Credit Score Trends

--by Kari Michel

The U.S. government and mortgage lenders have developed various loan modification programs to help homeowners better manage their mortgage debt so that they can meet their monthly payment obligations. Given these new programs, what is the impact to the consumer’s score? Do consumer scores drop more if they work with their lenders to get their mortgage loan restructured or if they file for bankruptcy?

The finding from a study conducted by VantageScore ^® Solutions* reveals that a delinquency on a mortgage has a greater impact on the consumer’s score than a loan modification. Bankruptcy, short sale, and foreclosure have the greatest impact to a score. A bankruptcy or poor bankruptcy score can negatively impact a consumer for a minimum of seven years with a potential score decrease of 365 points. However, with a loan modification, consumers can rehabilitate their scores to an acceptable risk level within nine months.  This depends on them bringing all their delinquent accounts to current status. Loan modifications have little impact on their consumer credit score and the influence on their score can range from a 20 point decrease to an increase of 30 points.

Lenders should proactively seek out a mortgage loan modification before consumers experience severe delinquency in their credit files and credit score trends. The restructured mortgage should provide sufficient cash availability to remain with the consumer.  This ensures that any other delinquent debts can be updated to current status. Whenever possible, bankruptcy should be avoided because it has the greatest consequences for the lender and the consumer.

*For more detailed information on this study, Credit Scoring and Mortgage Modifications: What lenders need to know, please click on this link to access an archived file of a recent webinar:

 http://register.sourcemediaconferences.com/click/clickReg.cfm?URLID=5258

--by Kelly Kent

When reviewing offers for prospective clients, lenders often deal with a significant amount of missing information in assessing the outcomes of lending decisions, such as:

• Why did a consumer accept an offer with a competitor?
• What were the differentiating factors between other offers and my offer, i.e. what were their credit score trends?
• What happened to consumers that we declined? Do they perform as expected or better than anticipated?
• What were their credit risk models?

While lenders can easily understand the implications of the loans they have offered and booked with consumers, they often have little information about two important groups of consumers:

1. Lost leads: consumers to whom they made an offer but did not book
2. Proxy performance: consumers to whom financing was not offered, but where the consumer found financing elsewhere

Performing a lost lead analysis on the applications approved and declined, can provide considerable insight into the outcomes and credit performance of consumers that were not added to the lender’s portfolio.

Lost lead analysis can also help answer key questions for each of these groups:

• How many of these consumers accepted credit elsewhere?
• What were their credit attributes?
• What are the credit characteristics of the consumers we're not booking?
• Were these loans booked by one of my peers or another type of lender?
• What were the terms and conditions of these offers?
• What was the performance of the loans booked elsewhere?
• Who did they choose for loan origination?

Within each of these groups, further analysis can be conducted to provide lenders with actionable feedback on the implications of their lending policies, possibly identifying opportunities for changes to better fulfill lending objectives. Some key questions can be answered with this information:

• Are competitors offering longer repayment terms?
• Are peers offering lower interest rates to the same consumers?
• Are peers accepting lower scoring consumers to increase market share?

The results of a lost lead analysis can either confirm that the competitive marketplace is behaving in a manner that matches a lender’s perspective.  It can also shine a light into aspects of the market where policy changes may lead to superior results. In both circumstances, the information provided is invaluable in making the best decision in today’s highly-sensitive lending environment.

 

--by Kari Michel

Most lenders use a credit scoring model in their decision process for opening new accounts; however, between 35 and 50 million adults in the US may be considered unscoreable with traditional credit scoring models. That is equivalent to 18-to-25 percent of the adult population. 

Due to recent market conditions and shrinking qualified candidates lenders have placed a renewed interest in assessing the risk of this under served population.  Unscoreable consumers could be a pocket of missed opportunity for many lenders. To assess these consumers, lenders must have the ability to better distinguish between consumers with a clear track record of unfavorable credit behaviors versus those that are just beginning to develop their credit history and credit risk models.

Unscoreable consumers can be split out into three populations:

• Infrequent credit users:  Consumers who have not been active on their accounts for the past six months, and who prefer to use non-traditional credit tools for their financial needs.

• New entrants:  Consumers who do not have at least one account with more than six months of activity; including young adults just entering the workforce,  recently divorced or widowed individuals with little or no credit history in their name, newly arrived immigrants, or people who avoid the traditional system by choice.

• Thin file consumers:  Consumers who have less than three accounts and rarely utilize traditional credit and likely prefer using alternative credit tools and credit score trends.

A study done by VantageScore® Solutions, LLC shows that a large percentage of the
unscoreable population can be scored with VantageScore* and a portion of these are credit-worthy (defined as the population of consumers who have a cumulative likelihood to become 90 days or more delinquent is less than 5 percent).  The following is a high-level summary of the findings for consumers who had at least one trade:

Lenders can review their credit decisioning process to determine if they have the tools in place to assess the risk of those unscoreable consumers.  As with this population there is an opportunity for portfolio expansion as demonstrated by the VantageScore study.

*VantageScore is a generic credit scoring model introduced to meet the market demands for a highly predictive consumer score. Developed as a joint venture among the three major credit reporting companies (CRCs) – Equifax, Experian and TransUnion.

-- by Kelly Kent

Source: Experian-Oliver Wyman Market Intelligence Reports

Analyzing recent trends from vintages published in the Experian-Oliver Wyman Market Intelligence Reports, there are numerous insights that can be gleaned from just a cursory review of the results.

Mortgage trends

As noted in an earlier posting, recent mortgage vintage analysis' show a broad range of behaviors between more recent vintages and older, more established vintages that were originated before the significant run-up of housing prices seen in the middle of the decade. The 30+ delinquency levels for mortgage vintages in 2005, 2006, and 2007 approach and in two cases exceed 10 percent of trades in the last 12 months of performance, and have spiked from historical trends, beginning almost immediately after origination. On the other end of the spectrum, the vintages from 2003 and 2002 have barely approached or exceeded 5 percent for the last 6 or 7 years.

Band card trends

As one would expect, the 30+ delinquency trends demonstrated within bankcard vintage analysis are vastly different from the trends of mortgage vintages. Firstly, card delinquencies show a clear seasonal trend, with a more consistent yearly pattern evident in all vintages, resulting from the revolving structure of the product. The most interesting trends within the card vintages do show that the more recent vintages, 2005 to 2008, display higher 30+ delinquency levels, especially the Q2 2007 vintage, which is far and away the underperformer of the group.

Within each vintage pool, an analysis can extend into the risk distribution and details of the portfolio and further segment the pool by credit score, specifically VantageScore.  In other words, the loans in this pool are only for the most creditworthy customers at the time of origination. The noticeable trend is that while these consumers were largely resistant to deteriorating economic conditions, each vintage segment has seen a spike in the most recent 9-12 months.

Given that these consumers tend to have the highest limits and lowest utilization of any VantageScore band, this trend encourages further account management consideration and raises flags about overall bankcard performance in coming months.

Even a basic review of vintage analysis pools and the subsequent analysis opportunities that result from this data can be extremely useful. This vintage analysis can add a new perspective to risk management, supplementing more established analysis techniques, and further enhancing the ability to see the risk within the risk.

-- by Kelly Kent

In a recent article, www.CNNMoney.com reported that Federal Reserve Chairman, Ben Bernanke, said that the pace of recovery in 2010 would be moderate and added that the unemployment rate would come down quite slowly, due to headwinds on ongoing credit problems and the effort by families to reduce household debt.’

While some media outlets promote an optimistic economic viewpoint, clearly there are signs that significant challenges lie ahead for lenders. As Bernanke forecasts, many issues that have plagued credit markets will sustain themselves in the coming years. Therefore lenders need to be equipped to monitor these continued credit problems if they wish to survive this protracted time of distress.

While banks and financial institutions are implementing increasingly sophisticated and thorough processes to monitor fluctuations in credit trends, they have little intelligence to compare their credit performance to that of their peers.  Lenders frequently cite that they are concerned about their lack of awareness or intelligence regarding the credit performance and status of their peers.  Marketing intelligence solutions are important for management of risk, loan portfolio monitoring and related decisioning strategies.

Currently, many vendors offer data on industry-wide trends, but few vendors provide the information needed to allow a lender to understand its position relative to a well-defined group of firms that it considers its peers. As a result, too many lenders are performing benchmarking using data sources that are biased, incomplete, inaccurate, or that lack the detail necessary to derive meaningful conclusions.

If you were going to measure yourself personally against a group to understand your comparative performance, why would you perform that comparison against people who had little or nothing in common with you? Does an elite runner measure himself against a weekend warrior to gauge his performance? No; he segments the runners by gender, age, and performance class to understand exactly how he stacks up.

Today’s lending environment is not forgiving enough for lenders to make broad industry comparisons if they want to ensure long-term success. Lenders cannot presume they are leading the pack, when, in fact, the race is closer than ever.

-- By Ken Pruett

Earlier this week I blogged about some of the other types of frauds that impact our customers such as “never pay” and “bust out” fraud. Today I want to touch a bit on some of the third party fraud scenarios that are often top of mind with our customers: identity theft; synthetic identities; and account takeover.  

Identity Theft
Identity theft usually occurs during the acquisition stage of the customer life cycle. Simply put, identity theft is the use of stolen identity information to fraudulently open up a new account.  These accounts do not have to be just credit card related. For example, there are instances of people using others identities to open up wireless phone and utilities accounts 

Recent fraud trends show this type of fraud is on the rise again after a decrease over the past several years.  A recent Experian study found that people who have better credit scores are more likely to have their identity stolen than those with very poor credit scores. It does seem logical that fraudsters would likely opt to steal an identity from someone with higher credit limits and available purchasing power.  This type of fraud gets the majority of media attention because it is the consumer who is often the victim (as opposed to a major corporation). 

Fraud changes over time and recent findings show that looking at data from a historical perspective is a good way to help prevent identity theft.  For example, if you see a phone number being used by multiple parties, this could be an indicator of a fraud ring in action.  Using these types of data elements can make your fraud models much more predictive and reduce your fraud referral rates. 

Synthetic Identities
Synthetic Identities are another acquisition fraud problem.  It is similar to identity theft, but the information used is fictitious in nature.  The fraud perpetrator may be taking pieces of information from a variety of parties to create a new identity.  Trade lines may be purchased from companies who act as middle men between good consumers with good credit and perpetrators who creating new identities.   This strategy allows the fraud perpetrator to quickly create a fictitious identity that looks like a real person with an active and good credit history. 

Most of the trade lines will be for authorized users only.  The perpetrator opens up a variety of accounts in a short period of time using the trade lines. When creditors try to collect, they can’t find the account owners because they never existed.  As Heather Grover mentioned in her blog, this fraud has leveled off in some areas and even decreased in others, but is probably still worth keeping an eye on.  One concern on which to focus especially is that these identities are sometimes used for bust out fraud. 

The best approach to predicting this type of fraud is using strong fraud models that incorporate a variety of non-credit and credit variables in the model development process.  These models look beyond the basic validation and verification of identity elements (such as name, address, and social security number), by leveraging additional attributes associated with a holistic identity -- such as inconsistent use of those identity elements.

Account Takeover
Another type of fraud that occurs during the account management period of the customer life cycle is account takeover fraud.  This type of fraud occurs when an individual uses a variety of methods to take over an account of another individual. This may be accomplished by changing online passwords, changing an address or even adding themselves as an authorized user to a credit card.  

Some customers have tools in place to try to prevent this, but social networking sites are making it easier to obtain personal information for many consumers.  For example, a person may have been asked to provide the answer to a challenge question such as the name of their high school as a means to properly identify them before gaining access to a banking account.  Today, this piece of information is often readily available on social networking sites making it easier for the fraud perpetrators to defeat these types of tools. 

It may be more useful to use out of wallet, or knowledge-based authentication and challenge tools that dynamically generate questions based on credit or public record data to avoid this type of fraud. 

-- By Kari Michel

This blog completes my discussion on monitoring new account decisions with a final focus: scorecard monitoring and performance.  It is imperative to validate acquisitions scorecards regularly to measure how well a model is able to distinguish good accounts from bad accounts. With a sufficient number of aged accounts, performance charts can be used to:

• Validate the predictive power of a credit scoring model;
• Determine if the model effectively ranks risk; and
• Identify the delinquency rate of recently booked accounts at various intervals above and below the primary cutoff score.

To summarize, successful lenders maximize their scoring investment by incorporating a number of best practices into their account acquisitions processes:

1. They keep a close watch on their scores, policies, and strategies to improve portfolio strength.
2. They create monthly reports to look at population stability, decision management, scoring models and scorecard performance.
3. They update their strategies to meet their organization’s profitability goals through sound acquisition strategies, scorecard monitoring and scorecard management.
 

-- By Kari Michel

In my last blog I gave an overview of monitoring reports for new account acquisition decisions listing three main categories that reports typically fall into:  (1) population stability; (2) decision management; (3) scorecard performance.

Today, I want to focus on population stability.   Applicant pools may change over time as a result of new marketing strategies, changes in product mix, pricing updates, competition, economic changes or a combination of these. Population stability reports identify acquisition trends and the degree to which the applicant pool has shifted over time, including the scorecard components driving the shift in custom credit scoring models. 

Population stability reports include:

• Actual versus expected score distribution
• Actual versus expected scorecard characteristics distributions (available with custom models)
• Mean applicant scores
• Volumes, approval and booking rates

These types of reports provide information to help monitor trends over time, rather than spikes from month to month.  Understanding the trends allows one to be proactive in determining if the shifts warrant changes to lending policies or cut-off scores.

Population stability is only one area that needs to be monitored; in my next blog I will discuss decision management reports.

 

-- by Kelly Kent

The title of this edition, ‘The risk within the risk’ is a testament to the amount of information that can be gleaned from an assessment of the performances of vintage pools.

Vintage pools offer numerous perspectives of risk. They allow for a deep appreciation of the effects of loan maturation, and can also point toward the impact of external factors, such as changes in real estate prices, origination standards, and other macroeconomic factors, by highlighting measurable differences in vintage to vintage performance.

What is a vintage pool?

By the Experian definition, vintage pools are created by taking a sample of all consumers who originated loans in a specific period, perhaps a certain quarter, and tracking the performance of the same consumers and loans through the life of each loan.

Vintage pools can be analyzed for various characteristics, but three of the most relevant are:

* Vintage delinquency, which allows for an understanding of the repayment trends within each pool;

* Payoff trends, which reflect the pace at which pools are being repaid; and

* Charge-off curves, which provide insights into the charge-off rates of each pool.

The credit grade of each borrower within a vintage pool is extremely important in understanding the vintage characteristics over time, and credit scores are based on the status of the borrower just before the new loan was originated. This process ensures that the new loan origination and the performance of the specific loan do not influence the borrower’s credit score. By using this method of pooling and scoring, each vintage segment contains the same group of loans over time – allowing for a valid comparison of vintage pools and the characteristics found within.

Once vintage pools have been defined and created, the possibilities for this data are numerous...

-- By Kari Michel

What is your credit risk score?  Is it 300, 700, 900 or something in between?  In order to understand what it means, you need to know which score you are referencing.  Lenders use many different scoring models to determine who qualifies for a loan and at what interest rate. For example, Experian has developed many scores, such as VantageScore®..  Think of VantageScore® as just one of many credit scores available in the marketplace.

While all credit risk models have the same purpose, to use credit information to assess risk, each credit model is unique in that each one has its own proprietary formula that combines and calculates various credit information from your credit report.  Even if lenders used the same credit risk score, the interpretation of risk depends on the lender, and their lending policies and criteria may vary.

Additionally, each credit risk model has its own score range as well.  While the score range may be relatively similar to another score range, the meaning of the score may not necessarily be the same.   For example, a 640 in one score may not mean the same thing or have the same credit risk as a 640 for another score.  It is also possible for two different scores to represent the same level of risk. If you have a good credit score with one lender, you will likely have a good score with other lenders, even if the number is different.
 

Part 2

Two additional tactics that you should incorporate into your relationship management penetration strategy include:

• Conducting relationship reviews in addition to loan reviews; and
• Identifying and proactively monitoring changes in client behavior.

Relationship reviews
Relationship reviews are a comprehensive and thorough examination of the client’s business and should be the foundation for your relationship management process. They seek to provide both the client and the relationship manager with a roadmap for the upcoming 14- to 16-month period by identifying specific goals and concerns, as well as constructing a snapshot of the client today. The purpose of a relationship review is to understand the broader direction.  Bluntly put, an annual loan review is not a penetration activity. Its primary focus is to verify the ongoing credit worthiness of an existing deal in the books. More details will come about this topic in a future blog.

Monitoring changes in behavior
Monitoring changes in client behavior through the use of “activity thresholding” is quickly becoming a mainstay in the financial industry. The idea isn’t new; however, the application of the concept to penetration is. Instead of having changes in credit score trigger an alert related to risk management and mitigation, we would instead look at thresholds related to line usage, number of deposit transactions, changes in average deposit amount and credit card transactions.

These kinds of client behaviors and activities provide insight into what is occurring within a clients business and as such, allow us to provide recommendations for products and services that are meaningful and appropriate.
 
 

In my last blog, I talked about the overall need for a vision for your loan portfolio and the similarity of a loan portfolio to that of an investment portfolio.  Now that we have that vision in place, we can focus on the overall strategy to achieve that vision. 

A valuable first step in managing an investment portfolio is to establish a targeted value by a certain time (say, our targeted retirement age).  Similarly, it’s important that we establish our vision for the loan portfolio regarding overall diversification, return and risk levels.

The next step is to create a strategy to achieve the targeted state.  By focusing on the gaps between our current state and the vision state we have created, we can develop an action plan for achieving the future/vision state.  I am going to introduce some rather unique ideas here. 

Consider which of your portfolio segments are overweight?  One that comes to mind would be the commercial real estate portfolio.  The binge that has taken place over the past five plus years has resulted in an unhealthy concentration of loans in the commercial real estate segment.  In this one area alone, we will face the greatest challenge of right-sizing our portfolio mix and achieving the appropriate risk model per our vision. 

We have to assess our overall credit risk in the portfolios next.  For small business and consumer portfolios, this is relatively easy using the various credit scores that are available to assess the current risk.  For the larger commercial and industrial portfolios and the commercial real estate portfolios, we must employ some more manual processes to assess risk.  Unfortunately, we have to perform appropriate risk assessments (current up-to-date risk assessments) in order to move on to the next stage of this overall process (which is to execute on the strategy).

Once we have the dollar amounts of either growth or divestiture in various portfolio segments, we can employ the risk assessment to determine the appropriate execution of either growth or divestiture.

Stick with me on this topic because in my next blog we will discuss appropriate risk assessment methodologies and determine appropriate portfolio distributions/segmentations.

Whenever an industry encounters problems, the natural tendency is to play the blame game.  In the banking industry, credit risk managers are looking for who or what to blame for the tide of charge offs and delinquencies in their under-performing loan portfolios and in their commercial loan origination operations.  Credit scoring has definitely taken it on the chin as an easy target during 2008. 

Is credit scoring the problem? Absolutely not! 

As with anything, the more complacent we become…and the more we “turn off our brains” and stop thinking…the more risk we assume.  The more we solely rely upon the credit score alone, the more we subject ourselves to the risks inherent in “score and go” lending.

We are all well aware that credit scoring measures propensity to repay and not capacity to repay.  Over the past several years, the propensity to repay has been boosted by ever-increasing real estate values and by the refinance boom.  For example, some consumers have been able to survive on a 50 percent debt–to- income due to constant use of credit cards …by paying off those cards with a home mortgage refinance.  That set of behaviors would have shown a propensity to repay…but  was it ever acceptable to have 50 percent of your income go to debt payments?! 

Statistically it may have worked for a few years, but once real estate values stopped escalating, the problem with lack of capacity to repay reared its ugly head. 

When it comes to risk management, let’s get back to reality and sound principles.