All posts by Kelly Kent
There has been a lot of discussion around the auto loan market regarding delinquency rates in the past year. It is a topic Experian is asked about frequently from clients in regard to what particular economic market behaviors mean for the overall consumer lending. To understand this issue more clearly, I ran a deeper dive on the data from our Q3 Experian-Oliver Wyman Market Intelligence report. There are some interesting, and perhaps concerning, trends in the data for automotive loans and leases. Want Insights on the latest consumer credit trends? Register for our 2016 year-end review webinar. Register now Auto loan delinquency rates are at their highest mark since 2008 The findings indicate that the performance of the most recent loans opened from Q4 2015 are now performing as poorly as the loans from the credit crisis back in 2008. In fact, you have to go back to 2008, and in some cases, 2007, to see loan default rates as poorly as the Q4 2015 auto loans originated in the last year. Below we have the auto loan vintage performance for loans originated in Q4 of the last 8 years — going back to 2008. The lines on the chart each represent 60 days late or more (60+) delinquency rates over specific time period grades. For these charts, I analyzed the first three, six, and nine months from the loan origination date. As you can see, the rates of delinquency have steadily increased in recent years, with the increase in the Q4 2015 loans opened equaling or even surpassing 2008 levels. The above chart reflects all credit grades, so one might think that this change is a result of the change in the credit origination mix. By digging a little deeper into the data, we can control for the VantageScore at the loan opening, or origination date, and review performance by looking at two different score segments separately. Is there concern for Superprime and Prime consumers auto loans? In the chart immediately below, the same analysis as above has been conducted, but only for trades originated by Superprime and Prime consumers at the time of origination. You can see that although the trend is not as pronounced as when all grades are considered, even these tiers of consumers are showing significant increases in their 60+ days past due (DPD) rates in recent vintages. Separately, looking at the Subprime and Deep Subprime segments, you can really see the dramatic changes that have occurred in the performance of recent auto vintages. Holding score segments constant, the data indicates a rate of credit deterioration in the Subprime and Deep Subprime segments that we have not observed since at least 2008 — back to when we started tracking this data. What’s concerning here is not only the absolute values of the vintage delinquencies but also the trend, which is moving upward for all three time periods. Where does the risk fall? Now that we see the evidence of the deterioration of credit performance across the credit spectrum, one might ask – who is bearing the risk in these recent vintages? Taking a closer look at the chart below, you can see the significant increase in the volumes of loans across lender type, but particularly interesting to me is the increase in 2016 for the Captive Auto lenders and Credit Unions, who are hitting highs in their lending volumes in recent quarters. If the above trend holds and the trajectory continues, this suggests exposure issues for those lenders with higher volumes in recent months. What does this mean for your business? Speak to Experian\'s global consulting practice to learn more. Learn more Just to be thorough, let\'s continue and look at the relative amounts of loans going to the different score segments by each of the lender types. Comparing the lender type and the score segments (below) reveals that finance lenders have a greater than average exposure to the Subprime and Deep Subprime segments. To summarize, although auto lending has recently been viewed as a segment where loan performance is good, relative to historical levels, I believe, the above data signals a striking change in that perspective. Recent loan performance has weakened to a point where comparing the 2008 vintage with 2015 vintage, one might not be able to distinguish between the two. // <![CDATA[ var elems={'winWidth':window.innerWidth,'winTol':600,'rotTol':800,'hgtTol':1500}, updRes=function(){var xAxislabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'14px'}},xAxislabelRotation=function(){if(elems.winWidth<elems.rotTol){return-90}else{return 0}},seriesLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},legenLabelSize=function(){if(elems.winWidth<elems.winTol){return'12px'}else{return'16px'}},chartHeight=function(){if(elems.winWidth<elems.rotTol){return 600}else{return 400}},labelInside=function(){if(elems.winWidth<elems.rotTol){return false}else{return true}},chartStack=function(){if(elems.winWidth<elems.rotTol){return null}else{return'normal'}};this.sourceRef=function(){return['Source: Experian.com']};this.seriesColor=function(){return['#982881','#0d6eb6','#26478D','#d72b80','#575756','#b02383']};this.chartFontFamily=function(){return'"Roboto",Helvetica,Arial,sans-serif'};this.xAxislabelSize=function(){return xAxislabelSize()};this.xAxislabelOverflow=function(){return'none'};this.xAxislabelRotation=function(){return xAxislabelRotation()};this.seriesLabelSize=function(){return seriesLabelSize()};this.legenLabelSize=function(){return legenLabelSize()};this.chartHeight=function(){return chartHeight()};this.labelInside=function(){return labelInside()};this.chartStack=function(){return chartStack()}}(), updY=function(chart){var points=chart.series[0].points;for(var i=0;i elems.rotTol){if(thisWidth<20){var y=points[i].dataLabel.y;y-=10;points[i].dataLabel.css({color:'#575756'}).attr({y:y-thisWidth})}}}},updX=function(chart){var points=chart.series[0].points;for(var i=0;i elems.rotTol){if(thisWidth
Prescriptive solutions: Get the Rx for your right course of action By now, everyone is familiar with the phrase “big data” and what it means. As more and more data is generated, businesses need solutions to help analyze data, determine what it means and then assist in decisioning. In the past, solutions were limited to simply describing data by creating attributes for use in decisioning. Building on that, predictive analytics experts developed models to predict behavior, whether that was a risk model for repayment, a propensity model for opening a new account or a model for other purposes. The next evolution is prescriptive solutions, which go beyond describing or predicting behaviors. Prescriptive solutions can synthesize big data, analytics, business rules and strategies into an environment that provides businesses with an optimized workflow of suggested options to reach a final decision. Be prepared — developing prescriptive solutions is not simple. In order to fully harness the value of a prescriptive solution, you must include a series of minimum capabilities: Flexibility — The solution must provide users the ability to make quick changes to strategies to adjust to market forces, allowing an organization to pivot at will to grow the business. A system that lacks agility (for instance, one that relies heavily on IT resources) will not be able to realize the full value, as its recommendations will fall behind current market needs. Expertise — Deep knowledge and a detailed understanding of complex business objectives are necessary to link overall business goals to tactical strategies and decisions made about customers. Analytics — Both descriptive and predictive analytics will play a role here. For instance, the use of a layered score approach in decisioning — what we call dimensional decisioning — can provide significant insight into a target market or customer segment. Data — It is assumed that most businesses have more data than they know what to do with. While largely true, many organizations do not have the ability to access and manage that data for use in decision-making. Data quality is only important if you can actually make full use of it. Let’s elaborate on this last point. Although not intuitive, the data you use in the decision-making process should be the limiting factor for your decisions. By that, I mean that if you get the systems, analytics and strategy components of the equation right, your limitation in making decisions should be data-driven, and not a result of another part of the decision process. If your prescriptive environment is limited by gaps in flexibility, expertise or analytic capabilities, you are not going to be able to extract maximum value from your data. With greater ability to leverage your data — what I call “prescriptive capacity” — you will have the ability to take full advantage of the data you do have. Taking big data from its source through to the execution of a decision is where prescriptive solutions are most valuable. Ultimately, for a business to lead the market and gain a competitive advantage over its competitors — those that have not been able to translate data into meaningful decisions for their business — it takes a combination of the right capabilities and a deep understanding of how to optimize the ecosystem of big data, analytics, business rules and strategies to achieve success.
As Big Data becomes the norm in the credit industry and others, the seemingly non-stop efforts to accumulate more and more data leads me to ask the question - when is Big Data too much data? The answer doesn’t lie in the quantity of data itself, but rather in the application of it – Big Data is too much data when you can’t use it to make better decisions. So what do I mean by a better decision? From any number of perspectives, the answer to that question will vary. From the viewpoint of a marketer, maybe that decision is about whether new data will result in better response rates through improved segmentation. From a lender perspective, that decision might be about whether a borrower will repay a loan or the right interest rate to charge the borrower. That is one the points of the hype around Big Data – it is helping companies and individuals in all sorts of situations make better decisions – but regardless of the application, it appears that the science of Big Data must not just be based on an assumption that more data will always lead to better decisions, but that more data can lead to better decisions – if it is also the “right data”. Then how does one know when another new data source is helping? It’s not obvious that additional data won’t help make a better decision. It takes an expert to understand not only the data employed, but ultimately the use of the data in the decision-making process. It takes expertise that is not found just anywhere. At Experian, one of our core capabilities is based on the ability to distinguish between data that is predictive and can help our clients make better decisions, and that which is noise and is not helpful to our clients. Our scores and models, whether they be used for prospecting new customers, measuring risk in offering new credit, or determining how to best collect on an outstanding receivable, are all designed to optimize the decision making process. Learn more about our big data capabilities
I love a good analogy, and living in Southern California, lately I’ve been thinking a lot about earthquakes, and how lenders might want to start thinking like seismologists when considering the risk levels in their portfolios. Currently, scientists that study earthquakes review mountains of data around fault movement, tidal forces, even animal behavior, all in an attempt to find a concrete predictor of ‘the big one’. Small tremors are inputs, but the focus is on predicting and preparing for the large shock and impact of large earthquakes. Credit risk modeling, conversely, seems to focus on predicting the tremors, (risk scores that predict the risk of individual default) and less so the large-shock risk to the portfolio. So what are lenders doing to forecast ‘the big one’? Lenders are building sophisticated models that contemplate the likelihood of the big event – developing risk models and econometric models that look at loan repayment, house prices, unemployment rates – all in an attempt to be ahead of the credit version of ‘the big one’. This type of model and perspective is at a nascent stage for many lenders, but like the issues facing the people of Southern California, preparing for the big-one is an essential part of every lender’s planning in today’s economy.
As our newly elected officials begin to evaluate opportunities to drive economic growth in 2011, it seems to me that the role of lenders in motivating consumer activity will continue to be high on the list of both priorities and actions that will effectively move the needle of economic expansion. From where I sit, there are a number of consumer segments that each hold the potential to make a significant impact in this economy. For instance, renters with spotless credit, but have not been able or confident enough to purchase a home, could move into the real estate market, spurring growth and housing activity. Another group, and one I am specifically interested in discussing, are the so called ‘fallen angels’ - borrowers who previously had pristine track records, but have recently performed poorly enough to fall from the top tiers of consumer risk segments. I think the interesting quality of ‘fallen angels’ is not that they don’t possess the motivation needed to push economic growth, but rather the supply and opportunity for them to act does not exist. Lenders, through the use of risk scores and scoring models, have not yet determined how to easily identify the ‘fallen angel’ amongst the pool of higher-risk borrowers whose score tiers they now inhabit. This is a problem that can be solved though – through the use of credit attributes and analytic solutions, lenders can uncover these up-side segments within pools of potential borrowers – and many lenders are employing these assets today in their efforts to drive growth. I believe that as tools to identify and lend to untapped segments such as the ‘fallen angels’ develop, these consumers will inevitably turn out to be key contributors to any form of economic recovery.
A recent article in the USA Today titled, “Jobs rebound will be slow”*, outlines state-by-state forecasts for the United States, as released by Moody\'s Economy.com. Although the national forecasted increase, 0.9%, reflects the expectation that unemployment will remain an issue throughout 2011, the state-level detail possesses interesting variances that should be further considered by lenders in determining their marketing and acquisition strategies. What I find intriguing, is that Moody’s forecasts job growth for several states that since the beginning of the housing decline have been the hot-spots for mortgage default and high delinquency rates. Moody’s projects job growth for Florida (+2.5%), Nevada (+1.5%), and California (+0.5%) – the so called “sand states” – with comparable growth rates to states like Texas (+2.5%) and North Carolina (+1.3%), which have not experienced the same notoriety for increased risk levels and delinquency. Should this growth transpire, then these states that have been the center of credit risk in recent years will soon become centers of opportunity for lenders, as increased employment should result in decreasing delinquency rates, improved repayment habits, and a generally more creditworthy consumer population. This shift is important, since any economic recovery will start with jobs growth, leading to increased lending, which will drive housing and a broader economic growth. As I noted above, the Moody’s forecast implies that lenders who are looking to drive growth may find that profitable portfolio segments exist in some of what appear to be the unlikeliest places. __________________ *http://www.usatoday.com/money/economy/2009-02-06-new-jobs-growth-graphic_N.htm
With the issue of delayed bank foreclosures at the top of the evening news, I wanted to provide a different perspective on the issue and highlight what I think are some very important, yet often underestimated risks hidden within this issue. For many homeowners, the process of becoming delinquent and eventually going into default is actually a cash-flow positive experience. The process offers these borrowers temporary “free rent,” whereby a major previous monthly commitment is no longer a monthly obligation, freeing up cash for other purposes, including paying other bills. For those consumers who are managing cash flow issues each month, the lack of a mortgage commitment immediately allows them to meet other commitments more easily - making payments on credit cards and car loans that may have previously also become delinquent. From the perspective of a credit card or auto lender, the extended foreclosure process is a short-term positive – it allows a borrower who had previously struggled to remain current to now pay on time and in the short-run, contributes to portfolio health. Although these lenders will experience an improvement in delinquency rates, the reality is that the credit risk is simply dormant. At some point, the consumer’s mortgage will go into foreclosure, and which point the consumer will again be under pressure to continue meeting their obligations. The hidden and significant risk management issue is the misinterpretation of improved delinquency rates. Halting foreclosures means that an accumulating number of consumers are going to enter into this delayed stage of ‘free rent’, without any immediate prospect of having to make a rent or mortgage payment in the near future. In fact, according to Bank of America, “the average foreclosed borrower has not made a payment in 18 months”. This extended period of foreclosure delay will naturally result in a larger number of consumers being able to meet their non-mortgage obligations – but only while their free-rent status exists. A lender who has an interest in the “free rent” consumer is actually sitting on a time-bomb. When foreclosures stop or slow to a rate that is less than consumers entering it, that group will continue to grow in size - until foreclosures start again – at which point thousands of consumers will be processed and will have to start managing rent/housing payments again. Almost immediately, thousands of consumers who have had no problems meeting their obligations will have to start making decisions about which to pay and which not to pay. So, this buildup of rent-free mortgage holders presents a serious risk management issue to non-mortgage lenders that must be addressed. Lenders who have a relationship with a consumer who is delinquent on their mortgage may be easily fooled into thinking that they are not exposed to the same credit risk as mortgage lenders, but I think that these lenders will quickly find that consumers who have lived rent-free for over a year will have a very difficult time managing this transition, and if not diligent, credit card issuers and automotive lenders may find themselves in trouble. _____________________ http://cnews.canoe.ca/CNEWS/World/2010/10/08/15629836.html
With the news from the Federal Reserve that joblessness is not declining, and in fact is growing, a number of consumers are going to face newly difficult times and be further challenged to meet their credit obligations. Thinking about how this might impact the already struggling mortgage market, I’ve been considering what the impact of joblessness is on the incidence of strategic default and the resulting risk management issues for lenders. Using the definitions from our previous studies on strategic default, I think it’s quite clear that increased joblessness will definitely increase the number of ‘cash-flow managers’ and ‘distressed borrowers’, as newly jobless consumers face reduced income and struggle to pay their bills. But, will a loss of income also mean that people become more likely to strategically default? By definition, the answer is no – a strategic defaulter has the capacity to pay, but chooses not to, mostly due to their equity position in the home. But, I can’t help but consider a consumer who is 20% underwater, but making payments when employed, deciding that the same 20% that used to be acceptable to bear, is now illogical and will simply choose to stop payment? Although only a short-term fix, since they can use far less of their savings by simply ceasing to pay their mortgage, this would free up significant cash (or savings) for paying car loans, credit cards, college loans, etc; and yet, this practice would maintain the profile of a strategic defaulter. While it’s impossible to predict the true impact of joblessness, I would submit that beyond assessing credit risk, lenders need to consider that the definition of strategic default may contain a number of unique, and certainly evolving consumer risk segments. __________________________ http://money.cnn.com/2010/08/19/news/economy/initial_claims/index.htm
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.
I recently attended a conference where Credit Union managers spoke of the many changes facing their industry in the wake of the real estate crisis and economic decline that has impacted the US economy over the past couple of years. As these managers weighed in on the issues facing their businesses today, several themes began to emerge – tighter lending standards & risk management practices, increased regulatory scrutiny, and increased competition resulting in tighter margins for their portfolios. Across these issues, another major development was discussed – increased Credit Union mergers and acquisitions. As I considered the challenges facing these lenders, and the increase in M&A activity, it occurred to me that these lenders might have a common bond with an unexpected group –American family farms. Overall, Credit Unions are facing the challenge of adding significant fixed costs (more sophisticated lending platforms & risk management processes) all the while dealing with increased competition from lenders like large banks and captive automotive lenders. This challenge is not unlike the challenges faced by the family farm over the past few decades – small volume operators having to absorb significant fixed costs from innovation & increased corporate competition, without the benefit of scale to spread these costs over to maintain healthy lending margins. Without the benefit of scale, the family farm basically disappeared as large commercial operators acquired less-efficient (and less profitable) operators. Are Credit Unions entering into a similar period of competitive disadvantage? It appears that the Credit Union model will have to adjust in the very near future to remain viable. With high infrastructure expectations, many credit unions will have to develop improved decisioning strategies, become more proficient in assessing credit risk –implementing risk-based pricing models, and executing more efficient operational processes in order to sustain themselves when the challenges of regulation and infrastructure favor economies of scale. Otherwise, they are facing an uphill challenge, just as the family farm did (and does); to compete and survive in a market that favors the high-volume lender.
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.
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 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.
In the past few days I’ve read several articles discussing how lenders are taking various actions to reduce their exposure to toxic mortgages – some, like Bank of America, are engaging new principal repayment programs.* Others, (including Bank of America) are using existing incentive programs to fast-track the approvals of short-sales to stunt their losses and acquire stronger lenders on existing real-estate assets. Given the range of options available to lenders, there are significant decisions to make regarding the creditworthiness of existing consumers and which treatment strategies are best for each borrower, these decisions important for assessing credit risk, loan origination strategies and loan pricing and profitability. Experian analysis has uncovered the attributes of borrowers with various borrowing behaviors: strategic defaulters, cash-flow managers, and distressed borrowers, each of whom require a unique treatment strategy. The value of credit attributes and predictive risk scores, like Experian Premier Attributes and VantageScore, has never been higher to lenders. Firms like Bank of America are relying on credit delinquency attributes to segment eligible borrowers for its programs, and should also consider that more extensive use of attributes can further sub-segment its clients based on the total consumer credit profile. Consumers who are late on mortgage payments, yet current on other loans, may be likely to re-default; whereas some consumers may merely need financial planning advice and enhanced money management skills. As lenders develop new methods to manage portfolio risk and deal with toxic assets on their portfolios, they should also continue to seek new and innovative analytics, including optimization, to make the best decisions for their customers, and their business. * LA Times, March 25, 2010, ‘Bank of America to reduce mortgage principal for some borrowers’
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