Financial Services
By: Tom Hannagan Part 6 Peer Group 2 fee income Non-interest income again, as a percent of average total assets, declined to .86 percent from .95 percent in 2007. For Peer Group 2 (PG2), fees have also been steadily declining relative to asset size, down from 1.04 percent of assets in 2005. A smaller, non-interest bearing deposit base with no other new and offsetting sources of fee income will lead to increased pressure on this metric. Operating expenses Operating expenses also put more pressure on earnings on these smaller banks. They increased from 2.79 percent to 2.83 percent of average assets. That’s four basis points on the negative. Historically, this metric has been flattering for this size bank and usually moves up or down from year-to-year. It was almost equal at 2.82 percent of assets in 2004. As a result of the sizeable decline in margins, the continued decline in fee income and the slight increase in operating expenses PG2’s efficiency ratio lost ground from 59.52 percent in 2007 to only 64.72 percent in 2008. That means that every dollar in gross revenue cost them almost 65 cents in administrative expenses this year. This metric averaged 56 cents in 2005/2006. It’s amazing how close these numbers are for banks of very different size where you would expect clear economies of scale. The total impact of margin performance, fee income and operating expenses, plus the huge increase in provision expense of 59 basis points leads us to a total decline in pre-tax operating income of .96 percent on total assets. That is a total decline from 1.58 percent pre-tax ROA in 2007 to .64 percent pre-tax ROA, a loss of 61 percent from the pre-tax performance in 2007. My same conclusion as above would hold regarding the pricing of risk into bank lending (although the smaller banks didn’t perform a badly as the larger in this regard). Although all 490 banks are declining in all profit metrics, the smaller banks seem to have an edge in pricing loans, but not deposits. Although up dramatically in 2007, and even more in 2008 for both groups, the PG2 banks seem to be suffering fewer credit losses relative to their asset size than their larger brethren. Both groups have resulting huge profit declines, but the largest banks are under the most pressure through this period. An interesting point, with higher loan yields and fewer apparent losses, is whether PG2 banks are somewhat better at risk-based pricing (for whatever reason) than the largest bank group. Results are results. The 2009 numbers aren’t expected to show a lot of improvement as the general economy continues to slow and credit and financial risk management issues continue. We’ll probably comment on 2009 as the quarterlies become available this year.
For all you folks who, like me, waited until the last minute to knock out a term paper or class project in school, here is a friendly reminder…Yes, the Federal Trade Commission (FTC) pushed out the enforcement deadline of the Red Flags Rule to May 1, 2009. Yes, a sigh of relief was heard across compliance officers and operations managers nationwide. However, you should still keep a few things in mind as we approach May 1. First, per the FTC, "many entities also noted that because they generally are not required to comply with FTC rules in other contexts, they had not followed or even been aware of the rulemaking, and therefore learned of the requirements of the rule too late to be able to come into compliance by November 1, 2008." Those of you, who have not been subject to FTC enforcement in the past are quite possibly still subject to the Red Flags Rule based on your institution maintaining 'covered accounts' per the definition in the Red Flags Rule itself. Double check if you think otherwise. Second, the FTC was clear in stating that "this delay in enforcement is limited to the Identity Theft Red Flags Rule (16 CFR 681.2), and does not extend to the rule regarding address discrepancies applicable to users of consumer reports (16 CFR 681.1), or to the rule regarding changes of address applicable to card issuers (16 CFR 681.3)." So, while May 1 is still a few weeks away, if you are accessing consumer credit reports, for example, you should already have a formal written and operational process to detect and respond to address discrepancies on those credit reports.
Red Flags Rule I've heard more than one institution claim that they may limit and even reduce the identity elements (perhaps down to just name and address) that are captured during consumer applications or other transactions. Their rationale is that the fewer identity elements they request or require during these processes, the less information they will need to authenticate as part of their Red Flags Identity Theft Prevention Program. While this argument seems logical on the surface, I would suggest that if securely gathered/stored and appropriate to the nature of your business, additional data elements such as Social Security Number (SSN), date of birth and phone number can actually allow you to accomplish a few things to your benefit. 1. Analysis of our consumer authentication products shows that contributing SSN, date of birth, and phone (in addition to name and address) to an authentication process, will actually improve your ability to positively authenticate a consumer via an overall risk-based strategy. 2. The use of additional data elements, such as the phone number, can unlock additional data sources for use in verifying not only that phone number, but the inquiry name and address as well. 3. Just because you don't capture certain identity elements, doesn't mean the risk goes away. In providing additional identity elements for authentication, you can gain a more holistic view of a consumer - be that good, bad or ugly. It’s better to figure this out up front versus down the road when bills go unpaid and the bad guys scatter.
By: Prince Varma Hello. My name is Prince Varma and I’ve spent the better part of the last 16 years helping financial institutions (FI) successfully improve their in business development, portfolio growth and client relationship management practices. So, since the focus of this blog is to speak to readers about risk management, many of you are probably wondering what a “sales and business development” guy is doing writing a piece related to mitigating and managing risk? Great question! The simple fact is that the traditional or prevailing sentiment or definition related to risk management – mitigating credit risk -- is incomplete. A more accurate and comprehensive approach would be to recognize, acknowledge and address that “risk” cuts across the entire client relationship spectrum of: client penetration/growth; client retention; and client credit risk mitigation. How do penetration and retention count as “risk factors”? (this is where the sales guy stuff comes in) From a penetration perspective, the failure to recognize potential opportunities either within the existing client base or in the operating market, introduces revenue growth risk (meaning we aren’t keeping our eye on the top line). Ultimately it impacts the FI’s ability to add assets (either deposits or loans) and also has a direct affect on efficiency and deposit to loan ratios. From a retention perspective, the risk is even more obvious. Our most valued clients are the ones that we must continuously engage in a proactive manner. Let’s face it. In even the smallest markets, there are no less than four to six other institutions waiting to jump on your client in the event that you grow complacent. There is a huge difference between selection and satisfaction. And, if we aren’t focused on keeping a client after securing them, our net portfolio growth targets will be impossible to achieve. Considering the current market environment, now more than ever, effectively managing these three elements of “risk/exposure to the FI” is crucial to an institutions success both practically and pragmatically. Everyone internally at the bank is focused on the “credit risk mitigation” piece. The conversations that are occurring outside of the bank’s walls however are focused on the “L” word or liquidity and getting credit flowing again. How many times have we read or more frankly been beaten with this comment from business owners “…there’s no one making loans anymore…” or “…its impossible to get credit…?” That should be read as … penetration and retention Striking a balance between effective and appropriate credit risk exposure and deepening or growing the portfolio has been a challenge facing those of us in the front office for as long as I can remember. The “sales revolution” is effectively over. We’ve learned the critical lesson that we need to evolve beyond being strictly a credit officer (you did learn that right??!!). And, you didn’t/shouldn’t become a “banking products generalist” with no analytical depth. Knowing all this, it is important that we return to the guiding principles of effective lending which include: - evaluating the scope of the opportunity; - isolating the risk and identifying a reasonable and realistic recovery/mitigation remedy; - determining what other alternatives the borrower might be considering; and - being willing to let the “bad deals” walk. In subsequent blogs, I’ll provide you with specific tactics aimed at optimizing penetration and retention efforts and implementing effective and practical client management strategies. After all what would you expect from a business development guy…
By: Tom Hannagan Part 5 This continues the updated review of results from the Uniform Bank Performance Reports (UBPR), courtesy of the FDIC, for 2008. The UBPR is based on the quarterly required Call Reports submitted by insured banks. The FDIC compiles peer averages for various bank size groupings. Here are some findings for the two largest groups, covering 494 reporting banks. I wanted to see how the various profit performance components compare to the costs of credit risk discussed in my previous post. It is even more apparent than it was in early 2008 that banks still have a ways to go to be fully pricing loans for both expected and unexpected risk. Peer Group 2 (PG2) consists of 305 reporting banks between $1 billion and $3 billion in assets. PG2’s Net Interest Income was 5.75 percent of average total assets for the year. This is also down, as expected, from 6.73 percent in 2007. Net Interest Expense also decreased from 3.07 percent in 2007 to 2.31 percent for 2008. Net Interest Margin, also declined from 3.66 percent in 2007 to 3.42 percent in 2008, or a loss of 24 basis points. These margins are 31 bps or 10 percent higher than found in Peer Group 1 (PG1), but the drop of .24 percent was much larger than the .05 percent decline in PG1. As with all banks, Net Interest Margins have shown a steady chronic decline, but the drops for PG2 have been coming in larger chunks the last two years -- -24 basis points last year after dropping 16 points from 2006 to 2007. Behind the drop in margins, we find loans yields of 6.53 percent for 2008, which is down from 7.82 percent in 2007. This is a decline of 129 basis points or 16 percent. Meanwhile, rates paid on interest-earning deposits dropped from 3.70 percent in 2007 to 2.75 percent in 2008. This 95 basis point decline represents a 26 percent lower cost of interest-bearing deposits. Again, with a steeper decline in interest costs, you would think that margins should have improved somewhat. It wasn’t meant to be. We see the same two culprits as we did in PG1. Total deposit balances declined from 78 percent of average assets to 77 percent which means again, that a larger amount had to be borrowed to fund assets. Secondly, non-interest bearing demand deposits continued an already steady decline from 5.58 percent of average assets in 2007 to 5.03 percent. This, of course, resulted in fewer deposit balances relative to total asset size and a lower proportion of interest-cost-free deposits. Check my next blog for more on an analysis of Peer Group 2’s fee income, operating expenses and their use of risk-based pricing.
By: Tom Hannagan Part 4 Let’s dig a bit deeper into why Peer Group 1’s margins didn’t improve. We see two possible reasons: Total deposit balances declined from 72 percent of average assets to 70 percent. This means that a larger amount had to be borrowed to fund their assets. Secondly, non-interest bearing demand deposits declined from 4.85 percent of average assets to 4.24 percent. So, fewer deposit balances relative to total asset size, along with a lower proportion of interest-cost-free deposits, appear to have made the difference. Fee income Non-interest income, again as a percent of average total assets, was down to 1.12 percent from 1.23 percent in 2007. This was a decline of 9 percent. For Peer Group 1 (PG1), fees have also been steadily declining relative to asset size, down from 1.49 percent of assets in 2005. A lot of fee income is deposit based and largely based on non-interest bearing deposits. So, the declining interest-free balances, as a percent of total assets, are a source of pressure on fee income and have a negative impact on net interest margins. Operating expenses Operating expenses constituted more bad news as they increased from 2.63 percent to 2.77 percent of average assets. Most of the large scale cost-cutting didn’t get started early enough to favorably impact this number for last year. Historically, this metric has moved down, irregularly, as the size of the largest banks has grown. This number stood at 2.54 percent in 2006, for instance. We saw increase in both 2007 and again in 2008. As a result of the decline in margins and the larger percentage decline in fee income, while operating costs increased, the Peer Group 1 efficiency ratio lost ground from 57.71 percent in 2007 up to 63.70 percent in 2008. This 10 percent increase is a move to the bad. It means every dollar in gross revenue [net interest income + fee income] cost them almost 64 cents in administrative expenses in 2008. This metric averaged 55 cents in 2005/2006. The total impact of changes in margin performance, fee income, operating expenses and the 2008 increase in provision expense of 87 basis points, we arrive at a total decline in pre-tax operating income of 1.23 percent on total assets. That is a total decline of 80 percent from the pre-tax performance in 2007 of 1.53 percent pre-tax ROA to the 2008 result for the group of only .30 percent pre-tax ROA. It would appear that banks have not been utilizing pricing enough credit risk into their loan rates. This would be further confirmed if you compared bank loan rates to the historic risk spreads and absolute rates that the market currently has priced into both investment grade and below-investment-grade corporate bonds. These spreads have decreased some very recently, but it is predicted that more credit risk is present than bank lending rates would indicate.
Part 3 Reducing operational and overhead costs starts with the automation of tasks that would otherwise be performed by a human resource. By leveraging an advanced segmentation approach, it is possible to better identify accounts that will not require collector intervention. While automation is not a new concept to collections, significant benefits of modern systems include: • enabling more functions to be automated; • effectiveness of the automated functions to be validated; and • more changes made per year versus legacy systems. Fixing a bad phone number: The old way To illustrate effective automation, let’s use an example where an account is found to have a bad phone number. A common approach to this problem might be for the outbound collector to route the account to a skip specialist who can perform research. This often has the receiving party starting the process after the nightly batch process has transferred the account across departments. If a phone number is found, the account may be manually routed back to an outbound queue and if not, a no-contact letter may be generated. Additionally, there are tasks that need to be performed such as noting accounts that consume a collector’s time. Fixing a bad phone number: The new way A more efficient and cost-effective approach would be for the employee identifying the need for a new number to click a pre-defined button to let the collections system know of the issue. The system could then automatically call out to an external data source to: • collect the new number; • repopulate the appropriate field; • reroute the account back to the most appropriate outbound queue; • log a history of all automated functions performed, and • do all of this within just a few seconds! If the appropriate number cannot be located, the system would know which letter to send and then route the account to the most appropriate holding queue. Reducing operational costs After automation, the operational costs are further reduced by identifying which actions can be effectively replaced by lower-cost options that yield the same results, or even eliminating actions that present no substantial value. For example, why make a call when a letter will suffice? And what happens if we subsequently replace that letter with a text message or take no action at all? Intelligent features of modern systems such as champion/challenger testing can be employed to support a continuous learning process that increases the financial benefits of automation as experience and knowledge is gained. As new automation is introduced and validated as beneficial, other improvement theories can be tested and subsequently abandoned or adopted. Considering the possible impact of automation and action reductions on cost savings let’s assume that three dial attempts are made on the average delinquent account in the first 30 days at a cost of 25 cents each and on the fourth attempt there is a right party contact, which costs an additional $2.50 (assuming the talk time is five minutes). Adding one letter at 75 cents, we have a total cost to collect of $4.00 before the account hits 31 days past due. With 250,000 customers entering collections each month, we can save $200,000 each month in the early stage alone with just a 20 percent improvement. This result could easily be achieved by reducing talk time and eliminating unnecessary actions or unproductive call attempts. Annually that adds up to approximately $2.5 million dollars in savings, in this example. Champion/challenger tests, as well as, the improved functionality of modern systems can also be extended beyond the in-house work stream. Evaluating and comparing external agencies can significantly improve agency performance as well as enable the lender to better manage placement costs. For example, if a lender allocates 1,000 accounts to an external agency each month, with an average balance of $3,000, the total dollars allocated annually is $36 million. If 22 percent of the debt is collected and a 25 percent commission is charged, the net to the lender is nearly $6 million. Improving that return by a mere 4 percent through better allocation strategies, which is a conservative goal, we add another million to the bottom line each year. By factoring in the ability of next generation collections systems to automate most aspects of the placement process itself, including recalling accounts, we further improve efficiencies, free up valuable resources and allow management greater control of the process. Additional benefits of functionally rich modern systems also enable management to grant external resources various levels of remote access to the collections systems to better monitor activities and ensure that transactional data is properly captured. In addition to granting external agencies remote access, modern collections systems can also enable collectors to work from home-based workstations to further reduce operational costs. Many industry analysts see this as an emerging trend over the next few years, particularly when productivity can be monitored in real-time. My next blog will continue the discussion on the benefits of next generation collections systems and will provide details on improved change management processes.
Here are a few more frequently asked questions. 1. Am I a “creditor” under the rule? The term “creditor” has the same meaning as under the Equal Credit Opportunity Act (ECOA) and is defined as a person who regularly participates in credit decisions, including, for example, a mortgage broker, a person who arranges credit or a servicer of loans who participates in “workout” decisions. The term “credit” is defined, as in the ECOA, as the right granted by a creditor to defer payment for goods or services. It is important to note that commercial, as well as consumer, credit accounts may be covered by the Rule. 2. We are an insurance company that uses credit reports to underwrite insurance. Does the Red Flags Rule apply to us? The Red Flag Rule applies to creditors and depository institutions and should not apply to an insurer when engaged in activities related to insurance underwriting. To the extent that you extend credit, however, you may be covered. For example, you may wish to examine whether you permit consumers to finance their premiums; whether you extend credit to vendors, independent agents or other business partners; or whether you extend credit in connection with your investment activities, including real-estate investments. 3. I am an auto dealer. Does the rule apply to me? If the business extends auto credit to consumers or arranges auto credit for consumers, the Red Flag guidelines may apply.
By: Tom Hannagan Part 3 I believe it is quite important to compare your bank or your investment plans in a financial institution to the results of peer group averages. Not all banks are the same, believe it or not. In this column, we use the averages. Again, look for the differences in your target institution. About half of them beat certain performance numbers, while the other half are naturally worse. It can tell a useful story. This continues the updated review of results from the Uniform Bank Performance Reports (UBPR), courtesy of the FDIC, for 2008. The UBPR is based on the quarterly required Call Reports submitted by insured banks. The FDIC compiles peer averages for various bank size groupings. Here are the findings for the two largest groups that cover 494 reporting banks. I wanted to see how the various profit performance components compare to the costs of credit risk discussed in my previous post. It is even more apparent than it was in early 2008 that banks still have a ways to go to be fully pricing loans for both expected and unexpected risk. Peer Group 1 (PG1) is made up of the largest 189 reporting banks or those with over $3 billion in average total assets for 2008. Interest income was 5.25 percent of average total assets for the period. This is down, as we might expect, based on last year’s decline in the general level of interest rates from 6.16 percent in 2007. Net Interest Expense was also down from 2.98 percent in 2007 to 2.06 percent average for the year. Net Interest Margin, the difference between the two metrics, was down from 3.16 percent in 2007 to 3.11 percent as a percentage of total assets. It should be noted that Net Interest Margins have been in a steady, chronic decline for at least 10 years, with a torturous regular drop of 2 to 5 basis points per annum in recent years. Last year’s drop of five basis points is in line with that progression and it does add to continuing difficulty in generating bottom-line profits. To find out a bit more about why margins dropped, especially in light of the steady increase in lending over the same past decade, we looked first at loan pricing yields. For PG1 these averaged 6.12 percent for 2008, down (again, expectedly) from 7.32 percent in 2007. This is a drop of 120 basis points or a decline of 16 percent. Meanwhile, rates paid on interest-earning deposits dropped from 3.41 percent in 2007 to 2.39 percent in 2008. This 102 basis point decline represents a 30 percent lower interest expense on interest-bearing deposits. Based only on these two metrics, it seems like margins should have improved and not declined for these banks. Check my next blog for more on the reasons for Peer Group 1’s drop in margins and an analysis of the fee income and operating expenses for these institutions.
Here we are in March, 2009, four months after the Red Flags Rules deadline OR two months until the Red Flags deadline…depending on your glass-half-full / glass-half-empty view of the world. I can say with confidence that at this point in time, the Identity Theft Red Flags 'discussion' with our clients and the market at large continues in full earnest. That said, however, the nature of our discussions has changed substantially. A few months ago, the needs expressed by the market centered on education around the Red Flags Rule, Red Flag compliance and it's applicability to various markets and account types. I find that the majority of my daily conversations on the subject now regard efficiencies in process and cost combined with effectiveness and customer experience. Most of our clients 'get' what they need to be doing such as identifying, detecting and responding to Red Flag conditions. Where we are still working closely with our clients is in how they can optimize their policies and procedures to ensure that the majority of Red Flag conditions are detected and reconciled in singular automated steps. As I've said in previous blogs, detecting these conditions is the easy part. It's how you reconcile (a.k.a. respond to) those conditions that makes the difference in your bottom line. As May 1 approaches, now is a great time to be monitoring each step in your process in an effort to identify those areas that may still have room for efficiency gains and improved customer experience.
Address discrepancies aren't the end of the road, but they sure can be a bump in it. One of the handful of mandatory elements in the Red Flag guidelines, which focus on FACTA Sections 114 and 315, is the implementation of Section 315. Section 315 provides guidance regarding reasonable policies and procedures that a user of consumer reports must employ when a consumer reporting agency sends the user a notice of address discrepancy. A couple of common questions and answers to get us started: 1. How do the credit reporting agencies display an address discrepancy? Each credit reporting agency displays an “address discrepancy indicator,” which typically is simply a code in a specified field. Each credit reporting agency uses a different indicator. Experian, for example, supplies an indicator for each displayable address that denotes a match or mismatch to the address supplied upon inquiry. 2. How do I “form a reasonable belief” that a credit report relates to the consumer for whom it was requested? Following procedures that you have implemented as a part of your Customer Identification Program (CIP) under the USA PATRIOT Act can and should satisfy this requirement. You also may compare the credit report with information in your own records or information from a third-party source, or you may verify information in the credit report with the consumer directly. In my last posting, I discussed the value of a risk-based approach to Red Flag compliance. Foundational to that value is the ability to efficiently and effectively reconcile Red Flag conditions…including addressing discrepancies on a consumer credit report. Arguably, the biggest Red Flag problem we solve for our clients these days is in responding to identified and detected Red Flag conditions as part of their Identity Theft Prevention Program. There are many tools available that can detect Red Flag conditions. The best-in-class solutions, however, are those that not only detect these conditions, but allow for cost-effective and accurate reconciliation of high risk conditions. Remember, a Red Flag compliant program is one that identifies and detects high risk conditions, responds to the presence of those conditions, and is updated over time as risk and business processes change. A recent Experian analysis of records containing an address discrepancy on the credit profile showed that the vast majority of these could be positively reconciled (a.k.a. authenticated) via the use of alternate data sources and scores. Layer on top of a solid decisioning strategy using these elements, the use of consumer-facing knowledge-based authentication questions, and nearly all of that potential referral volume can be passed through automated checks without ever landing in a manual referral queue or call center. Now that address discrepancies can no longer be ignored, this approach can save your operations team from having to add headcount to respond to this initially detected condition.
By: Tom Hannagan Part 2 In my last post, I started my review of the Uniform Bank Performance Reports for the two largest financial institution peer groups through the end of 2008. Now, lets look at the resutls relating to credit cost, loss allowance accounts and the impacts on earnings. Again, as you look at these results, I encourage you to consider the processes that your bank currently utilizes for credit risk modeling and financial risk management. Credit costs More loans, especially in an economic downturn, mean more credit risk. Credit costs were up tremendously. The Peer group 1 banks reported net loan losses of .89% of total loans. This is an increase from .28% in 2007, which was up from an average of 18 basis points on the portfolio in 2006/2005. The Peer group 2 banks reported net loan losses of .74%. This is also up substantially from 24 basis points in 2007 and an average of 15 basis points in 2006/2005. The net loan losses reported in the fourth quarter significantly boosted both groups’ year-end loss percentages above where they stood through the first three quarters last year. Loss allowance accounts Both groups also ramped up their reserve for future expected losses substantially. The year-end loss allowance account (ALLL) as a percent of total loans stood at 1.81% for the largest banks. This is an increase of almost 50% from an average of 1.21% in the years 2007/2004. Peer group 2 banks saw their reserve for losses go up to 1.57% from an average of 1.24% in the years 2007/2004. The combination of covering the increased net loan losses and also increasing the loss reserve balance required a huge provision expenses. So, loan balances were up even in the face of increased write-offs and expected forward losses. Impacts on earnings Obviously, we would expect this provisioning burden to negatively impact earnings. It did, greatly. Peer group 1 banks saw a decline in return on assets to a negative .07%. This is just below break-even as a group. The average net income percentage stood at .42% of average assets at the end of the third quarter. So, the washout in the fourth quarter reports took the group average to a net loss position for the year. The ROA was at .96% in 2007 and an average of 1.26% in 2006/2005. That is a 111% decline in ROA from 2007. Return on equity also went into the red, down from 11.97% in 2007. ROE stood at 14.36% in 2005. For the $1B to $3B banks, ROA stood at .35%. This is still a positive number, however, it is way down from 1.08% in 2007, 1.30% in 2006 and 1.33% in 2005. The decline in 2008 was 67% from 2007. ROE for the group was also down, at 4.11% from 12.37% in 2007. The drops in profitability were not entirely the result of credit losses, but this was by far the largest impact from 2007. The seriously beefed-up ALLL accounts would seem to indicate that, as a group, the banks expect further loan losses, at least through 2009. These numbers largely pre-dated the launch of the Troubled Asset Relief Program and the tier one funding it provided in 2008. But, it is clear that banks had not contracted lending for all of 2008, even in the face of mounting credit issues and a declining economic picture. It will be interesting to see how things unfold in the next several quarters.
By: Tom Hannagan Part 1 It may be quite useful to compare your financial institution\'s portfolio risk management process or your investment plans , to the results of peer group averages. Not all banks are the same -- believe it or not. Here are the averages. You should look for differences in your target institution. About half of them beat certain performance numbers and the other half may be naturally worse. As promised, I have again reviewed the Uniform Bank Performance Reports for the two largest peer groups through the end 2008. The Uniform Bank Performance Report (UBPR) is a compilation of the FDIC, based on the call reports submitted by insured banks. The FDIC reports peer averages for various bank size groupings and here are a few notable findings for the two largest groups that covers 494 reporting banks. Peer group 1 Peer group 1 consisted of 189 institutions over $3 billion in average total assets for the year. Net loans accounted for 67.31% of average total assets, which is up from 65.79 % in 2007. Loans, as a percent of assets, have increased steadily since at least 2004. The loan-to-deposit ratio for the largest banks was also up to 96% from 91% in 2007 and 88% in both 2006 and 2005. So, it appears these banks were lending more in 2008 as an allocation of their total asset base and relative to their deposit sources of funding. In fact, net loans grew at a rate of 9.34% for this group, which is down from the average growth rate of 15.07% for the years 2005 through 2007. The growth rate in loans is down, which is probably due to tightened credit standards. However, it is still growth. And, since total average assets also had growth of 11.58% in 2008, the absolute dollars of loan balances increased at the largest banks. Peer group 2 Peer group 2 consisted of 305 reporting financial institutions between $1B and $3B in total assets. The net loans accounted for 72.96% of average total assets, up from 71.75% in 2007. Again, the loans as a percent of total assets have increased steadily since at least 2004. The loan-to-deposit ratio for these banks was up to 95% from 92% in 2007 and an average of 90% for 2006 and 2005. So, these banks are also lending more in 2008 as a portion of their asset base and relative to their deposit source of funding. Net loans grew at a rate of 10.48% for this group in 2008 which is down from 11.94% growth in 2007 and down from an average growth of 15.04% for 2006 and 2005. And, since total average assets also had growth of 10.02% in 2008, the absolute dollars of loan balances also increased at the intermediate size banks. Again here, the growth rate in loans is down, probably due to tightened credit standards, but it is still growth and it is at a slightly more aggressive rate than the largest bank group. Combined, for these 494 largest financial institutions, loans were still growing through 2008 both as a percentage of asset allocation and in absolute dollars. Tune in to my next blog to read more about the results shown relating to credit costs, loss allowance accounts and the impacts on earnings.
At which stage of the application process does the Red Flags Rule apply? The Red Flag Rule would apply whenever you detect a Red Flag in connection with an application. This could occur as soon as you receive an application, for example: if the application appears to have been altered or forged; or the consumer’s identification appears to be forged or is inconsistent with the information on the application. Is the social security number (SSN) check a requirement? No, but an invalid SSN may be a Red Flag – i.e., an indicator of possible identity theft – and obtaining and verifying a SSN may be a reasonable means of application risk management to detect this Red Flag when opening accounts. You may be able to utilize your existing procedures under your Customer Identification Program under the USA PATRIOT Act.
What to do when you see a Red Flag. Your Identity Theft Prevention Program should include appropriate responses when you detect a Red Flag. You must assess whether the Red Flag evidences a risk of identity theft. If so, your response must be commensurate with the degree of risk posed. Depending on the level of risk, an appropriate response may include contacting your applicant, not opening a new account or even determining that no response is necessary.
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