Credit Lending
By: Wendy Greenawalt US interest rates are at historically low levels, and while many Americans are taking advantage of the low interest rates and refinancing their mortgages, a great deal more are struggling to find jobs, and unable to take advantage of the rate- friendly lending environment. This market however, continues to be complex as lenders try to competitively price products while balancing dynamic consumer risk levels, multiple product options and minimize the cost of acquisition. Due to this, lenders need to implement advanced risk-based pricing strategies that will balance the uncertain risk profiles of consumers while closely monitoring long-term profitability as re-pricing may not be an option given recent regulatory guidelines. Risk-based pricing has been a hot topic recently with the Credit Card Act and Risk-Based Pricing Rule regulation and pending deadline. For lenders who have not performed a new applicant scorecard validation or detailed portfolio analysis in the last few years now is the time to review pricing strategies and portfolio mix. This analysis will aid in maintaining an acceptable risk level as the portfolio evolves with new consumers and risk tiers while ensuring short and long-term profitability and on-going regulatory compliance. At its core, risk-based pricing is a methodology that is used to determine the what interest rate should be charged to a consumer based on the inherent risk and profitability present within a defined pricing tier. By utilizing risk-based pricing, organizations can ensure the overall portfolio is profitable while providing competitive rates to each unique portfolio segment. Consistent review and strategy modification is crucial to success in today’s lending environment. Competition for the lowest risk consumers will continue to increase as qualified candidate pools shrink given the slow economic recovery. By reviewing your portfolio on a regular basis and monitoring portfolio pricing strategies closely an organization can achieve portfolio growth and revenue objectives while monitoring population stability, portfolio performance and future losses.
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.
Recently, a number of media articles have discussed the task facing financial institutions today – find opportunities growth in a challenging and flat economy. The majority of perspectives discuss the fact that lenders will soon have no choice but to look to the ‘fringe’, by lowering score cut-offs, adjusting acquisition strategies and introducing greater risk into their portfolios in order to grow. Risk and marketing departments are sure to be creating and analyzing credit risk models and assessing credit risk in new, untapped markets in order to achieve these objectives. While it may appear to be oversimplifying the task, many lenders have the opportunity to grow simply by understanding more about two groups of consumers that are already sitting in their offices (or application queues) today: applicants who are approved, but book elsewhere, and applicants that are declined. There are a number of analytic techniques that can be utilized to understand these populations further. Lenders can study the characteristics of other loans originated by these lost consumers, and can also perform analyses of how these consumers performed after booking competitive offers. By understanding the credit characteristics and account delinquency trends of its current applicants, lenders can uncover a wealth of information and insight about the growth opportunities sitting right before them.
By: Kari Michel What are your acquisition strategies to increase consumer lending and gain market share? This blog will discuss new approaches to create segment-based targeting campaigns and the ability to precisely time the offer delivery with consumer needs. The most aggressive and successful banks are using need and attitudinal segmentation, coupled with models that identify consumers in the market for loan products. The return on marketing investment from these refined marketing efforts often exceed 350%, measured on a net of control basis, after all marketing costs. Here is a case study, using Experian tools, showing how one marketer used segment-based targeting, tailoring and timing to increase their response rate 145% over a competitor’s product. In the highly competitive credit card arena, a new business model is emerging that is dependent on acquiring new accounts from consumers that are grouped into specific behavior segments (Credit Hungry Card Switchers and Case Oriented Skeptics) and looking at consumers that were in the market, as well as had the highest likelihood of opening a bankcard account within the next 1 – 4 months. Test Results Total Competitor Experian Experian lift Quantity 624,000 623,953 Response Rate % 2.09% 3.03% 145% Actual Responses 13,035 18,902 Booked Rate % 1.64% 2.24% 137% Actual Booked 10,208 13,989 Approval Rate % 78.30% 74.01% 95% In addition to a 145% lift in response rate, over 3,700 more accounts were booked over the competition. These same tools, “In The Market Models” (developed using credit bureau data) and “Financial Personalities®”, can help your organization have a greater return on your direct marketing investment by increasing acquisition rates.
By: Wendy Greenawalt The final provisions included in The Credit Card Act will go into effect on August 22, 2010. Most lenders began preparing for these changes some time ago, and may have already begun adhering to the guidelines. However, I would like to talk about the provisions included and discuss the implications they will have on credit card lenders. The first provision is the implementation of penalty fee guidelines. This clause prohibits card issuers from charging fees that exceed the consumer’s violation of the account terms. For example, if a consumer’s minimum monthly payment on a credit card account was $15, and the lender charges a $39 late fee, this would be considered excessive as the penalty is greater than the consumers’ obligation on that account. Going forward, the maximum fee a lender could charge in this example would be $15 or equal to the consumers obligation. In addition to late fee limitations, lenders can no longer charge multiple penalty fees based on a single late payment, other account term violations or fees for account inactivity. These limitations will have a dramatic impact on portfolio profitability, and lenders will need to account for this with all accounts going forward. The second major provision mandates that if a lender increased a consumer’s annual interest rate after January 1, 2009 due to credit risk, market conditions, or other factors, then the lender must maintain reasonable methodologies and perform account reviews no less than every 6 months. If during the account review, the credit risk, market conditions or other factors that resulted in the interest rate increase have changed, the lender must adjust the interest rate down if warranted. This provision only affects interest rate increases and does not supply specific terms on the amount of the interest rate reduction required; so lenders must assess this independently to determine their individual compliance requirements on covered accounts. The Credit Card Act was a measure to create better policies for consumers related to credit card accounts and overall will provide greater visibility and fair account practices for all consumers. However, The Credit Card Act places more pressure on lenders to find other revenue streams to make up for revenue that was previously received when accounts were not paid by the due date, fees and additional interest rate income were generated. Over the next few years, lenders will have to find ways to make up this shortcoming and generate revenue through acquisition strategies and/or new business channels in order to maintain a profitable portfolio. http://www.federalreserve.gov/newsevents/press/bcreg/20100303a.htm
US interest rates are at historically low levels, and while many Americans are taking advantage of the low interest rates and refinancing their mortgages, a great deal more are struggling to find jobs, and unable to take advantage of the rate- friendly lending environment. This market however, continues to be complex as lenders try to competitively price products while balancing dynamic consumer risk levels, multiple product options and minimize the cost of acquisition. Due to this, lenders need to implement advanced risk-based pricing strategies that will balance the uncertain risk profiles of consumers while closely monitoring long-term profitability as re-pricing may not be an option given recent regulatory guidelines. Risk-based pricing has been a hot topic recently with the Credit Card Act and Risk-Based Pricing Rule regulation and pending deadline. For lenders who have not performed a new applicant scorecard validation or detailed portfolio analysis in the last few years now is the time to review pricing strategies and portfolio mix. This analysis will aid in maintaining an acceptable risk level as the portfolio evolves with new consumers and risk tiers while ensuring short and long-term profitability and on-going regulatory compliance. At its core, risk-based pricing is a methodology that is used to determine the what interest rate should be charged to a consumer based on the inherent risk and profitability present within a defined pricing tier. By utilizing risk-based pricing, organizations can ensure the overall portfolio is profitable while providing competitive rates to each unique portfolio segment. Consistent review and strategy modification is crucial to success in today’s lending environment. Competition for the lowest risk consumers will continue to increase as qualified candidate pools shrink given the slow economic recovery. By reviewing your portfolio on a regular basis and monitoring portfolio pricing strategies closely an organization can achieve portfolio growth and revenue objectives while monitoring population stability, portfolio performance and future losses.
By: Kari Michel Credit risk models are used by almost every lender, and there are many choices to choose from including custom or generic models. With so many choices how do you know what is best for your portfolio? Custom models provide the strongest risk prediction and are developed using an organization’s own data. For many organizations, custom models may not be an option due to the size of the portfolio (may be too small), lack of data including not enough bads, time constraints, and/or lack of resources. If a custom model is not an option for your organization, generic bureau scoring models are a very powerful alternative for predicting risk. But how can you understand if your current scoring model is the best option for you? You may be using a generic model today and you hear about a new generic model, for example VantageScore. How do you determine if the new model is more predictive than your current model for your portfolio? The best way to understand if the new model is more predictive is to do a head-to-head comparison – a validation. A validation requires a sample of accounts from your portfolio including performance flags. An archive is pulled from the credit reporting agency and both scores are calculated from the same time period and a performance chart is created to show the comparison. There are two key performance metrics that are used to determine the strength of the model. The KS (Komogorov-Smirnov) is a statistical term that measures the maximum difference between the bad and good cumulative score distribution. The KS range is from 0% to 100%, with the higher the KS the stronger the model. The second measurement uses the bad capture rate in the bottom 5%, 10% or 15% of the score range. A stronger model will provide better risk prediction and allow an organization to make better risk decisions. Overall, when stronger scoring models are used, organizations will be best prepared to decrease their bad rates and have a more profitable portfolio.
By: Staci Baker With the increase in consumer behaviors such as ‘strategic default’, it has become increasingly difficult during the past few years for lenders to determine who the most creditworthy consumers are – defining consumers with the lowest credit risk. If you define risk as ‘the likelihood of [a consumer] becoming 90 days or more past due’, the findings are alarming. From June 2007 to June 2009, Super Prime consumers (those scoring 900 or higher) in the U.S. have gone from an average VantageScore* of 945 to 918, which increased their risk level from approx. 0.12% to 0.62% - an increase of 417% for this highly sought after population! Prime and near prime risk levels increased by 400% and 96% respectively. Whereas subprime consumers with few choices (stay subprime or improve their score), saw a slight decrease in risk, 8% - increasing their average VantageScore from 578 to 599. So how do lenders determine who to lend to, when the risk level for all credit tiers increases, or remain risky? In today’s dynamic economy, lenders need tools that will give them an edge, and allow them to identify consumer trends quickly. Incorporating analytic tools, like Premier Attributes, into lender’s origination models, will allow them to pinpoint specific consumer behavior, and provide segmentation through predefined attribute sets that are industry specific and target profitable accounts to improve acquisition strategies. As risk levels change, maintaining profitability becomes more difficult due to shrinking eligible consumer pools. By adding credit attributes, assessing credit risk both within an organization and for new accounts will be simplified and allow for more targeted prospects, thus maximizing prospecting strategies across the customer lifecycle and helping to increase profitability. * VantageScore®, LLC, May, 2010, “Finding Creditworthy Consumers in a Changing Economic Climate”
--By Wendy Greenawalt Recently the Federal Reserve Board and Federal Trade Commission issued a new rule requiring any lender who utilizes a credit report or score when making a credit decision to provide consumers with a risk-based pricing notice. The new regulation goes into effect on January 1, 2011, but lenders must begin the planning process now--as compliance will require potential changes to their current lending practices. The regulation is another evolution in an attempt to provide consumers with more visibility to their credit history and the impact a blemished record may have on their finances. The ruling is good for consumers, but will require lenders to modify existing lending processes and add another consumer disclosure, as well as additional costs to the lending process. The risk-based pricing rule provides lenders with two compliance options--the risk-based pricing notice or a credit score disclosure exception. In this blog, I will discuss the primary compliance option, the risk-based pricing notice. The risk-based pricing notice is a document that notifies consumers that the terms of their new credit account are materially less favorable than the most favorable terms. The notice will not be provided to all consumers, but rather just those that receive account terms that are worse than what is offered to the most credit worthy consumers. Determining who will receive the notice has been outlined in the rule, and lenders can use several options including the direct comparison, credit score proxy or tiered pricing method. For lenders that perform regular validation of their portfolio, determining which consumers to issue a notice to should not be difficult. However, for those lenders who do not perform regular scorecard performance monitoring, this is another reminder of the importance of on-going validations and monitoring. As the economy continues to recover and lenders begin to re-enter the market, it will be more important than ever to validate that scores are performing as expected to manage risk and revenue goals. In my next blog, I will discuss the credit score disclosure exception.
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
By: Kari Michel Credit quality deteriorated across the credit spectrum during the recession that began in December, 2007. As the recession winds down, lenders must start strategically assessing credit risk and target creditworthy consumer segments for lending opportunities, while avoiding those segments where consumer credit quality could continue to slip. Studies and analyses by VantageScore Solutions, LLC demonstrate that there are more than 60 million creditworthy borrowers in the United States - 7 million of whom cannot be identified using standard scoring models. Leveraging methods using VantageScore® in conjunction with consumer credit behaviors can effectively identify profitable opportunities and segments that require increased risk mitigation thus optimizing decisions. VantageScore Solutions examined how consumers credit scores changed over a 12 month period. The study focused on three areas of consumer behavior: Stable: consumers that stay within the same credit tier for one year Improving: consumers that move to a higher credit tier in any quarter and remain at a high credit tier for the remainder of the timeframe Deteriorating: consumers that move to a lower credit tier in any quarter and remain at a lower credit tier for the remainder of the timeframe Through a segmentation approach, using the three credit behaviors above and credit quality tiers, emerges a clearer picture into profitable segments for acquisitions and existing account management strategies. Download the white paper, “Finding creditworthy consumers in a changing economic climate”, for more information on finding creditworthy consumers from VantageScore Solutions. Lenders can use a similar segmentation analysis on their own population to identify pockets of opportunity to move beyond recession-based management strategies and intelligently re-enter into the world of originations and maximize portfolio profitability.
By: Wendy Greenawalt The auto industry has been hit hard by this Great Recession. Recently, some good news has emerged from the captive lenders, and the industry is beginning to rebound from the business challenges they have faced in the last few years. As such, many lenders are looking for ways to improve risk management and strategically grow their portfolio as the US economy begins to recover. Due to the economic decline, the pool of qualified consumers has shrunk, and competition for the best consumers has significantly increased. As a result, approval terms at the consumer level need to be more robust to increase loan origination and booking rates of new consumers. Leveraging optimized decisions is a way lenders can address regional pricing pressure to improve conversion rates within specific geographies. Specifically, lenders can perform a deep analysis of specific competitors such as captives, credit unions and banks to determine if approved loans are being lost to specific competitor segments. Once the analysis is complete, auto lenders can leverage optimization software to create robust pricing, loan amount and term account strategies to effectively compete within specific geographic regions and grow profitable portfolio segments. Optimization software utilizes a mathematical decisioning approach to identify the ideal consumer level decision to maximize organizational goals while considering defined constraints. The consumer level decisions can then be converted into a decision tree that can be deployed into current decisioning strategies to improve profitability and meet key business objectives over time.
By: Staci Baker With the shift in the economy, it has become increasingly more difficult to gauge -- in advance -- what a consumer is going to do when it comes to buying an automobile. However, there are tools available that allow auto lenders to gain insight into auto loans/leases that were approved but did not book, and for assessing credit risk of their consumers. By gaining competitive insight and improving risk management, an auto lender is able to positively impact loan origination strategies by determining the proper loan or lease term, what the finance offer should be and proactively address each unique market and risk segment. As the economy starts to rebound, the auto industry needs to take a more proactive approach in the way its members acquire business; the days of business-as-usual are gone. All factors except the length of the loan being the same, if one auto dealer is extending 60-month loans per its norm and the dealer down the road is extending 72-month loans, a consumer may choose the longer loan period to help conserve cash for other items. This is one scenario for which auto dealers could leverage Experian’s Auto Prospect Intelligence(SM). By performing a thorough analysis of approved loans that booked with other auto lenders, and their corresponding terms, auto lenders will receive a clear picture of who they are losing their loans to. This information will allow an organization to compare account terms within specific peer group or institution type (captive/banks/credit union) and address discrepancies by creating more robust pricing structures and enhanced loan terms, which will result in strategic portfolio growth.
Since 2007, when the housing and credit crises started to unfold, we’ve seen unemployment rates continue to rise (9.7% in March 2010 *) with very few indicators that they will return to levels that indicate a healthy economy any time soon. I’ve also found myself reading about the hardship and challenge that people are facing in today’s economy, and the question of creditworthiness keeps coming into my mind, especially as it relates to employment, or the lack thereof, by a consumer. Specifically, I can’t help but sense that there is a segment of the unemployed that will soon possess a better risk profile than someone who has remained employed throughout this crisis. In times of consistent economic performance, the static state does not create the broad range of unique circumstances that comes when sharp growth or decline occurs. For instance, the occurrence of strategic default is one circumstance where the capacity to pay has not been harmed, but the borrower defaults on the commitment anyway. Strategic defaults are rare in a stable market. In contrast, many unemployed individuals who have encountered unfortunate circumstances and are now out of work may have repayment issues today, but do possess highly desirable character traits (willingness to pay) that enhance their long-term desirability as a borrower. Although the use of credit score trends, credit risk modeling and credit attributes are essential in assessing the risk within these different borrowers, I think new risk models and lending policies will need to adjust to account for the growing number of individuals who might be exceptions to current policies. Will character start to account for more than a steady job? Perhaps. This change in lending policy, may in turn, allow lenders to uncover new and untapped opportunities for growth in segments they wouldn’t traditionally serve. * Source: US Department of Labor. http://www.bls.gov/bls/unemployment.htm
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 tiers shows two major phases. Firstly, the changes from 2007 to 2008 reflect the decline in the number of consumers in VantageScore B, C, and D, and the increase in the number of consumers in VantageScore F. This is consistent with the housing crisis and economic issues at that time. Also notable at this time is the increase in VantageScore 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.
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