By: Tom Hannagan Some articles that I’ve come across recently have puzzled me. In those articles, authors use the terms “monetary base” and “money supply” synonymously -- but those terms are actually very different. The monetary base (currency plus Fed deposits) is a much smaller number than the money supply (M1). The huge change in the “base”, which the Fed did affect by adding $1T or so to infuse a lot of quick liquidity into the financial system late in 2007/early 2008, does not necessarily impact M1 (which includes the base plus all bank demand deposits) all that much in the short-term, and may impact it even less in the intermediate-term if the Fed reduces its holdings of securities.  Some are correct, of course, in positing that a rotation out of securities by the Fed will tend to put pressure on market rates. Some are equivocating the 2007 liquidity moves of the Fed, with a major monetary policy change. When the capital markets froze due to liquidity and credit risks in August/September of 2007, monetary policy was not the immediate risk, or even a consideration. Without the liquidity injections in that timeframe, monetary policy would have become less than an academic consideration. Tying the “constrained” (which actually was a slowdown in growth of) bank lending to bank reserves on account at the Fed I don’t think their Fed reserve balance was ever an issue for lending. Banks slowed down lending because the level of credit risk increased. Borrowers were defaulting. Bank deposit balances were actually increasing through the financial crisis. [See my Feb 26 and March 5 blogs] So, loan funding, at least from deposit sources was not the problem for most banks. Of course, for a small number of banks that had major securities losses, capital was being lost and therefore not available to back increased lending. But demand deposit balances were growing. Some authors are linking bank reserves to the ability of banks to raise liabilities, which makes little sense. Banks’ respective abilities to gather demand deposits (insured by the FDIC, at no small expense to the banks) was always wide open, and their ability to borrow funds is much more a function of asset quality (or net asset value) more than it relates their relatively small reserve balances at the Fed. These actions may result in high inflation levels and high interest rates -- but it will be because of poor Fed decisions in the future, not because of the Fed’s action of last year. It will also depend on whether the fiscal (deficit) actions of the government are: 1) economically productive and 2) tempered to a recovery, or not. I think that is a bigger macro-economic risk than Fed monetary policy. In fact, the only way bank executives can wisely manage the entity over an extended timeframe is to be able to direct resources across all possibilities on a risk-adjusted basis. The question isn’t whether risk-based pricing is appropriate for all lines of business, but rather how might or should it be applied. For commercial lending into the middle and corporate markets, there is enough money at stake to warrant evaluating each loan and deposit, as well as the status of the client relationship, on an individual basis. This means some form of simulation modeling by relationship managers on new sales opportunities (including renewals) and the model’s ready access to current data on all existing pieces of business with each relationship. [See my April 24 blog entry.] This process also implies the ability to easily aggregate the risk-return status of a group of related clients and to show lenders how their portfolio of accounts is performing on a risk-adjusted basis. This type of model-based analysis needs to be flexible enough to handle differing loan structures, easy for a lender to use and quick. The better models can perform such analysis in minutes. I’ve discussed the elements of such models in earlier posts. But, with small business and consumer lending there are other considerations that come into play. The principles of risk-based pricing are consistent across any loan or deposit. With small business lending, the process of selling, negotiating, underwriting and origination is significantly more streamlined and under some form of workflow control. With consumer lending, there are more regulations to take into account and there are mass marketing considerations driving the “sales” process. Agreement covers what the new owner wants now and may decide it wants in the future. This a form of strategic business risk that comes with accepting the capital infusion from this particular source.  

In recent months, the topics of stress-testing and loss forecasting have been at the forefront of the international media and, more importantly, at the forefront of the minds of American banking executives. The increased involvement of the federal government in managing the balance sheets of the country’s largest banks has mixed implications for financial institutions in this country. On one hand, some banks have been in the practice of building macroeconomic scenarios for years and have tried and tested methods for risk management and loss forecasting. On the other hand, in financial institutions where these practices were conducted in a less methodical manner, if at all, the scrutiny placed on capital adequacy forecasting has left many looking to quickly implement standards that will address regulatory concerns when their number is called. For those clients to whom this process is new, or for those who do not possess a methodology that would withstand the examination of federal inspectors, the question seems to be – where do we begin? I think that before you can understand where you’re going, you must first understand where you are and where you have been. In this case, it means having a detailed understanding of key industry and peer benchmarks and your relative position to those benchmarks. Even simple benchmarking exercises provide answers to some very important questions. • What is my risk profile versus that of the industry? • How does the composition of my portfolio differ from that of my peers? • How do my delinquencies compare to those of my peers? How has this position been changing? By having a thorough understanding of one’s position in these challenging circumstances, it allows for a more educated foundation upon which to build assessments of the future.  

By: Kari Michel Are you using scores to make new applicant decisions? Scoring models need to be monitored regularly to ensure a sound and successful lending program. Would you buy a car and run it for years without maintenance -- and expect it to run at peak performance? Of course not. Just like oil changes or tune-ups, there are several critical components that need to be addressed regarding your scoring models on a regular basis. Monitoring reports are essential for organizations to answer the following questions: • Are we in compliance? • How is our portfolio performing? • Are we making the most effective use of your scores? To understand how to improve your portfolio performance, you must have good monitoring reports. Typically, reports fall into one of three categories: (1) population stability, (2) decision management, (3) scorecard performance. Having the right information will allow you to monitor and validate your underwriting strategies and make any adjustments when necessary. Additionally, that information will let you know that your scorecards are still performing as expected. In my next blog, I will discuss the population stability report in more detail.

By: Tracy Bremmer It’s not really all about the credit score. Now don’t get me wrong, a credit score is a very important tool used in credit decision making; however there’s so much more that lenders use to say “accept” or “decline.” Many lenders segment their customer/prospect base prior to ever using the score. They use credit-related attributes such as, “has this consumer had a bankruptcy in the last two years?” or “do they have an existing mortgage account?” to segment out consumers into risk-tier buckets. Lenders also evaluate information from the application such as income or number of years at current residence. These types of application attributes help the lender gain insight that is not typically evaluated in the traditional risk score. For lenders who already have a relationship with a customer, they will look at their existing relationships with that customer prior to making a decision. They’ll look at things like payment history and current product mix to better understand who best to cross-sell, up-sell, or in today’s economy, down-sell. In addition, many lenders will run the applicant through some type of fraud database to ensure the person really is who they say they are. I like to think of the score as the center of the decision, with all of these other metrics as necessary inputs to the entire decision process. It is like going out for an ice cream sundae and starting with the vanilla and needing all the mix-ins to make it complete.

-- By Kari Michel What is your credit risk score?  Is it 300, 700, 900 or something in between?  In order to understand what it means, you need to know which score you are referencing.  Lenders use many different scoring models to determine who qualifies for a loan and at what interest rate. For example, Experian has developed many scores, such as VantageScore®.  Think of VantageScore® as just one of many credit scores available in the marketplace. While all credit risk models have the same purpose, to use credit information to assess risk, each credit model is unique in that each one has its own proprietary formula that combines and calculates various credit information from your credit report.  Even if lenders used the same credit risk score, the interpretation of risk depends on the lender, and their lending policies and criteria may vary. Additionally, each credit risk model has its own score range as well.  While the score range may be relatively similar to another score range, the meaning of the score may not necessarily be the same. For example, a 640 in one score may not mean the same thing or have the same credit risk as a 640 for another score.  It is also possible for two different scores to represent the same level of risk. If you have a good credit score with one lender, you will likely have a good score with other lenders, even if the number is different.

As I've suggested in previous postings, we've certainly expected more clarifying language from the Red Flags Rule drafting agencies.  Well, here is some pretty good information in the form of another FAQ document created by the Board of Governors of the Federal Reserve System (FRB), Federal Deposit Insurance Corporation (FDIC), National Credit Union Administration (NCUA), Office of the Comptroller of the Currency (OCC), Office of Thrift Supervision (OTS), and Federal Trade Commission (FTC). This is a great step forward in responding to many of the same Red Flag guidelines questions that we get from our clients, and I hope it's not the last one we see.  You can access the document via any of the agency website, but for quick reference, here is the FDIC version: http://www.fdic.gov/news/news/press/2009/pr09088.html

We at Experian have been conducting a survey of visitors to our Red Flag guidelines microsite (www.experian.com/redflags). Some initial findings show that approximately 40 percent of those surveyed were "ready" by the original November 1, 2008 deadline.  However, nearly 50 percent of the respondents found the Identity Theft Red Flag deadline extension(s) helpful. For those of you that have not taken the survey, please do so.  We welcome your feedback.  

As most industry folks are aware, the FTC recently pushed out their Red Flags Rule enforcement deadline to August 1, 2009.  It is important to note, however, that this extension does not apply to the specific requirement that institutions with covered accounts detect and respond to address discrepancies related to consumer credit profiles.  The original November 1, 2008 deadline is, and has been, the line in the sand for this requirement.  I recommend that those institutions still working toward a compliant written and operational Identity Theft Prevention Program ensure that they have in place today a process to detect and respond to address discrepancies noted on credit profiles.

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.  

Back during World War I, the concept of “triage” was first introduced to the battlefield.  Faced with massive casualties and limited medical resources, a system was developed to identify and select those who most needed treatment and who would best respond to treatment.  Some casualties were tagged as terminal and received no aid; others with minimal injuries were also passed over.  Instead, medical staff focused their attentions on those who required their services in order to be saved.  These were the ones who needed and would respond to appropriate treatment.  Our clients realize that the collections battlefield of today requires a similar approach.  They have limited resources to face this mounting wave of delinquencies and charge offs.  They also realize that they can’t throw bodies at this problem. They need to work smarter and use data and decisioning more effectively to help them survive this collections efficiency battle. Some accounts will never “cure” no matter what you do.  Others will self-cure with minimal or no active effort. Taking the right actions on the right accounts, with the right resources, at the right time is best accomplished with advanced segmentation that employs behavioral scoring, bureau-based scores and other relevant account data. The actual data and scores that should be used depend on the situation and account status, and there is no one-size-fits-all approach.  

How is your financial institution/organization working to improve your collections work stream?What are some of your keys for collections efficiency?What tools do you use to manage your collections workflow?

What are your thoughts on the third extension to the Identity Theft Red Flags Rule deadline? Was your institution ready to meet Red Flag guidelines? 

In addition to behavioral models, collections and account management groups need the ability to implement collections workflow strategies in order to effectively handle and process accounts, particularly when the optimization of resources is a priority. While the behavioral models will effectively evaluate and measure the likelihood that an account will become delinquent or result in a loss, strategies are the specific actions taken, based on the score prediction, as well as other key information that is available when those actions are appropriate. Identifying high-risk accounts, for example, may result in strategies designed to accelerate collections management activity and execute more aggressive actions. On the other hand, identifying low-risk accounts can help determine when to take advantage of cost-saving actions and focus on customer retention programs.  Effective strategies also address how to handle accounts that fall between the high- and low-risk extremes, as well as accounts that fall into special categories such as first payment defaults, recently delinquent accounts and unique customer or product segments. To accommodate lenders with systems that cannot support either behavioral scorecards or strategies, Experian developed the powerful service bureau solution, Portfolio Management Package, which is also referred to as PMP. To use this service, lenders send Experian customer master file data on a daily basis. Experian processes the data through the Portfolio Management Package system which includes calculating Fast Start behavior scores and identifying special handling accounts and electronically delivers the recommended strategies and actions codes within hours. Scoring and strategy parameters can be easily changed, as well as portfolio segmentation, special handling options and scorecard selections. PMP also supports Champion Challenger testing to enable users to learn which strategies are most effective. Comprehensive reports suites provide the critical information needed for lenders to design strategies and evaluate and compare the performance of those strategies.  

  Does the rule list the Red Flags? The Identity Theft Red Flags Rule provides several examples of Red Flags in four separate categories: 1. alerts and notifications recieved from credit reporting agencies and third-party service providers; 2. the presentation of suspicious documents or suspicious identifying information;   3. unusual or suspicious account usage patterns; and 4. notices from a customer, identity theft victim or law enforcement.    

Optimization is a very broad and commonly used term today and the exact interpretation is typically driven by one's industry experience and exposure to modern analytical tools. Webster defines optimize as: "to make as perfect, effective or functional as possible". In the risk/collections world, when we want to optimize our strategies as perfect as technology will allow us, we need to turn to advanced mathematical engineering. More than just scoring and behavioral trending, the most powerful optimization tools leverage all available data and consider business constraints in addition to behavioral propensities for collections efficiency and collections management. A good example of how this can be leveraged in collections is with letter strategies. The cost of mailing letters is often a significant portion of the collections operational budget. After the initial letter required by the Fair Debt Collection Practice Act (FDCPA) has been sent, the question immediately becomes: “What is the best use of lettering dollars to maximize return?” With optimization technology we can leverage historical response data while also considering factors such as the cost of each letter, performance of each letter variation and departmental budget constraints, while weighing the alternatives to determine the best possible action to take for each individual customer. n short, cutting edge mathematical optimization technology answers the question: "Where is the point of diminishing return between collections treatment effectiveness and efficiency / cost?"