An Argument for Profitability Analysis
Author: Tom Parsons
I spent considerable time in the 90’s implementing profitability systems. The purpose was to build a P&L for each measurable unit in a financial institution. Typically, these units were branches and products. The base data used in the analysis is customer accounts; individual loans and deposits. As such, if other variables were attached to the account records, the accounts could be rolled-up into P&Ls for customer relationships, officers, geographic locations, anything that was important to be measured.
At its core, a profitability system needs Funds Transfer Pricing (FTP). There is a second element, non-interest items (income and expense), but bankers live by the margin so this post will address margin measurement.
Funds Transfer Pricing is the banker’s Robin Hood: it takes from the lenders and gives to the deposit gatherers in an attempt to figure out where value is being created in the bank. It gives credit where credit is due.
The premise is if all you have is the bank-level P&L, all you have is the big picture – no details. Sitting in the middle is the treasury department, deciding how much to take from the loan income and how much to give to the deposit gatherer for finding cheap money. Typically, there is a mismatch or gap and the difference ends up in the treasury. That number is ideally small as it makes no sense for the treasury department to be a profit center (we can debate that separately).
If you picture a classic yield curve (let’s say the treasury) with low short term rates and higher long term rates, the loan rate should be above the risk-free yield curve because the bank has taken on risk (credit, liquidity, etc.) If the loan rate were below the curve, the bank would be better off buying a treasury note. Deposits are the opposite. They’ve borrowed below the risk free rate. Had they paid more than the treasury, it’s likely they would have been better off borrowing, assuming they had access. The amount kept by the funding center (treasury), the gap, represents the income earned by taking on interest rate risk.
Now let’s take a look with some numbers. Picture a simple situation where the loans are earning 5% and deposits are paying .40%. The net real bank spread is 4.60%. But how much of that 4.60% was really contributed by the lenders? We are going to charge them for use of the funds using one or more of a variety of methods. There are numerous methods to get this rate but let’s assume we charge them 1.80% (the current 10 year treasury). Their net spread is 3.20% (5% - 1.80%). Then we have to credit the deposits for bringing in dollars to lend out; assume we credit them 1.70% (the 2 year rate). Their net interest income is 1.30% (1.70% - .40%). So the lenders contribute 3.20% and the deposits 1.30%, but that adds up to 4.50% - where did the other 10 basis points go? To the treasury manager (funding center). This .10%, if viewed in an IRR system, would represent the income generated by taking on mismatch risk, assuming the transfer rates to the units are the risk free (treasury) curve. Why did loans and deposits get different rates? Because the rate was picked off of a sloping yield curve and the term of the deposits is less than that of the loans.
This only works if the FTP is somehow assigned using fair rules and real market rates. Otherwise, you can game the system to make branches, products, and other measurable business units look as profitable or unprofitable as you like. This leads us to the next discussion – methods for choosing the FTP rate – How should the FTP rate be assigned? Well, in a number of ways from the simple to the complex and that merits its own blog post.
While this post talks about both sides of the balance sheet, many bankers find value in data analytics for the deposit side on its own. If you can measure the contributions of an individual deposit account, roll that into a customer, then segment customers on profitability, you now have target segments in your own book of business. Consider what that means: without adding a single new customer and the expense that comes with it, knowing who is least profitable or even break-even, allows you to target them for improvement leading to increased bank profitability and, ultimately, shareholder value.
More information on Deposit Profitability Analytics can be found here
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September 13, 2019