We've recently discussed management of risk, collections strategy, credit attributes, and the like for the bank card, telco, and real estate markets. This blog will provide insights into the trends of the automotive finance market as of third quarter 2009.  In terms of credit quality, the market has been relatively steady in year-over-year comparisons.  The subprime group saw the biggest change in risk distribution from 3Q08, with a -3.74 percent shift. Overall, balances have declined to just over $673 billion (- 4 percent).  In 3Q09, banks held the largest total of outstanding automotive balances of $241 billion (with captive auto next at $203 billion).  Credit unions had the largest increase from 3Q08 (with $5 billion) and the finance/other group had the largest decrease in balances (- $23 billion). How are automotive loans performing?  Total 30- and 60-day delinquencies are still on the rise, but the rate of increase of 30-day delinquencies appears to be slowing.   New originations are dominating in the Prime plus market (66 percent), up by 10 percent.  Lending criteria has tightened and, as a result, we see scores on both new and used vehicles continue to increase.  For new buyers, over 83 percent are Prime plus.  For used buyers, over 53 percent are Prime plus.  The average credit score changed from 762 in 3Q08 to 775 in 3Q09 — up 13 points for new vehicles.  For used vehicles in the same time period: 670 to 684, up 14 points. Lastly, let’s take a look at how financing has changed from 3Q08 to 3Q09.  The financed amounts and monthly payments have dropped year-over-year as well as the average term and average rate. Source:  State of the Automotive Finance Market, Third Quarter 2009 by Melinda Zabritski, director of Automotive Credit at Experian and Experian-Oliver Wyman Market Intelligence Reports    

By: Tom Hannagan Apparently my last post on the role of risk management in the pricing of deposit services hit some nerve ends. That’s good. The industry needs its “nerve ends” tweaked after the dearth of effective risk management that contributed to the financial malaise of the last couple of years. Banks, or any business, can prosper by simply following their competitors’ marketing strategies and meeting or slightly undercutting their prices. The actions of competitors are an important piece of intelligence to consider, but not necessarily optimal for your bank to copy. One question is regarding the “how-to” behind risk-based pricing (RBP) of deposits. The answer has four parts. Let’s see. First, because of the importance and size of the deposit business (yes, it’s a line of business) as a funding source, one needs to isolate the interest rate risk. This is done by transfer pricing, or in a sense, crediting the deposit balances for their marginal value as an offset to borrowing funds. This transfer price has nothing to do with the earnings credit rate used in account analysis – that is a merchandising issue used to generate fee income. Fees, resulting from account analysis, when not waived, affect the profitability of deposit services, but are not a risk element. Two things are critical to the transfer of funding credit: 1) the assumptions regarding the duration, or reliability of the deposit balances and 2) the rate curve used to match the duration. Different types of deposit behave differently based on changes in rates paid. Checking account deposit funds tend to be very loyal or “sticky” – they don’t move around a lot (or easily) because of rate paid, if any. At the other extreme, time deposits tend to be very rate-sensitive and can move (in or out) for small incremental gains. Savings, money market and NOW accounts are in-between. Since deposits are an offset (ultimately) to marginal borrowing, just as loans might (ultimately) require marginal borrowing, we recommend using the same rate curve for both asset and liability transfer pricing. The money is the same thing on both sides of the balance sheet and the rate curve used to fund a loan or credit a deposit should be the same. We believe this will help, greatly, to isolate IRR. It is also seems more fair when explaining the concept to line management. Secondly, although there is essentially no credit risk associated with deposits, there is operational risk. Deposit make up most of the liability side of the balance sheet and therefore the lion’s share of institutional funding. Deposits are also a major source of operational expense. The mitigated operational risks such as physical security, backup processing arrangements, various kinds of insurance and catastrophe plans, are normal expenses of doing business and included in a bank’s financial statements. The costs need to be broken down by deposit category to get a picture of the risk-adjusted operating expenses. The third major consideration for analyzing risk-adjusted deposit profitability is its revenue contribution. Deposit-related fee income can be a very significant number and needs to be allocated to particular deposit category that generates this income. This is an important aspect of the return, along with the risk-adjusted funding value of the balances. It will vary substantially for various deposit types. Time deposits have essentially zero fee income, whereas checking accounts can produce significant revenues. The fourth major consideration is capital. There are unexpected losses associated with deposits that must be covered by risk-based capital – or equity. The unexpected losses include: unmitigated operational risks, any error in transfer pricing the market risk, and business or strategic risk. Although the unexpected losses associated with deposit products are substantially less than found in the lending products, they needs to be taken into account to have a fully risk-adjusted view. It is also necessary to be able to compare the risk-adjusted profit and profitability of such diverse services as found within banking. Enterprise risk management needs to consider all of the lines of business, and all of the products of the organization, on a risk-adjusted performance basis. Otherwise it is impossible to decide on the allocation of resources, including precious capital. Without this risk management view of deposits (just as with loans) it is impossible to price the services in a completely knowledgeable fashion. Good entity governance, asset and liability posturing, and competent line of business management, all require more and better risk-based profit considerations to be an important part of the intelligence used to optimally price deposits.      

Meat and potatoes Data are the meat and potatoes of fraud detection.  You can have the brightest and most capable statistical modeling team in the world.  But if they have crappy data, they will build crappy models.  Fraud prevention models, predictive scores, and decisioning strategies in general are only as good as the data upon which they are built. How do you measure data performance? If a key part of my fraud risk strategy deals with the ability to match a name with an address, for example, then I am going to be interested in overall coverage and match rate statistics.  I will want to know basic metrics like how many records I have in my database with name and address populated.  And how many addresses do I typically have for consumers?  Just one, or many?  I will want to know how often, on average, we are able to match a name with an address.  It doesn’t do much good to tell you your name and address don’t match when, in reality, they do. With any fraud product, I will definitely want to know how often we can locate the consumer in the first place.  If you send me a name, address, and social security number, what is the likelihood that I will be able to find that particular consumer in my database?  This process of finding a consumer based on certain input data (such as name and address) is called pinning.  If you have incomplete or stale data, your pin rate will undoubtedly suffer.  And my fraud tool isn’t much good if I don’t recognize many of the people you are sending me. Data need to be fresh.  Old and out-of-date information will hurt your strategies, often punishing good consumers.  Let’s say I moved one year ago, but your address data are two-years old, what are the chances that you are going to be able to match my name and address?  Stale data are yucky. Quality Data = WIN It is all too easy to focus on the more sexy aspects of fraud detection (such as predictive scoring, out of wallet questions, red flag rules, etc.) while ignoring the foundation upon which all of these strategies are built.