Credit Lending

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 the VantageScore® credit score 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® 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: Wendy Greenawalt Optimization has become somewhat of a buzzword lately being used to solve all sorts of problems. This got me thinking about what optimizing decisions really means to me? In pondering the question, I decided to start at the beginning and really think about what optimization really stands for. For me, it is an unbiased mathematical way to determine the most advantageous solution to a problem given all the options and variables. At its simplest form, optimization is a tool, which synthesizes data and can be applied to everyday problems such as determining the best route to take when running errands. Everyone is pressed for time these days and finding a few extra minutes or dollars left in our bank account at the end of the month is appealing. The first step to determine my ideal route was to identify the different route options, including toll-roads, factoring the total miles driven, travel time and cost associated with each option. In addition, I incorporated limitations such as required stops, avoid main street, don’t visit the grocery store before lunch and must be back home as quickly as possible. Optimization is a way to take all of these limitations and objectives and simultaneously compare all possible combinations and outcomes to determine the ideal option to maximize a goal, which in this case was to be home as quickly as possible. While this is by its nature a very simple example, optimizing decisions can be applied to home and business in very imaginative and effective means. Business is catching on and optimization is finding its way into more and more businesses to save time and money, which will provide a competitive advantage. I encourage all of you to think about optimization in a new way and explore the opportunities where it can be applied to provide improvements over business-as-usual as well as to improve your quality of life.  

Recently, the Commerce Department reported that consumer spending levels continued to rise in February, increasing for the fifth straight month *, while flat income levels drove savings levels lower. At the same time, media outlets such as Fox Businesses, reported that the consumer “shopping cart” ** showed price increases for the fourth straight month. Somewhat in opposition to this market trend, the Q4 2009 Experian-Oliver Wyman Market Intelligence Reports reveal that the average level of credit card debt per consumer decreased overall, but showed increases in only one score band. In the Q4 reports, the score band that demonstrated balance increases was VantageScore® credit score A – the super prime consumer - whose average balance went up $30 to $1,739. In this time of economic challenge and pressure on household incomes, it’s interesting to see that the lower credit scoring consumers display the characteristics of improved credit management and deleveraging; while at the same time, consumers with credit scores in the low-risk tiers may be showing signs of increased expenses and deteriorated savings. Recent delinquency trends support that low-risk consumers are deteriorating in performance for some product vintages. Even more interestingly, Chris Low, Chief Economist at FTN Financial in New York was quoted as saying "I guess the big takeaway is that consumers are comfortably consuming again. We have positive numbers five months in a row since October, which I guess is a good sign,".  I suggest that there needs to be more analysis applied within the details of these figures to determine whether consumers really are ‘comfortable’ with their spending, or whether this is just a broad assumption that is masking the uncomfortable realities that lie within.

By: Wendy Greenawalt In my last few blogs, I have discussed how optimization can be leveraged to make improved decisions across an organization while considering the impact that opimizing decisions have to organizational profits, costs or other business metrics. In this entry, I would like to discuss how optimization is used to improve decisions at the point of acquisition, while minimizing costs. Determining the right account terms at inception is increasingly important due to recent regulatory legislation such as the Credit Card Act.  Doing so plays a role in assessing credit risk, relationship managment, and increasing out of wallet share. These regulations have established guidelines specific to consumer age, verification of income, teaser rates and interest rate increases. Complying with these regulations will require changes to existing processes and creation of new toolsets to ensure organizations adhere to the guidelines. These new regulations will not only increase the costs associated with obtaining new customers, but also the long term revenue and value as changes in account terms will have to be carefully considered. The cost of on-boarding and servicing individual accounts continues to escalate while internal resources remain flat. Due to this, organizations of all sizes are looking for ways to improve efficiency and decisions while minimizing costs. Optimizing decisions is an ideal solution to this problem. Optimized strategy trees (trees that optimize decisioning strategies) can be easily implemented into current processes to ensure lending decisions adhere to organizational revenue, growth or cost objectives as well as regulatory requirements.  Optimized strategy trees enable organizations to create executable strategies that provide on-going decisions based upon optimization conducted at a consumer level. Optimized strategy trees outperform manually created trees as they are created utilizing sophisticated mathematical analysis and ensure organizational objectives are adhered to. In addition, an organization can quantify the expected ROI of decisioning strategies and provide validation in strategies – before implementation. This type of data is not available without the use of a sophisticated optimization software application.  By implementing optimized strategy trees, organizations can minimize the volume of accounts that must be manually reviewed, which results in lower resource costs. In addition, account terms are determined based on organizational priorities leading to increased revenue, retention and profitability.

By: Wendy Greenawalt Financial institutions have placed very little focus on portfolio growth over the last few years.  Recent market updates have provided little guidance to the future of the marketplace, but there seems to be a consensus that the US economic recovery will be slow compared to previous recessions. The latest economic indicators show that slow employment growth, continued property value fluctuations and lower consumer confidence will continue to influence the demand and issuance of new credit. However, the positive aspect is that most analysts agree that these indicators will improve over the next 12 to 24 months. Due to this, lenders should start thinking about updating acquisition strategies now and consider new tools that can help them reach their short and long-term portfolio growth goals. Most financial institutions have experienced high account delinquency levels in the past few years. These account delinquencies have had a major impact to consumer credit scores. The bad news is that the pool of qualified candidates continues to shrink so the competition for the best consumers will only increase over the next few years. Identifying target populations and improving response/booking rates will be a challenge for some time so marketers must create smarter, more tailored offers to remain competitive and strategically grow their portfolios. Recently, new scores have been created to estimate consumer income and debt ratios when combined with consumer credit data. This data can be very valuable and when combined with optimization (optimizing decisions) can provide robust acquisition strategies. Specifically, optimization / optimizing decisions allows an organization to define product offerings, contact methods, timing and consumer known preferences, as well as organizational goals such as response rates, consumer level profitability and product specific growth metrics into a software application. The optimization software will then utilize a proven mathematical technique to identify the ideal product offering and timing to meet or exceed the defined organizational goals.  The consumer level decisions can then be executed via normal channels such as mail, email or call centers. Not only does optimization software reduce campaign development time, but it also allows marketers to quantify the effectiveness of marketing campaigns – before execution. Today, optimization technology provide decision analytics accessible for organizations of almost any size and can provide an improvement over business-as-usual techniques for decisioning strategies. If your organization is looking for new tools to incorporate into existing acquisition processes, I would encourage you to consider optimization and the value it can bring to your organization.

By: Kari Michel Lenders want to find new customer through more informed credit risk decisions and use new types of data relationships to cross-sell.   The strategic goals of any company are to get more customers and revenue while reducing costs on the operating side and the credit loss side.  Some of the ways to meet these goals are to improve operating efficiency in creating and managing credit attributes, which represent the building blocks of how lenders make customer decisions. Lenders face many challenges in leveraging data from multiple credit and non-credit sources (e.g. credit bureaus) and maintaining data attributes across multiple systems. Furthermore, a lack of access to raw data makes it difficult to create effective, predictive attributes. Simply managing the discrepancies between specifications and code can become a very time consuming effort.  Maintaining a common set of attributes used in many types of scorecards and decision types often becomes difficult.  As a result, there is a heavy reliance on external people and technical resources to find the right tools to try and pull the data sources and attributes together. In an ideal situation, a lender should be able to easily access raw data elements across multiple sources and aggregate the data into meaningful attributes. Experian can offer these capabilities through its Attribute Toolbox product, allowing one or more systems to access a common set of standard analytics.  A set of highly predictive attributes, Premier Attributes, are available and offers a much more effective solution  for managing standard attributes across an enterprise.  With the use of these tools, lenders can decrease maintenance costs by quickly integrating data and analytics into existing business architecture to make profitable decisions.  

By:Wendy Greenawalt In my last few blogs, I have discussed how optimizing decisions can be leveraged across an organization while considering the impact those decisions have to organizational profits, costs or other business metrics. In this entry, I would like to discuss how this strategy can be used in optimizing decisions at the point of acquisition, while minimizing costs. Determining the right account terms at inception is increasingly important due to recent regulatory legislation such as the Credit Card Act. These regulations have established guidelines specific to consumer age, verification of income, teaser rates and interest rate increases. Complying with these regulations will require changes to existing processes and creation of new toolsets to ensure organizations adhere to the guidelines. These new regulations will not only increase the costs associated with obtaining new customers, but also the long term revenue and value as changes in account terms will have to be carefully considered. The cost of on-boarding and servicing individual accounts continues to escalate, and internal resources remain flat. Due to this, organizations of all sizes are looking for ways to improve efficiency and decisions while minimizing costs. Optimization is an ideal solution to this problem. Optimized strategy trees can be easily implemented into current processes and ensure lending decisions adhere to organizational revenue, growth or cost objectives as well as regulatory requirements.  Optimized strategy trees enable organizations to create executable strategies that provide on-going decisions based upon optimization conducted at a consumer level. Optimized strategy trees outperform manually created trees as they are created utilizing sophisticated mathematical analysis and ensure organizational objectives are adhered to. In addition, an organization can quantify the expected ROI of a given strategy and provide validation in strategies – before implementation. This type of data is not available without the use of a sophisticated optimization software application.  By implementing optimized strategy trees, organizations can minimize the volume of accounts that must be manually reviewed, which results in lower resource costs. In addition, account terms are determined based on organizational priorities leading to increased revenue, retention and profitability.

By: Wendy Greenawalt Marketing is typically one of the largest expenses for an organization while also being a priority to reach short and long-term growth objectives. With the current economic environment, continuing to be unpredictable many organizations have reduced budgets and focused on more risk and recovery activities. However, in the coming year we expect to see improvements and organizations renew their focus to portfolio growth. We expect that campaign budgets will continue to be much lower than what was allocated before the mortgage meltdown but organizations are still looking for gains in efficiency and response to meet business objectives. Creation of optimized marketing strategies is quick and easy when leveraging optimization technology enabling your internal resources to focus on more strategic issues. Whether your objective is to increase organizational or customer level profit, growth in specific product lines or maximizing internal resources optimization can easily identify the right solution while adhering to key business objectives. The advanced software now available enables an organization to compare multiple campaign options simultaneously while analyzing the impact of modifications to revenue, response or other business metrics. Specifically, very detailed product offer information, contact channels, timing, and letter costs from multiple vendors and consumer preferences can all be incorporated into an optimization solution. Once defined the complex mathematical algorithm factors every combination of all variables, which could range in the thousands, are considered at the consumer level to determine the optimal treatment to maximize organizational goals and constraints. In addition, by incorporating optimized decisions into marketing strategies marketers can execute campaigns in a much shorter timeframe allowing an organization to capitalize on changing market conditions and consumer behaviors. To illustrate the benefit of optimization an Experian bankcard client was able to reduced analytical time to launch programs from 7 days to 90 minutes while improving net present value. In my next blog, we will discuss how organizations can cut costs when acquiring new accounts.  

By: Wendy Greenawalt The economy has changed drastically in the last few years and most organizations have had to reduce costs across their businesses to retain profits. Determining the appropriate cost-cutting measures requires careful consideration of trade-offs while quantifying the short- and long-term organizational priorities.  Too often, cost reduction decisions are driven by dynamic market conditions, which mandate quick decision-making. Due to this, decisions are made without a sound understanding of the true impact to organizational objectives. Optimization (optimizing decisions) can be used for virtually any business problem and provides decisions based on complex mathematics. Therefore, whether you are making decisions related to outsourcing versus staffing, internal versus external project development or specific business unit cost savings opportunities, optimization can be applied. While some analytical requirements exist to obtain the highest business metric improvements, most organizations have the data available that is required to take full advantage of optimization technology.  If you are using predictive models, credit attributes and have multiple actions that can be taken on an individual consumer, then, most likely, your organization can benefit from strategies in optimizing decisions. In my next few blogs, I will discuss how optimization / optimizing decisions can be used to create better strategies across an organization whether your focus is marketing, risk, customer management or collections.  

As the economic environment changes on what feels like a daily basis, the importance of having information about consumer credit trends and the future direction of credit becomes invaluable for planning and achieving strategic goals. I recently had the opportunity to speak with members of the collections industry about collections strategy and collections change management -- and discussed the use of business intelligence data in their industry. I was surprised at how little analysis was conducted in terms of anticipating strategic changes in economic and credit factors that impact the collections business. Mostly, it seems like anecdotal information and media coverage is used to get ‘a feeling’ for the direction of the economy and thus the collections industry. Clearly, there are opportunities to understand these high-level changes in more detail and as a result, I wanted to review some business intelligence capabilities that Experian offers – and to expand on the opportunities I think exist to for collections firms to leverage data and better inform their decisions: * Experian possesses the ability to capture the entire consumer credit perspective, allowing collections firms to understand trends that consider all consumer relationships. * Within each loan type, insights are available by analyzing loan characteristics such as, number of trades, balances, revolving credit limits, trade ages, and delinquency trends. These metrics can help define market sizes, relative delinquency levels and identify segments where accounts are curing faster or more slowly, impacting collectability. * Layering in geographic detail can reveal more granular segment trends, creating segments for both macro and regional-level credit characteristics. * Experian Business Intelligence has visibility to the type of financial institution, allowing for a market by market view of credit patterns and trends. * Risk profiling by VantageScore can shed light on credit score trends, breaking down larger segments into smaller score-based segments and identifying pockets of opportunity and risk. I’ll continue to consider the opportunities for collections firms to leverage business intelligence data in subsequent blogs, where I’ll also discuss the value of credit forecasting to the collections industry.