevitably lead to higher levels of net interest income and a more effective risk management process.