Just as we’ve seen management reporting move from main frames to laptops over the last few decades, gaining flexibility and insight along the way, tools for managing your portfolio are getting better, faster and cheaper.

To help illustrate the issues at hand, let’s first take an historical look at another industry.

During the early boom years at Ford, the story goes, clerks would weigh piles of invoices as a means of estimating how much they owed to their various suppliers. They knew about how many dollars per pound a supplier’s typical invoices contained, so they made a few simplifying assumptions in the name of practicality and efficiency, and ran with it.

This shortcut makes a certain amount of sense — Henry Ford legendarily hated accountants and hired far too few of them to do the dreary job of adding up all the invoices one by one. The estimate was probably tolerably close to the real numbers. And, among Ford’s suppliers, who was willing to walk away from an account like Ford over a little billing eccentricity?

So there really weren’t too many serious drawbacks to Henry Ford’s AP system, with the possible exception of giving auditors heart attacks. He saved a ton of money on accountants, the suppliers got paid — sometimes more, sometimes less — and Ford Motor Company continued to churn out both cars and profits. Why fix it if it isn’t broke, after all?

However, the story goes on: Once grandson Henry Ford II took over the company (and got the increasingly erratic Henry I out of the way), one of his first acts was to hire accountants … a lot of accountants. For Ford had started losing money hand over fist, and Henry II desperately needed to know why, where and how. By hiring smart people to change the way he did business, he was able to keep Ford in business and return the company to profitability.

We in the equipment finance industry are of course far too smart to do anything so crazy as estimate costs by weighing piles of paper, right? Even if we’re not that smart, the auditors would have our scalps. So we carefully count everything, issue reports and statements, and, in general, use best practices to understand and manage our businesses.

Yet, lurking behind the numbers are processes and practices not too much more sophisticated than weighing stacks of paper. Just as with Ford, the results are close and, in boom times, close is good enough. Problems arise because not all times are boom times. Indeed, lean times strongly tend to winnow out players who rely on “good enough” processes to get by.

Consider the following examples: The stacks of invoices in the first case are the thousands of recently completed transactions coming off a lease portfolio. Each of those deals embodies a set of concrete, real-world outcomes — yields, costs, realized residual, perhaps a default or renewal or buyout, payment history and so on. Each of these outcomes correlates to a greater or lesser degree to a wide range of facts in the deal — credit rating, physical asset, physical address, industry, term of deal and on and on. Now, we can ignore the details and the correlations and just “weigh the invoices.” We come up with gross numbers to represent the outcomes, and apply these gross numbers to our forecasts — as portfolio-level yield expectations, default assumptions, attrition curves, loss reserves, etc. Then, we use those projections to determine our funding needs and to forecast profits.

A second example, say you’re offered a portfolio to buy. If it’s a small number of high-value assets, you’ll probably evaluate each transaction individually, then make a few tax-effect assumptions, apply some fudge factors to yield expectations, add a little extra margin and come up with a bid. If it’s a large number of relatively low value assets, you’ll probably do some spot checking, fudge a little harder, add a little more margin and make a bid. In each case, this process contains a lot of invoice weighing — a huge amount of pertinent data is in the transactions, but the amount of work needed to turn that data into useable — bidable — information is just too great. Besides, it’s what everybody else is doing, so it’s not like you’re likely to lose a desirable deal because of method.

Just as at Ford in the old days, this rough and ready method works great — in a reasonably consistent and growing economy. If things don’t change much, and there is enough business to go around, using gross portfolio-level assumptions will provide profits enough to cover reasonable expenses and margins enough on a portfolio level to smooth over any lumpiness in the details.

It also helps a lot if your competition is also using the same “weigh the invoices” method. If, on the other hand, your competition is suddenly able to slice and dice their data in ways that produce actionable information that makes them more competitive, you could be in trouble. For example, viewing current attrition behavior at a finance program level that could lead to a decision to pull out of or double down in a market, or testing residual sensitivity on a product code basis, which might lead to a more competitive bid on a portfolio purchase. Or even just being able to see, at a glance, how changes in accounting rules effect income recognition on your current and projected portfolio, and to “what if” your assumptions without having IT write specialized queries.

The problem with getting useful portfolio analysis has been two-fold: first, the “horsepower” needed to process 100,000 or more transactions at a time was generally only available on the mainframes within the confines of the IT department. Second, the analytic tools available were “flexible,” meaning “not designed specifically with the equipment finance industry’s needs in mind.” If you wanted something specific — say, the effect of realized residuals on after-tax portfolio yield — you were either out of luck, or were going to need to write your own software to do the math. The result was, and still is today, that, if you want data on transaction yield by asset type, you have to have IT write a report for you, including the math for whatever yield you mean. Then, you’d need to schedule a run of that report, which would show up on your desk or desktop PC.

Looking at the report, you might have a question, such as why one segment is performing differently from another similar segment. Maybe you’d like to change an assumption. It’s back to IT for another report… and so on. The effect of this tedious, inflexible system has been two-fold: management has spent a fortune developing an ever-growing set of more-or-less inflexible reports and tools, and managers have developed work-arounds — we weigh the invoices.

Today, a readily available and inexpensive high-end laptop has the power to analyze tens of thousands of leases at a time, running software built specifically for the analytics needed in the equipment finance industry. Not only can this new technology perform much of the analysis generated today — faster and more flexibly — but it represents a technologic leap-frog: an analyst can perform ad-hoc what-if analysis on thousands of transactions in seconds, instantly see the cash, tax and accounting ramifications, and generate reports. The analyst can sort the data by any characteristic — financial product, asset type, credit, geography, term and so on — 
and calculate sophisticated yields, such as an after-tax book ROE or RAROC, on each segment. Changes made anywhere can be immediately reflected in cash flows, tax calculations and pro-forma accounting statements.

Technology has opened the door to much more sophisticated statistical analysis as well. For example, in order to comply with the proposed accounting changes, an analyst could calculate the average term of a class of transactions with renewal and buyout options from an historic portfolio, or could generate attrition curves and probabilistic distributions by finance program or asset type right on his or her PC. Such analysis has tended to be a major research project. In the future, it will be as routine and updating a spreadsheet.

It bears noting that this analytics revolution is taking place in parallel with the move toward data warehouses, but is distinct from them. Having the data nicely aggregated in one place is very important, but does not, in and of itself, give you any insight into what’s going on in your portfolio. Equipment finance firms need both to have the data available, and to have tools that allow them to view that data in meaningful ways.

Moving to more advanced analysis is not a matter of if, but of when. Today, we have the option to use current technology to gain insight into our portfolios, and to gain competitive advantage in our industry.

Joseph Moore is the director of Sales & Marketing at Ivory Consulting Corporation. Over the last 12 years, Moore has worked with many ELFA-member companies, trained hundreds of equipment finance professionals in the intricacies of equipment finance pricing and given a number of presentations at ELFA events. Moore holds an M.B.A. in International Business and Finance.