Tag: credit scoring

Previously, we looked at the various ways a dual score strategy could help you focus in on an appropriate lending population. Find your mail-to population with a prospecting score on top of a risk score; locate the riskiest of all consumers by layering a bankruptcy score with your risk model. But other than multiple scores, what other tools can be used to improve credit scoring effectiveness? Credit attributes add additional layers of insight from a risk perspective. Not everyone who scores an 850 represent the same level of risk once you start interrogating their broader profile. How much total debt are they carrying? What is the nature of it - is it mortgage or mostly revolving? A credit score may not fully articulate a consumer as high risk, but if their debt obligations are high, they may represent a very different type of risk than from another consumer with the same 850 score.  Think of attribute overlays in terms of tuning the final score valuation of an individual consumer by making the credit profile more transparent, allowing a lender to see more than just the risk odds associated with the initial score. Attributes can also help you refine offers. A consumer may be right for you in terms of risk, but are you right for them? If they have 4 credit cards with $20K limits each, they’re likely going to toss your $5K card offer in the trash. Attributes can tell us these things, and more. For example, while a risk score can tell us what the risk of a consumer is within a set window, certain credit attributes can tell us something about the stability of that consumer to remain within that risk band. Recent trends in score migration – the change in a level of creditworthiness of a consumer subsequent to generation of a current credit score – can undermine the most conservative of risk management policies. At the height of the recession, VantageScore® Solutions LLC studied the migration of scores across all risk bands and was able to identify certain financial management behaviors found within their credit files. These behaviors (signaling, credit footprint, and utility) assess the consumer’s likelihood of improving, significantly deteriorating, or maintaining a stable score over the next 12 months.  Knowing which subgroup of your low-risk population is deteriorating, or which high risk groups are improving, can help you make better decision today.

Last month, I wrote about seeking ways to ensure growth without increasing risk.  This month, I’ll present a few approaches that use multiple scores to give a more complete view into a consumer’s true profile. Let’s start with bankruptcy scores. You use a risk score to capture traditional risk, but bankruptcy behavior is significantly different from a consumer profile perspective. We’ve seen a tremendous amount of bankruptcy activity in the market. Despite the fact that filings were slightly lower than 2010 volume, bankruptcies remain a serious threat with over 1.3 million consumer filings in 2011; a number that is projected for 2012.  Factoring in a bankruptcy score over a traditional risk score, allows better visibility into consumers who may be “balance loading”, but not necessarily going delinquent, on their accounts. By looking at both aspects of risk, layering scores can identify consumers who may look good from a traditional credit score, but are poised to file bankruptcy. This way, a lender can keep their approval rates up and lower risk of overall dollar losses. Layering scores can be used in other areas of the customer life cycle as well. For example, as new lending starts to heat up in markets like Auto and Bankcard, adding a next generation response score to a risk score in your prospecting campaigns, can translate into a very clear definition of the population you want to target. By combining a prospecting score with a risk score to find credit worthy consumers who are most likely to open, you help mitigate the traditional inverse relationship between open rates and credit worthiness. Target the population that is worth your precious prospecting resources. Next time, we’ll look at other analytics that help complete our view of consumer risk. In the meantime, let me know what scoring topics are on your mind.

For as long as there have been loans, there has been credit risk and risk management. In the early days of US banking, the difficulty in assessing risk meant that lending was severely limited, and many people were effectively locked out of the lending system. Individual review of loans gave way to numerical scoring systems used to make more consistent credit decisions, which later evolved into the statistically derived models we know today. Use of credit scores is an essential part of almost every credit decision made today. But what is the next evolution of credit risk assessment? Does that current look at a single number tell all we need to know before extending credit? As shown in a recent score stability study, VantageScoreSM remains very predictive even in highly volatile cycles. While generic risk scores remain the most cost-effective, expedient and compliant method of assessing risk, this last economic cycle clearly shows a need for the addition of other metrics (including other generic scores) to more fully illuminate the inherent risk of an individual from every angle. We’ve seen financial institutions tightening their lending policies in response to recent market conditions, sometimes to the point of hampering growth. But what if there was an opportunity to relook at this strategy with additional analytics to ensure continued growth without increasing risk?  We'll plan to explore that further over the coming weeks, so stick with me.  And if there is a specific question or idea on your mind, leave a comment and we'll cover that too.

By: Kari Michel The way medical debts are treated in scores may change with the introduction of June 2011, Medical Debt Responsibility Act. The Medical Debt Responsibility Act would require the three national credit bureaus to expunge medical collection records of $2,500 or less from files within 45 days of their being paid or settled. The bill is co-sponsored by Representative Heath Shuler (D-N.C.), Don Manzullo (R-Ill.) and Ralph M. Hall (R-Texas). As a general rule, expunging predictive information is not in the best interest of consumers or credit granters -- both of which benefit when credit reports and scores are as accurate and predictive as possible. If any type of debt information proven to be predictive is expunged, consumers risk exposure to improper credit products as they may appear to be more financially equipped to handle new debt than they truly are. Medical debts are never taken into consideration by VantageScore® Solutions LLC if the debt reporting is known to be from a medical facility. When a medical debt is outsourced to a third-party collection agency, it is treated the same as other debts that are in collection. Collection accounts of lower than $250, or ones that have been settled, have less impact on a consumer’s VantageScore® credit score. With or without the medical debt in collection information, the VantageScore® credit score model remains highly predictive.

With the raising of the U.S. debt ceiling and its recent ramifications consuming the headlines over the past month, I began to wonder what would happen if the general credit consumer had made a similar argument to their credit lender. Something along the lines of, “Can you please increase my credit line (although I am maxed out)? I promise to reduce my spending in the future!” While novel, probably not possible. In fact, just the opposite typically occurs when an individual begins to borrow up to their personal “debt ceiling.” When the amount of credit an individual utilizes to what is available to them increases above a certain percentage, it can adversely affect their credit score, in turn affecting their ability to secure additional credit. This percentage, known as the utility rate is one of several factors that are considered as part of an individual’s credit score calculation. For example, the utilization rate makes up approximately 23% of an individual’s calculated VantageScore® credit score. The good news is that consumers as a whole have been reducing their utilization rate on revolving credit products such as credit cards and home equity lines (HELOCs) to the lowest levels in over two years. Bankcard and HELOC utilization is down to 20.3% and 49.8%, respectively according to the Q2 2011 Experian – Oliver Wyman Market Intelligence Reports. In addition to lowering their utilization rate, consumers are also doing a better job of managing their current debt, resulting in multi-year lows for delinquency rates as mentioned in my previous blog post. By lowering their utilization and delinquency rates, consumers are viewed as less of a credit risk and become more attractive to lenders for offering new products and increasing credit limits. Perhaps the government could learn a lesson or two from today’s credit consumer.

By: Tracy Bremmer Score migration has always been a topic of interest among financial institutions. I can remember doing score migration analyses as a consultant at Experian for some of the top financial institutions as far back as 2004, prior to the economic meltdown. Lenders were interested in knowing if I could approve a certain number of people above a particular cut-off, and how many of them will be below that cutoff within five or more years. Or conversely, of all the people I’ve rejected because they were below my cut-off, how many of them would have qualified a year later or maybe even qualified the following month. We’ve done some research recently to gain a better understanding of the impact of score migration, given the economic downturn. What we found was that in aggregate, there is not a ton of change going on. Because as consumers move up or down in their score, the overall average shift tends to be minimal. However, when we’ve tracked this on a quarterly basis into score bands or even at a consumer level, the shift is more meaningful. The general trend is that the VantageScore® credit score “A” band, or best scorers, has been shrinking over time, while the VantageScore® credit score “D” & “F” bands, lower scorers, has grown over time. For instance, in 2010 Q4, the amount of consumers in VantageScore® credit score A was the lowest it has been in the past three years. Conversely, the number of consumers falling into the VantageScore® credit score “D” & “F” bands are the highest they have been during that same time period. This constant shift in credit scores, driven by changes in a consumer’s credit file, can impact risk levels beyond the initial point of applicant approval. For this reason, we recommend updating and refreshing scores on a very regular basis, along with regular scorecard monitoring, to ensure that risk propensity and the offering continue to be appropriately aligned with one another.

By: Staci Baker There has been a lot of talk in the news about the Dodd-Frank Act lately. According to the Dodd-Frank Resource Center of the American Financial Services Association (AFSA), “The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010, which passed on July 21, 2010, is unprecedented in magnitude, and will impact every sector of the financial services industry.”  The aim of the Act is to put measures in place that address the issues that led to the financial crisis. This is done by setting up new regulatory bodies, and limiting the dealings of banks and other financial institutions. For the purpose of this blog, I will focus on describing the new regulatory agencies.  The Bureau of Consumer Financial Protection (CFPB), is an independent watchdog housed within the Federal Reserve. The CFPB has the authority to “regulate consumer financial products and services in compliance with federal law.”[ii] They are responsible for the accuracy of information, hidden fees and deceptive practices for consumers from within the following industries – mortgage, credit cards and other financial products. The Financial Stability Oversight Council is “charged with identifying threats to the financial stability of the United States, promoting market discipline, and responding to emerging risks to the stability of the United States financial system.”ii Through the Treasury, this council will create a new Office of Financial Research, which will be responsible for collecting and analyzing data to identify and monitor emerging risks to the economy, and publish the findings in periodic reports.  These new regulatory agencies are critical to US business processes, as they will more closely monitor business practices, create new tighter legislation, and report findings to the public. The legislation that is created will decrease risk levels posed by large, complex companies, as well as address discrepancy that has been raised throughout the financial crisis.     What are your views of the Dodd-Frank Act? Do you believe this is the legislation needed to stem future financial crisis? If not, what would help you and your business?  

By: Kari Michel How are your generic or custom models performing? As a result of the volatile economy, consumer behavior has changed significantly over the last several years and may have impacted the predictiveness of your models. Credit models need to monitored regularly and updated periodically in order to remain predictive. Let’s take a look at VantageScore, it was recently redeveloped using consumer behavioral data reflecting the volatile economic environment of the last few years. The development sample was compiled using two performance timeframes: 2006 – 2008, and 2007 – 2009, with each contributing 50% of the development sample. This is a unique approach and is unlike traditional score development methodology, which typically uses a single, two year time window. Developing models with data over an extended window reduces algorithm sensitivity to highly volatile behavior in a single timeframe. Additionally, the model is more stable as the development is built on a broader range of consumer behaviors. The validation results show VantageScore 2.0 outperforms VantageScore 1.0 by 3% for new accounts and 2% for existing accounts overall. To illustrate the differences that were seen in consumer behavior, the following chart and table show the consumer characteristics that contribute to a consumer’s score and compare the characteristic contributions of VantageScore 2.0 vs VantageScore 1.0. Payment History Utilization Balances Length of Credit Recent Credit Available Credit Vantage Score 2.0 28% 23% 9% 8% 30% 1% Vantage Score 1.0 32% 23% 15% 13% 10% 7% As we expect ‘payment history’ is a large portion driving the score, 28% for VantageScore 2.0 and 32% for VantageScore 1.0. What is interesting to see is the ‘recent credit’ contribution has increased significantly to 30% from 10%. There also is a shift with lower emphases on balances, 9% versus 15% as well as ‘length of credit’, 8% versus 13%. As you can see, consumer behavior changes over time and it is imperative to monitor and validate your scorecards in order to assess if they are producing the results you expect. If they are not, you may need to redevelop or switch to a newer version of a generic model.

With the recent release of first-time unemployment applications by the Labor Department showing weaker than expected results, it comes as no surprise that July foreclosure rates  also reflect the on-going stress being experienced by consumers across the nation. When considering credit score trends and delinquency measures across credit products, it’s interesting to see how these trends appear to be playing out in terms of their impact on consumer score migration patterns. Over the past year or so, it appears that the impact of a struggling economy is the creation of a two-tier consumer credit system. On one hand, for consumers with stronger credit risk scores who are able to successfully manage their financial obligations, we see stability in the composition of the prime and super-prime population. On the other hand, as other consumers face challenging times, especially through joblessness and reductions in real-estate equity, there are consumers who experience significant credit management issues and subsequently, their risk scores decline. The interesting phenomenon is that there seems to be fewer and fewer consumers who remain in between these two segments. Credit score migration patterns suggest the evolution of two distinct consumer populations: a relatively stable, lower-risk segment, and a somewhat bottom-heavy higher-risk population, comprised of consumers with long-term repayment challenges, recent foreclosures, repossessions and higher delinquency rates. Clearly, this type of change in score distribution directly impacts lenders and their acquisition and account management strategies. With few signs of a pending economic recovery, it will be interesting to watch this pattern develop in the long-term to see if the chasm between these groups becomes wider and more measurable, or whether other economic influences will further transform the consumer credit landscape.

By: Kari Michel The Federal Reserve’s decision to permit card issuers to use income estimation models to meet the Accountability, Responsibility, and Disclosure (CARD) Act requirements to assess a borrower’s ability to repay a loan makes good sense. But are income estimation models useful for anything other than supporting compliance with this new regulation? Yes; in fact these types of models offer many advantages and uses for the financial industry. They provide a range of benefits including better fraud mitigation, stronger risk management, and responsible provision of credit. Using income estimation models to understand your customers’ complete financial picture is valuable in all phases of the customer lifecycle, including: • Loan Origination – use as a best practice for determining income capacity • Prospecting – target customers within a specific income range • Acquisitions – set line assignments for approved customers • Account Management – assess repayment ability before approving line increases • Collections – optimize valuation and recovery efforts One of the key benefits of income estimation models is they validate consumer income in real time and can be easily integrated into current processes to reduce expensive manual verification procedures and increase your ROI. But not all scoring models are created equal. When considering an income estimation model, it’s important to consider the source of the income data upon which the model was developed. The best models rely on verified income data and cover all income sources, including wages, rent, alimony, and Social Security. To lean more about how income estimation models can help with risk management strategies, please join the following webinar: Ability to pay:  Going beyond the Credit CARD on June 8, 2010. http://www.bulldogsolutions.net/ExperianConsumerInfo/EXC1001/frmRegistration.aspx?bdls=24143    

A common request for information we receive pertains to shifts in credit score trends. While broader changes in consumer migration are well documented – increases in foreclosure and default have negatively impacted consumer scores for a group of consumers – little analysis exists on the more granular changes between the score tiers. For this blog, I conducted a brief analysis on consumers who held at least one mortgage, and viewed the changes in their score tier distributions over the past three years to see if there was more that could be learned from a closer look. I found the findings to be quite interesting. As you can see by the chart below, the shifts within different VantageScore® credit score tiers shows two major phases. Firstly, the changes from 2007 to 2008 reflect the decline in the number of consumers in VantageScore® credit score tiers B, C, and D, and the increase in the number of consumers in VantageScore® credit score tier F. This is consistent with the housing crisis and economic issues at that time. Also notable at this time is the increase in VantageScore® credit score tier A proportions. Loan origination trends show that lenders continued to supply credit to these consumers in this period, and the increase in number of consumers considered ‘super prime’ grew. The second phase occurs between 2008 and 2010, where there is a period of stabilization for many of the middle-tier consumers, but a dramatic decline in the number of previously-growing super-prime consumers. The chart shows the decline in proportion of this high-scoring tier and the resulting growth of the next highest tier, which inherited many of the downward-shifting consumers. I find this analysis intriguing since it tends to highlight the recent patterns within the super-prime and prime consumer and adds some new perspective to the management of risk across the score ranges, not just the problematic subprime population that has garnered so much attention. As for the true causes of this change – is unemployment, or declining housing prices are to blame? Obviously, a deeper study into the changes at the top of the score range is necessary to assess the true credit risk, but what is clear is that changes are not consistent across the score spectrum and further analyses must consider the uniqueness of each consumer.

By: Kari Michel This blog completes my discussion on monitoring new account decisions with a final focus: scorecard monitoring and performance.  It is imperative to validate acquisitions scorecards regularly to measure how well a model is able to distinguish good accounts from bad accounts. With a sufficient number of aged accounts, performance charts can be used to: • Validate the predictive power of a credit scoring model; • Determine if the model effectively ranks risk; and • Identify the delinquency rate of recently booked accounts at various intervals above and below the primary cutoff score. To summarize, successful lenders maximize their scoring investment by incorporating a number of best practices into their account acquisitions processes: 1. They keep a close watch on their scores, policies, and strategies to improve portfolio strength. 2. They create monthly reports to look at population stability, decision management, scoring models and scorecard performance. 3. They update their strategies to meet their organization’s profitability goals through sound acquisition strategies, scorecard monitoring and scorecard management.

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.

We know that financial institutions are tightening their credit standards for lending.  But we don’t necessarily know exactly how financial institutions are addressing portfolio risk management -- how they are going about tightening those standards. As a commercial lender, when the economy was performing well, I found it much easier to get a loan request approved even if it did not meet typical standards.  I just needed to provide an explanation as to why a company’s financial performance was sub-par and what changes the company had made to address that performance -- and my deal was approved. When the economy started to decline, standards were suddenly elevated and it became much more difficult to get deals approved.  For example, in good times, credits with a 1.1:1 debt service coverage could be approved; when times got tough – and that 1.1:1 was no longer acceptable – the coverage had to be 1.25:1 or higher. Let’s consider this logic.  When times are good, we loosen our standards and allow poorer performing businesses’ loan requests to be approved…and when times are bad we require our clients perform at much higher standards.  Does this make sense?  Obviously not.  The reality is that when the economy is performing well, we should hold our borrowers to higher standards.  When times are worse, more leniency in standards may be appropriate, keeping in mind, of course, appropriate risk management measures. As we tighten our credit belts, let’s not choke out our potentially good customers.  In the same respect, once times are good, let’s not get so loose regarding our standards that we let in weak credits that we know will be a problem when the economy goes south.