5 Loan Portfolio Metrics your Analysts are Missing by not Combining Your Data in One System
October 2016
“Our lives are defined by opportunities–even the ones we miss,” Brad Pitt pronounces in The Curious Case of Benjamin Button, the 2008 movie in which he nails the peculiar lead role.
There’s much truth in that statement, whether you place the focus on your personal or professional life.
That truism explains why we know Ringo Starr, not the deposed Pete Best, as The Beatles’ drummer. Why the Chicago Bulls won six NBA titles with Michael Jordan, while the Portland Trail Blazers — who passed on him in the college draft — are still looking for their first crown since 1977. And why Harrison Ford made millions as the whip-snapping, Nazi-foiling role of Indiana Jones instead of first choice Tom Selleck, who was contractually bound to his hit TV show, Magnum P.I.
In an example more relevant to your daily existence, is your credit union taking full advantage of best-in-class data analysis infrastructure and processes?
Here are five areas where integrating your data streams into a single system, rather than flowing data through various platforms, could spell the difference between becoming a highly efficient, growth-oriented credit union, or one that muddles along in a comfortable but not strategically advantageous position.
1. Missed cross-sell opportunities. Whether you put your trust in old adages or hard numbers, the message is the same: One in the hand is worth two in the bush. According to Marketing Metrics, the probability of selling to an existing customer is no less than 60%, while the probability of selling to a new prospect is no better than 20%. 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. Overlooking data integrity issues. Ensuring that your credit union inputs, updates, and deletes data in deliberate, automated fashion is an absolute necessity. Doubts about the validity of your data undermines your entire investment in collecting, maintaining, and analyzing databases. Dividing your data between various channels means there is no foolproof way to cross-check and dissect the data for errors or omissions.
3. Not understanding lifetime value and risk of the member. All members might be created equal — but they certainly don’t behave equally. In an era of razor-thin margins, mounting regulation, and rapidly increasing cybersecurity concerns and expenses, maintaining nonproductive relationships can be an anchor on a credit union’s ledger. While the answer in some cases is to sever that tie, the successful long game revolves around “coaching” people to become better members.
Not everyone starts in the same spot. Not everyone ends in the same spot. But certainly, patterns exist, and over time you can identify the various tracks toward member productivity. To assure that you usher members from their Point A to a Point B that represents success for both the individual and the credit union, you must develop a system that closely monitors their behavior and prompts both cross-selling opportunities and interventions. That system requires not simply access to accurate, robust data, but a commonality of that data conducive to synchronized, continual analytic assessments.
4. The power of derivative metrics. Instinct shouldn’t be discounted as a tool in gauging whether or not to grant a loan (or set the terms of that loan), or to determine precisely the right point in a conversation to invite a member to explore one of your credit union’s products and services. But it’d also be foolish to rely on instinct as the basis of your lending strategy. 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. Consider that total borrowing often has an inverse relationship with default risk; in many cases, those with the smallest loan amounts default more often than those with high loan amounts. And these correlations 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.
5. Comparative internal metrics. Over the past few years, as consumers have flooded toward digital banking options and margins have shrunk, credit unions have focused much attention on quantifying profitability at all levels of the organization. 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 foregoing the comparative analysis that sheds light on these answers, or forming incomplete or incorrect conclusions.
Don’t let opportunities slip through your credit union’s collective fingers. Invest in a data solution that makes you a collection of Starrs — er, stars.
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