Tag: credit scoring

New challenges created by the COVID-19 pandemic have made it imperative for utility providers to adapt strategies and processes that preserve positive customer relationships. At the same time, they must ensure proper individualized customer treatment by using industry-specific risk scores and modeled income options at the time of onboarding As part of our ongoing Q&A perspective series, Shawn Rife, Experian’s Director of Risk Scoring, sat down with us to discuss consumer trends and their potential impact on the onboarding process. Q: Several utility providers use credit scoring to identify which customers are required to pay a deposit. How does the credit scoring process work and do traditional credit scores differ from industry-specific scores? The goal for utility providers is to onboard as many consumers as possible without having to obtain security deposits. The use of traditional credit scoring can be key to maximizing consumer opportunities. To that end, credit can be used even for consumers with little or no past-payment history in order to prove their financial ability to take on utility payments. Q: How can the utilities industry use consumer income information to help identify consumers who are eligible for income assistance programs? Typically, income information is used to promote inclusion and maximize onboarding, rather than to decline/exclude consumers. A key use of income data within the utility space is to identify the eligibility for need-based financial aid programs and provide relief to the consumers who need it most. Q: Many utility providers stop the onboarding process and apply a larger deposit when they do not get a “hit” on a certain customer. Is there additional data available to score these “no hit” customers and turn a deposit into an approval? Yes, various additional data sources that can be leveraged to drive first or second chances that would otherwise be unattainable. These sources include, but are not limited to, alternative payment data, full-file public record information and other forms of consumer-permissioned payment data. Q: Have you noticed any employment trends due to the COVID-19 pandemic? How can those be applied at the time of onboarding? According to Experian’s latest State of the Economy Report, the U.S. labor market continues to have a slow recovery amidst the current COVID-19 crisis, with the unemployment rate at 7.9% in September. While the ongoing effects on unemployment are still unknown, there’s a good chance that several job/employment categories will be disproportionately affected long-term, which could have ramifications on employment rates and earnings. To that end, Experian has developed exclusive capabilities to help utility providers identify impacted consumers and target programs aimed at providing financial assistance. Ultimately, the usage of income and employment/unemployment data should increase in the future as it can be highly predictive of a consumer’s ability to pay For more insight on how to enhance your collection processes and capabilities, watch our Experian Symposium Series event on-demand. Watch now Learn more About our Experts: Shawn Rife, Director of Risk Scoring, Experian Consumer Information Services, North America Shawn manages Experian’s credit risk scoring models while empowering clients to maximize the scope and influence of their lending universe. He leads the implementation of alternative credit data within the lending environment, as well as key product implementation initiatives.

There are more than 100 million people in the United States who don’t have a fair chance at access to credit. These people are forced to rely on high-interest credit cards and loans for things most of us take for granted, like financing a family car or getting an apartment. At Experian, we have a fundamental mission to be a champion for the consumer. Our commitment to increasing financial inclusion and helping consumers gain access to the financial services they need is one of the reasons we have been selected as a Fintech Breakthrough Award winner for the third consecutive year. The Fintech Breakthrough Awards is the premier awards program founded to recognize the fintech innovators, leaders and visionaries from around the world. The 2020 Fintech Breakthrough Award program attracted more than 3,750 nominations from across the globe. Last year, Experian took home the award for Best Overall Analytics Platform for our Ascend Analytical Sandbox™, a first-to-market analytics environment that promised to move companies beyond just business intelligence and data visualization to data insights and answers they could use. The year prior, Experian won the Consumer Lending Innovation Award for our Text for Credit™ solution, a powerful tool for providing consumers the convenience to securely bypass the standard-length ‘pen & paper’ or keystroke intensive credit application process while helping lenders make smart, fraud protected lending decisions. This year, we are excited to announce that Experian has been selected once again as a winner in the Consumer Lending Innovation category for Experian Boost™. Experian Boost – with direct, active consumer consent – scans eligible accounts for ‘boostable’ positive payment data (e.g., utility and telecom payments) and provides the means for consumers to add that data to their Experian credit reports. Now, for the very first time, millions of consumers benefit from payments they’ve been making for years but were never reflected on their credit reports. Since launching in March 2019, cumulatively, more than 18 million points have been added to FICO® Scores via Experian Boost. Two-thirds of consumers who completed the Experian Boost process increased their FICO Score and among these, the average score increase has been more than 13 points, and 12% have moved up in credit score category. “Like many fintechs, our goal is to help more consumers gain access to the financial services they need,” said Alex Lintner, Group President of Experian Consumer Information Services. “Experian Boost is an example of our mission brought to life. It is the first and only service to truly put consumers in control of their credit. We’re proud of this recognition from Fintech Breakthrough and the momentum we’ve seen with Experian Boost to date.” Contributing consumer payment history to an Experian credit file allows fintech lenders to make more informed decisions when examining prospective borrowers. Only positive payment histories are aggregated through the platform and consumers can remove the new data at any time. There is no limit to how many times one can use Experian Boost to contribute new data. For more information, visit Experian.com/Boost.  

A few months ago, I got a letter from the DMV reminding me that it was finally time to replace my driver’s license. I’ve had it since I was 21 and I’ve been dreading having to get a new one. I was especially apprehensive because this time around I’m not just getting a regular old driver’s license, I’m getting a REAL ID. For those of you who haven’t had this wonderful experience yet, a REAL ID is the new form of driver’s license (or state ID) that you’ll need to board a domestic flight starting October 1, 2020. Some states already offered compliant IDs, but others—like California, where I’m from—didn’t. This means that if I want to fly within the U.S. using my driver’s license next year, I can’t renew by mail. It’s Easier Than It Looks Imagine my surprise when I started the process to schedule my appointment, and the California DMV website made things really easy! There’s an online application you can fill out before you get to the DMV and they walk you through the documents to bring to the appointment (which I was able to schedule online). Despite common thought that the DMV and agencies like it are slow to adopt technology, the ease of this process may indicate a shift toward a digital-first mindset. As financial institutions embrace a similar shift, they’ll be better positioned to meet the needs of customers. Case in point, the electronic checklist the DMV provided to prepare me for my appointment. I sailed through the first two parts of the checklist, confirming that I’ll bring my proof of identity and social security number, but I paused when I got to the “Two Proofs of Residency” screen. Like many people my age—read: 85% of the millennial population, according to a recent Experian study—I don’t have a mortgage or any other documents relating to property ownership. I also don’t have my name on my utilities (thanks, roomie) or my cell phone bill (thanks Mom). I do however have a signed lease with my name on it, plus my renter’s insurance, both of which are acceptable as proof of residency. And just like that, I’m all set to get my REAL ID, even though I don’t have some of the basic adulting documents you might expect, because the DMV took into account the fact that not all REAL ID applicants are alike. Imagine if lenders could adopt that same flexibility and create opportunities for the more than 45 million U.S. consumers1 who lack a credit report or have too little information to generate a credit score. The Bigger Picture By removing some of the usual barriers to entry, the DMV made the process of getting my REAL ID much easier than it might have been and corrected my assumptions about how difficult the process would be. Experian has the same line of thought when it comes to helping you determine whether a borrower is credit-worthy. Just because someone doesn’t have a credit card, auto loan or other traditional credit score contributor doesn’t mean they should be written off. That’s why we created Experian BoostTM, a product that lets consumers give read-only access to their bank accounts and add in positive utility and telecommunications bill payments to their credit file to change their scores in real time and demonstrate their stability, ability and willingness to repay. It’s a win-win for lenders and consumers. 2 out of 3 users of Experian Boost see an increase in their FICO Score and of those who saw an increase, 13% moved up a credit tier. This gives lenders a wider pool to market to, and thanks to their improved credit scores, those borrowers are eligible for more attractive rates. Increasing your flexibility and removing barriers to entry can greatly expand your potential pool of borrowers without increasing your exposure to risk. Learn more about how Experian can help you leverage alternative credit data and expand your customer base in our 2019 State of Alternative Data Whitepaper. Read Full Report 1Kenneth P. Brevoort, Philipp Grimm, Michelle Kambara. “Data Point: Credit Invisibles.” The Consumer Financial Protection Bureau Office of Research. May 2015.

The 1990s brought us a wealth of innovative technology, including the Blackberry, Windows 98, and Nintendo. As much as we loved those inventions, we moved on to enjoy better technology when it became available, and now have smartphones, Windows 10 and Xbox. Similarly, technological and modeling advances have been made in the credit scoring arena, with new software that brings significant benefits to lenders who use them. Later this year, FICO will retire its Score V1, making it mandatory for those lenders still using the old software to find another solution. Now is the time for lenders to take a look at their software and myriad reasons to move to a modern credit score solution. Portfolio Growth As many as 70 million Americans either have no credit score or a thin credit file. One-third of Millennials have never bothered to apply for a credit card, and the percentage of Americans under 35 with credit card debt is at its lowest level in more than 25 years, according to the Federal Reserve. A recent study found that Millennials use cash and debit cards much more than older Americans. Over time, Millennials without credit histories could struggle to get credit. Are there other data sets that provide a window into whether a thin file consumer is creditworthy or not? Modern credit scoring models are now being used in the marketplace without negatively impacting credit quality. For example, VantageScore 3.0 allows for the scoring of 30 million to 35 million more people consumers who are typically unscoreable by other traditional generic credit models. VantageScore 3.0 does this by using a broader, deeper set of credit file data and more advanced modeling techniques. This allows the VantageScore model to more accurately predict unique consumer behaviors—is the consumer paying his utility bill on time?—and better evaluate thin file consumers. Mitigate Risk In today’s ever-changing regulatory landscape, lenders can stay ahead of the curve by relying on innovative credit score models like VantageScore. These models incorporate the best of both worlds by leaning on innovative scoring analytics that are more inclusive, while providing marketplace lenders with assurances the decisioning is both statistically sound and compliant with fair lending laws. Newer solutions also offer enhanced documentation to ease the burden associated with model risk management and regulatory compliance responsibilities. Updated scores Consumer credit scores can vary depending on the type of scoring model a lender uses. If it's an old, outdated version, a consumer might be scored lower. If it's a newer, more advanced model, the consumer has a better shot at being scored more fairly. Moving to a more advanced scoring model can help broaden the base of potential borrowers. By sticking to old models—and older scores—a sizable number of consumers are left at a disadvantage in the form of a higher interest rate, lower loan amount or even a declined application. Introducing advanced scoring models can provide a more accurate picture of a consumer. As an example, for many of the newest consumer risk models, like FICO Score 9, a consumer’s unpaid medical collection agency accounts will be assessed differently from unpaid non-medical collection agency accounts. This isn't true for most pre-2012 consumer risk score versions. Each version contains different nuances for increasing your score, and it’s important to understand what they are. Upgrading your credit score to the latest VantageScore credit score or FICO solution is easier than you think, with a switch to a modern solution taking no longer than eight weeks and your current business processes still in place. Are you ready to reap the rewards of modern credit scoring?

Whether its new regulations and enforcement actions from the Consumer Financial Protection Bureau or emerging legislation in Congress, the public policy environment for consumer and commercial credit is dynamic and increasingly complex. If you are interested to learn more about how to navigate an increasingly choppy regulatory environment, consider joining a breakout session at Experian’s Vision 2016 Conference that I will be moderating. I’ll be joined by several experts and practitioners, including: John Bottega, Enterprise Data Management Conor French, Funding Circle Troy Dennis, TD Bank Don Taylor, President, Automated Collection Services During our session, you’ll learn about some of the most trying regulatory issues confronting the consumer and commercial credit ecosystem. Most importantly, the session will look at how to turn potential challenges into opportunities. This includes learning how to incorporate new alternative data sets into credit scoring models while still ensuring compliance with existing fair lending laws. We’ll also take a deep dive into some of the coming changes to debt collection practices as a result of the CFPB’s highly anticipated rulemaking. Finally, the panel will take a close look at the challenges of online marketplace lenders and some of the mounting regulations facing small business lenders. Learn more about Vision 2016 and how to register for the May conference.

Whether it is an online marketplace lender offering to refinance the student loan debt of a recent college graduate or an online small-business lender providing an entrepreneur with a loan when no one else will, there is no doubt innovation in the online lending sector is changing how Americans gain access to credit. This expanding market segment takes great pride in using “next-generation” underwriting and credit scoring risk models. In particular, many online lenders are incorporating noncredit information such as income, education history (i.e., type of degree and college), professional licenses and consumer-supplied information in an effort to strike the right balance between properly assessing credit risk and serving consumers typically shunned by traditional lenders because of a thin credit history. Regulatory concerns The exponential growth of the online lending sector has caught the attention of regulators — such as the U.S. Treasury Department, the Federal Deposit Insurance Corporation, Congress and the California Business Development Office — who are interested in learning more about how online marketplace lenders are assessing the credit risk of consumers and small businesses. At least one official, Antonio Weiss, a counselor to the Treasury secretary, has publicly raised concerns about the use of so-called nontraditional data in the underwriting process, particularly data gleaned from social media accounts. Weiss said that “just because a credit decision is made by an algorithm, doesn’t mean it is fair,” citing the need for lenders to be aware of compliance with fair lending obligations when integrating nontraditional credit data. Innovative and “tried and true” are not mutually exclusive Some have suggested the only way to assuage regulatory concerns and control risk is by using tried-and-true legacy credit risk models. The fact is, however, online marketplace lenders can — and should — continue to push the envelope on innovative underwriting and business models, so long as these models properly gauge credit risk and ensure compliance with fair lending rules. It’s not a simple either-or scenario. Lenders always must ensure their scoring analytics are based upon predictive and accurate data. That’s why lenders historically have relied on credit history, which is based upon data consumers can dispute using their rights under the Fair Credit Reporting Act. Statistically sound and validated scores protect consumers from discrimination and lenders from disparate impact claims under the Equal Credit Opportunity Act. The Office of the Comptroller of the Currency guidance on model risk management is an example of regulators’ focus on holding responsible the entities they oversee for the validation, testing and accuracy of their models. Marketplace lenders who want to push the limit can look to credit scoring models now being used in the marketplace without negatively impacting credit quality or raising fair lending risk. For example, VantageScore® 3.0 allows for the scoring of 30 million to 35 million more people who currently are unscoreable under legacy credit score models. VantageScore does this by using a broader, deeper set of credit file data and more advanced modeling techniques. This allows the VantageScore model to capture unique consumer behaviors more accurately. In conclusion, online marketplace lenders should continue innovating with their own “secret sauce” and custom decisioning systems that may include a mix of noncredit factors. But they also can stay ahead of the curve by relying on innovative “tried-and-true” credit score models, like VantageScore 3.0. These models incorporate the best of both worlds by leaning on innovative scoring analytics that are more inclusive, while providing marketplace lenders with assurances the decisioning is both statistically sound and compliant with fair lending laws. VantageScore® is a registered trademark of VantageScore Solutions, LLC.

Every portfolio has a set of delinquent customers who do not make their payments on time. Truth. Every lender wants to collect on those payments. Truth. But will you really ever be able to recover all of those delinquent funds? Sadly, no. Still, financial institutions often treat all delinquent customers equally, working the account the same and assuming eventually they’ll get their funds. The sentiment to recover is good, but a lot of collection resources are wasted on customers who are difficult or impossible to recover. The good news? There is a better way. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective accounts to prioritize to your best collectors, do not contact and proceed to legal actions to significantly increase the recovery of dollars, and at the same time reduce collection costs. I had the opportunity to recently present at the annual Debt Buyer Association’s International Conference and chat with my peers about this very topic. We asked the room, “How many of you are using scoring to determine how to work your collection accounts?” The response was 50/50, revealing many of these well-intentioned collectors are working themselves too hard, and likely not getting the desired returns. Before you dive into your collections work, you need to respond to two questions: Which accounts am I going to work first? How am I going to work those accounts? This is where scoring enters the scene. A scoring model is a statistical algorithm that assigns a numerical expression based on known information to predict an unknown future outcome. You can then use segmentation to group individuals with others that show the same behavior characteristics and rank order groups for collection strategies. In short, you allow the score to dictate the collection efforts and slope your expenses based on the propensity and expected amount of the consumer to pay. This will inform you on: What type, if any, skip trace tactic you should use? If you should purchase additional data? What intensity you should work the account? With scoring, you will see different performances on different debts. If you have 100 accounts you are collecting on, you’ll then want to find the accounts where you will have the greatest likelihood to collect, and collect the most dollars. I like to say, “You can’t get blood from a stone.” Well the same holds true for certain accounts in your collections pile. Try all you like, but you’ll never recoup those dollars, or the dollars you do recoup will be minimal. With a scoring strategy, you can establish your “hit list” and find the most attractive accounts to collect on, and also match your most profitable accounts with your best collectors. My message to anyone managing a collections portfolio can be summed up in three key messages. You need to use scoring in your business to optimize resources and increase profits. The better data that goes into your model will net you better performance results. Get a compliance infrastructure in place so you can ensure you are collecting the right way and stay out of trouble. The beauty of scores is they tell you what to do. It will help you best match resources to the most profitable accounts, and work smarter, not harder. That’s the power of scoring.

By: Teri Tassara In my blog last month, I covered the importance of using quality credit attributes to gain greater accuracy in risk models.  Credit attributes are also powerful in strengthening the decision process by providing granular views on consumers based on unique behavior characteristics.  Effective uses include segmentation, overlay to scores and policy definition – across the entire customer lifecycle, from prospecting to collections and recovery. Overlay to scores – Credit attributes can be used to effectively segment generic scores to arrive at refined “Yes” or “No” decisions.  In essence, this is customization without the added time and expense of custom model development.  By overlaying attributes to scores, you can further segment the scored population to achieve appreciable lift over and above the use of a score alone. Segmentation – Once you made your “Yes” or “No” decision based on a specific score or within a score range, credit attributes can be used to tailor your final decision based on the “who”, “what” and “why”.  For instance, you have two consumers with the same score. Credit attributes will tell you that Consumer A has a total credit limit of $25K and a BTL of 8%; Consumer B has a total credit limit of $15K, but a BTL of 25%.   This insight will allow you to determine the best offer for each consumer. Policy definition - Policy rules can be applied first to get the desirable universe.  For example, an auto lender may have a strict policy against giving credit to anyone with a repossession in the past, regardless of the consumer’s current risk score. High quality attributes can play a significant role in the overall decision making process, and its expansive usage across the customer lifecycle adds greater flexibility which translates to faster speed to market.  In today’s dynamic market, credit attributes that are continuously aligned with market trends and purposed across various analytical are essential to delivering better decisions.  

As a scoring manager, this question has always stumped me because there was never a clear answer. It simply meant less than prime – but how much less? What does the term actually mean? How do you quantify something so subjective? Do you assign it a credit score? Which one? There were definitely more questions than answers. But a new proposed ruling from the FDIC could change all that – at least when it comes to large bank pricing assessments. The proposed ruling does a couple of things to bring clarity to the murky waters of the subprime definition. First, it replaces the term “subprime” with “high-risk consumer loans”. Then they go one better: they quantify high-risk as having a 20% probability of default or higher. Finally, something we can calculate! The arbitrary 3-digit credit score that has been used in the past to define the line between prime and subprime has several flaws. First of all, if a subprime loan is defined as having any particular credit score, it has to be for a specific version of a specific model at a specific time. That’s because the default rates associated to any given score is relative to the model used to calculate it. There are hundreds of custom-build and generic scoring models in use by lenders today – does that single score represent the same level of risk to all of them? Absolutely not. And even if all risk models were calibrated exactly the same, just assigning credit risk a number has no real meaning over time. We all know what scores shift, that consumer credit behavior is not the same today as it was just 6 years ago. In 2006, if a score of X represented a 15% likelihood of default, that same score today could represent 20% or more. It is far better to align a definition of risk with its probability of default to begin with! While it only currently applies to the large bank pricing assessments with the FDIC, this proposed ruling is a great step in the right direction. As this new approach catches on, we may see it start to move into other polices and adopted by various organizations as they assess risk throughout the lending cycle.

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 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  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. With or without the medical debt in collection information, the VantageScore 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. 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 “A” band, or best scorers, has been shrinking over time, while the VantageScore “D” & “F” bands, lower scorers, has grown over time. For instance, in 2010 Q4, the amount of consumers in VantageScore A was the lowest it has been in the past three years. Conversely, the number of consumers falling into the VantageScore “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.