Decision Analytics | Credit Risk Models

- by Kelly Kent

A recent January 29, 2010 article in the Wall Street Journal * discussing the repurchasing of loans by banks from Freddie Mae and Fannie Mac included a simple, yet compelling statement that I feel is worth further analysis. The article stated that "while growth in subprime defaults is slowing, defaults on prime loans are accelerating." I think this statement might come as a surprise to some who feel that there is some amount of credit risk and economic immunity for prime and super-prime consumers – many of whom are highly sought-after in today’s credit market. To support this statement, I reference a few statistics from the Experian-Oliver Wyman Market Intelligence Reports:

• From Q1 2007 to Q1 2008, 30+ DPD mortgage delinquency rates for VantageScore A and B consumers remained flat (actually down 2%); while near-prime, subprime, and deep-subprime consumers experienced an increase of over 36% in 30+ rates.

• From Q4 2008 to Q4 2009, 30+ DPD mortgage delinquency rates for VantageScore A and B consumers increased by 42%; whereas consumers in the lower VantageScore tiers saw their 30+ DPD rate increase by only 23% in the same period

Clearly, whether through economic or some other form of impact, repayment practices of prime and super-prime, consumers have been changing as of late, and this is translating to higher delinquency rates. The call-to-action for lenders, in their financial risk management and credit risk modeling efforts, is increased attentiveness in assessing credit risk beyond just a credit score...whether this be using a combination of scores, or adding Premier Attributes into lending models – in order to fully assess each consumer’s risk profile.

*  http://online.wsj.com/article/SB10001424052748704343104575033543886200942.html
 

-- by Tom Hannagan

While waiting on the compilation of fourth quarter banking industry results, I thought it might be interesting to relate the commercial real estate (CRE) risk management position facing commercial banks from the third quarter. CRE risk is an important consideration in enterprise risk management and for loan pricing and profitability.

The slowdown in the global economy has affected CRE credit risk because of increased vacancy rates, halted development projects, and the loss of value affecting commercial properties. As CRE loans come up for renewal, many will find that there have equity deficits and that they are facing tightened credit standards.

If a commercial property loan started life at 80 percent loan to value, and the property value has dropped 25 percent, the renewed loan balance will be down at least 25 percent, requiring a substantial net payoff from the borrower. This net cash payoff requirement would be tough to accomplish in good times and all-but-impossible for many borrowers in this economy. After all, the main reason for the decline in property value to begin with is its reduced cash flow performance.

 Following the third quarter numbers, total U.S. commercial real estate is generally estimated at $3.4 to $3.5 trillion. Commercial banks owned just over half of that debt, or about $1.8 trillion according to Federal Reserve and FDIC sources. The (possibly only) good news with that total is that commercial banks owned a relatively small share of the commercial-mortgage-backed securities (CMBS) slice of CRE exposure. CMBS assets were 21 percent of total CRE credit or $714 billion, but banks owned a total of $54 billion, which represented only 3 percent of total bank CRE assets. Unfortunately, the opposite is true for construction lending. U.S. banks, in total, had $486 to $534 billion (depending on the source) in construction and land loans, representing 27 percent to 30 percent of banks’ total CRE holdings. 

The true credit risk management picture is much more revealing if we cut the numbers by bank size. According to Deutsche Bank research, the largest 97 banks (those with over $10 billion in total assets) had $14.8 trillion in total assets and $1.0 trillion of the banking industry’s CRE credits.  This amounts to about 7 percent of the total assets for this group of larger banks. The 7,500 community banks, with aggregate assets of $2 trillion, had about $786 billion in CRE lending. This amounts to about 28 percent of total assets. That is roughly four times the level of exposure found in the larger banks. The 7 percent level of credit risk average exposure at the large bank group is less than their average level of equity or risk-based capital. For the banks under the $10 billion level, the 28 percent level of CRE exposure is almost three times their average equity position.

The riskiest portion of CRE lending is clearly the construction and land development loans. The subtotals in this area confirm where the cumulative risk lies. Again, according to Deutsche Bank research, the largest 97 banks had $299 billion of the banking industry’s $534 billion in construction loans. Although this is 56 percent of total bank construction lending, it amounts to only 2 percent of this group’s total assets.  The 7,500 community banks had aggregate construction loans of $235 billion. This amounts to about 8.5 percent of total assets. That is a bit over four times the level of exposure found in the larger banks. The 2 percent level of construction credit risk exposure at the large bank group is one-fourth of their average level of common equity. At banks under the $10 billion level, the 8.5 percent level of CRE exposure, compared to total assets, is about the same as their average equity position.

According to Moody’s, bank have already taken about $90 billion in net loan losses in CRE assets through the third quarter of 2009. That means the industry has perhaps another $150 billion in write-offs coming. This would total $240 billion in CRE credit losses for the banking industry due to this economic downturn. That would equate to 13.3 percent of the banking industry’s share of total CRE credit. With the decline in commercial property values ranging from 10 percent to 40 percent, a 13 percent loss is certainly not a worst case scenario.

Banks have ramped up their loss reserves, and although the numbers aren’t out yet, we know many banks have used the fourth quarter 2009 to further bolster their allowances for loan and lease losses (ALLL). The larger the ALLL, the safer the risk-based equity account. Risk managers are aware of all of this and banks are very actively developing their strategies to handle the refunding requirements and, at the same time, be in a position to explain to regulators and external auditor how they are protecting shareholders. But the numbers are very daunting and not every bank will have enough net cash flow and risk equity to cover the inevitable losses.

-- by Kari Michel

The Credit Card Act has important implications on risk management, relationship management and assessing credit risk.  The Federal Reserve Board approved final rules implementing the Credit Card Act which will require credit card issuers to assess borrowers’ “ability to pay”.  The Credit Card Act added new Truth in Lending Act (TILA) Section 150 prohibiting a card issuer from opening a credit card account for a consumer, or increasing the credit limit applicable to a credit card account, unless the card issuer considers the consumer’s ability to make the required minimum payment.

The final rules explicitly allow for the use of “empirically derived, demonstrably and statistically sound models that reasonably estimate a consumer’s income or assets.”  The Board clarified that the card issuers are not obligated to obtain income or asset information directly from a consumer.  Instead, card issuers may also rely on information from third parties, subject to any applicable restrictions on information sharing. The rule provides that card issuers may use models that reasonably estimate a consumer’s income or assets.  This rule will become effective February 22, 2010.

Many lenders were relieved to hear that ‘ability to pay’ models can be used to comply with this new regulation.  Experian’s Income InsightSM addresses the requirements of the final Federal Reserve Board rules for assessment of ability to pay.  The model supports lenders’ compliance in a cost-effective and efficient manner.

--by Tom Hannagan

Apparently my last post on the role of risk management in the pricing of deposit services hit some nerve ends. That’s good. The industry needs its “nerve ends” tweaked after the dearth of effective risk management that contributed to the financial malaise of the last couple of years. Banks, or any business, can prosper by simply following their competitors’ marketing strategies and meeting or slightly undercutting their prices. The actions of competitors are an important piece of intelligence to consider, but not necessarily optimal for your bank to copy.

One question is regarding the “how-to” behind risk-based pricing (RBP) of deposits. The answer has four parts. Let’s see. First, because of the importance and size of the deposit business (yes, it’s a line of business) as a funding source, one needs to isolate the interest rate risk. This is done by transfer pricing, or in a sense, crediting the deposit balances for their marginal value as an offset to borrowing funds. This transfer price has nothing to do with the earnings credit rate used in account analysis – that is a merchandising issue used to generate fee income. Fees, resulting from account analysis, when not waived, affect the profitability of deposit services, but are not a risk element.

Two things are critical to the transfer of funding credit: 1) the assumptions regarding the duration, or reliability of the deposit balances and 2) the rate curve used to match the duration. Different types of deposit behave differently based on changes in rates paid. Checking account deposit funds tend to be very loyal or “sticky” - they don’t move around a lot (or easily) because of rate paid, if any. At the other extreme, time deposits tend to be very rate-sensitive and can move (in or out) for small incremental gains. Savings, money market and NOW accounts are in-between.

Since deposits are an offset (ultimately) to marginal borrowing, just as loans might (ultimately) require marginal borrowing, we recommend using the same rate curve for both asset and liability transfer pricing. The money is the same thing on both sides of the balance sheet and the rate curve used to fund a loan or credit a deposit should be the same. We believe this will help, greatly, to isolate IRR. It is also seems more fair when explaining the concept to line management.

Secondly, although there is essentially no credit risk associated with deposits, there is operational risk. Deposit make up most of the liability side of the balance sheet and therefore the lion’s share of institutional funding. Deposits are also a major source of operational expense. The mitigated operational risks such as physical security, backup processing arrangements, various kinds of insurance and catastrophe plans, are normal expenses of doing business and included in a bank’s financial statements. The costs need to be broken down by deposit category to get a picture of the risk-adjusted operating expenses.

The third major consideration for analyzing risk-adjusted deposit profitability is its revenue contribution. Deposit-related fee income can be a very significant number and needs to be allocated to particular deposit category that generates this income. This is an important aspect of the return, along with the risk-adjusted funding value of the balances. It will vary substantially for various deposit types. Time deposits have essentially zero fee income, whereas checking accounts can produce significant revenues.

The fourth major consideration is capital. There are unexpected losses associated with deposits that must be covered by risk-based capital – or equity. The unexpected losses include: unmitigated operational risks, any error in transfer pricing the market risk, and business or strategic risk. Although the unexpected losses associated with deposit products are substantially less than found in the lending products, they needs to be taken into account to have a fully risk-adjusted view. It is also necessary to be able to compare the risk-adjusted profit and profitability of such diverse services as found within banking. 

Enterprise risk management needs to consider all of the lines of business, and all of the products of the organization, on a risk-adjusted performance basis. Otherwise it is impossible to decide on the allocation of resources, including precious capital. Without this risk management view of deposits (just as with loans) it is impossible to price the services in a completely knowledgeable fashion. Good entity governance, asset and liability posturing, and competent line of business management, all require more and better risk-based profit considerations to be an important part of the intelligence used to optimally price deposits.

--by Kent Kelly

In a continuation of my previous entry, I’d like to take the concept of the first-mover and specifically discuss the relevance of this to the current bank card market.

Here are some statistics to set the stage:

• Q2 2009 bankcard origination levels are now at 54 percent of Q2 2008 levels
• In Q2 2009, bankcard originations for subprime and deep-subprime were down 63 percent from Q2 2008
• New average limits for bank cards are down 19 percent in Q2 2009 from peak in Q3 2008
• Total unused limits continued to decline in Q3 2009, decreasing by  $100 billion in Q3 2009

Clearly, the bank card market is experiencing a decline in credit supply, along with deterioration of credit performance and problematic delinquency trends, and yet in order to grow, lenders are currently determining the timing and manner in which to increase their presence in this market. In the following points, I’ll review just a few of the opportunities and risks inherent in each area that could dictate how this occurs.

Lender chooses to be a first-mover:

• Mining for gold – lenders currently have an opportunity to identify long-term profitable segments within larger segments of underserved consumers. Credit score trends show a number of lower-risk consumers falling to lower score tiers, and within this segment, there will be consumers who represent highly profitable relationships. Early movers have the opportunity to access these consumers with unrealized creditworthiness at their most receptive moment, and thus have the ability to achieve extraordinary profits in underserved segments.
 
• Low acquisition costs – The lack of new credit flowing into the market would indicate a lack of competitiveness in the bank card acquisitions space. As such, a first-mover would likely incur lower acquisitions costs as consumers have fewer options and alternatives to consider.
 
• Adverse selection - Given the high utilization rates of many consumers, lenders could face an abnormally high adverse selection issue, where a large number of the most risky consumers are likely to accept offers to access much needed credit – creating risk management issues.
 
• Consumer loyalty – Whether through switching costs or loyalty incentives, first-movers have an opportunity to achieve retention benefits from the development of new client relationships in a vacant competitive space.

Lender chooses to be a secondary or late-mover:

• Reduced risk by allowing first-mover to experience growing pains before entry. The implementation of new acquisitions and risk-based pricing management techniques with new bank card legislation will not be perfected immediately. Second-movers will be able to read and react to the responses to first movers’ strategies (measuring delinquency levels in new subprime segments) and refine their pricing and policy approaches.

• One of the most common first-mover advantages is the presence of switching costs by the customer. With minimal switching costs in place in the bank card industry, the ability for second-movers to deal with an incumbent is not one where switching costs are significant issues – second-movers would be able to steal market share with relative ease.

• Cherry-picked opportunities – as noted above, many previously attractive consumers will have been engaged by the first-mover, challenging the second-mover to find remaining attractive segments within the market. For instance, economic deterioration has resulted in short-term joblessness for some consumers who might be strong credit risks, given the return of capacity to repay. Once these consumers are mined by the first-mover, the second-mover will likely incur greater costs to acquire these clients.

Whether lenders choose to be first to market, or follow as a second-mover, there are profitable opportunities and risk management challenges associated with each strategy.  Academics and bloggers continue to debate the merits of each, (1)  but it is the ultimately lenders of today that will provide the proof.

[1] http://www.fastcompany.com/magazine/38/cdu.html

--by Roger Ahern

To calculate the expected business benefits of making an improvement to your decisioning strategies, you must first identify and prioritize the key metrics you are trying to positively impact.  For example, if one of your key business objectives is improved enterprise risk management, then some of the key metrics you seek to impact, in order to effectively address changes in credit score trends, could include reducing net credit losses through improved credit risk modeling and scorecard monitoring. Assessing credit risk is a key element of enterprise risk management and can addressed as part of your application risk management processes as well as other decisioning strategies that are applied at different points in the customer lifecycle. 

In working with our clients, Experian has identified 15 key metrics that can be positively impacted through optimizing decisions.  As you review the list of metrics below, you should identify those metrics that are most important to your organization.

• Approval rates
• Booking or activation rates
• Revenue
• Customer net present value
• 30/60/90-day delinquencies
• Average charge-off amount
• Average recovery amount
• Manual review rates
• Annual application volume
• Charge-offs (bad debt & fraud)
• Avg. cost per dollar collected
• Average amount collected
• Annual recoveries
• Regulatory compliance
• Churn or attrition

Based on Experian’s extensive experience working with clients around the world to achieve positive business results through optimizing decisions, you can expect between a 10 percent and 15 percent improvement in any of these metrics through the improved use of data, analytics and decision management software.

The initial high-level business benefit calculation, therefore, is quite important and straightforward.  As an example, assume your current approval rate for vehicle loans is 65 percent, the average value of an approved application is $200 and your volume is 75,000 applications per year.  Keeping all else equal, a 10 percent improvement in your approval rates (from 65 percent to 72 percent) would generate $10.7 million in incremental business value each year ($200 x 75,000 x .65 x 1.1).  To prioritize your business improvement efforts, you’ll want to calculate expected business benefits across a number of key metrics and then focus on those that will deliver the greatest value to your organization.

--by Tom Hannagan

This blog has often discussed many aspects of risk-adjusted pricing for loans. Loans, with their inherent credit risk, certainly deserve a lot of attention when it comes to risk management in banking. But, that doesn’t mean you should ignore the risk management implications found in the other product lines. Enterprise risk management needs to consider all of the lines of business, and all of the products of the organization. This would include the deposit services arena.

Deposits make up roughly 65 percent to 75 percent of the liability side of the balance sheet for most financial institutions, representing the lion’s share of their funding source. This is a major source of operational expense and also represents most of the bank’s interest expense. The deposit activity has operational risk, and this large funding source plays a huge role in market risk – including both interest rate risk and liquidity risk. It stands to reason that such risks are considered when pricing deposit services. Unfortunately it is not always the case. Okay, to be honest, it’s too rarely the case.

This raises serious entity governance questions. How can such a large operational undertaking, not withstanding the criticality of the funding implications, not be subjected to risk-based pricing considerations? We have seen warnings already that the current low interest rate environment will not last forever. When the economy improves and rates head upwards, banks need to understand the bottom line profit implications. Deposit rate sensitivity across the various deposit types is a huge portion of the impact on net interest income. Risk-based pricing of these services should be considered before committing to provide them.

Even without the credit risk implications found on the loan side of the balance sheet, there is still plenty of operational and market risk impact that needs to be taken into account from the liability side. When risk management is not considered and mitigated as part of the day-to-day management of the deposit line of business, the bank is leaving these risks completely to chance. This unmitigated risk increases the portion of overall risk that is then considered to be “unexpected” in nature and thereby increases the equity capital required to support the bank.

--by Roger Ahern

It’s been proven in practice many times that by optimizing decisions (through improved decisioning strategies, credit risk modeling, risk-based pricing, enhanced scoring models, etc.) you will realize significant business benefits in key metrics, such as net interest margin, collections efficiency, fraud referral rates and many more.  However, given that a typical company may make more than eight million decisions per year, which decisions should one focus on to deliver the greatest business benefit? 

In working with our clients, Experian has compiled the following list of relevant types of decisions that can be improved through improvements in decision analytics.  As you review the list below, you should identify those decisions that are relevant to your organization, and then determine which decision types would warrant the greatest opportunity for improvement.

• Cross-sell determination
• Prospect determination
• Prescreen decision
• Offer/treatment determination
• Fraud determination
• Approve/decline decision
• Initial credit line/limit/usage amount
• Initial pricing determination
• Risk-based pricing
• NSF pay/no-pay decision
• Over-limit/shadow limit authorization
• Credit line/limit/usage/ management
• Retention decisions
• Loan/payment modification
• Repricing determination
• Predelinquency treatment
• Early/late-stage delinquency treatment
• Collections agency placement
• Collection/recovery treatment

--Kelly Kent

In my previous two blogs, I introduced the definition of strategic default and compared and contrasted the population to other types of consumers with mortgage delinquency.  I also reviewed a few key characteristics that distinguish strategic defaulters as a distinct population.

Although I’ve mentioned that segmenting this group is important, I would like to specifically discuss the value of segmentation as it applies to loan modification programs and the selection of candidates for modification.

How should loan modification strategies be differentiated based on this population?

By definition, strategic defaulters are more likely to take advantage of loan modification programs. They are committed to making the most personally-lucrative financial decisions, so the opportunity to have their loan modified - extending their ‘free’ occupancy – can be highly appealing.  Given the adverse selection issue at play with these consumers, lenders need to design loan modification programs that limit abuse and essentially screen-out strategic defaulters from the population.

The objective of lenders when creating loan modification programs should be to identify consumers who show the characteristics of cash-flow managers within our study. These consumers often show similar signs of distress as the strategic defaulters, but differentiate themselves by exhibiting a willingness to pay that the strategic defaulter, by definition, does not. 

So, how can a lender make this identification?
Although these groups share similar characteristics at times, it is recommended that lenders reconsider their loan modification decisioning algorithms, and modify their loan modification offers to screen out strategic defaulters.  In fact, they could even develop programs such as equity-sharing arrangements whereby the strategic defaulter could be persuaded to remain committed to the mortgage.  In the end, strategic defaulters will not self-identify by showing lower credit score trends, by being a bank credit risk, or having previous bankruptcy scores, so lenders must create processes to identify them among their peers.

For more detailed analyses, lenders could also extend the Experian-Oliver Wyman study further, and integrate additional attributes such as current LTV, product type, etc. to expand their segment and identify strategic defaulters within their individual portfolios.

--Kelly Kent

A recent New York Times (1) article outlined the latest release of credit borrowing by the Federal Reserve, indicating that American’s borrowed less for the ninth-straight month in October. Nested within the statistics released by the Federal Reserve were metrics around reduced revolving credit demand and comments about how “Americans are borrowing less as they try to replenish depleted investments.”

While this may be true, I tend to believe that macro-level statements are not fully explaining the differences between consumer experiences that influence relationship management choices in the current economic environment.

To expand on this, I think a closer look at consumers at opposite ends of the credit risk spectrum tells a very interesting story. In fact, recent bank card usage and delinquency data suggests that there are at least a couple of distinct patterns within the overall trend of reducing revolving credit demand:

• First, although it is true that overall revolving credit balances are decreasing, this is a macro-level trend that is not consistent with the detail we see at the consumer level. In fact, despite a reduction of open credit card accounts and overall industry balances, at the consumer-level, individual balances are up – that’s to say that although there are fewer cards out there, those that do have them are carrying higher balances.

• Secondly, there are significant differences between the most and least-risky consumers when it comes to changes in balances. For instance, consumers who fall into the least-risky VantageScore® tiers, Tier A and B, show only 12 percent and 4 percent year-over-year balance increases in Q3 2009, respectively. Contrast that to the increase in average balance for VantageScore F consumers, who are the most risky, whose average balances increased more than 28 percent for the same time period.

So, although the industry-level trend holds true, the challenges facing the “average” consumer in America are not average at all – they are unique and specific to each consumer and continue to illustrate the challenge in assessing consumers' credit card risk in the current credit environment.

1 http://www.nytimes.com/2009/12/08/business/economy/08econ.html

--by Kari Michel

On September 28, 2009, the Federal Reserve Board (FRB) proposed rules implementing the Credit Card Act, which among other changes, will require lenders to consider a borrower’s “ability to pay” when making lending decisions and assessing credit risk.  The proposed rule is set to become effective on February 22, 2010.  The FRB Rule is not final, and lenders will have to assess their own compliance obligations under the final rule. 

Lenders of all sizes will need to have tools in place to comply with the new requirements.  Time is of the essence and lenders are intensifying their efforts to determine how they will assess their borrowers’ income and assets in conjunction with current obligations. What are you doing to prepare for the new proposed changes? 

Experian can help by providing lenders with a suite of income-related tools to assist with income verification and estimation.  Our income estimation product is FCRA and ECOA compliant, and can support lenders’ ability to comply with recent legislation. The products can further be used in acquisition, account management and collection processes, such as collections software.

 

--by Tom Hannagan

I was hoping someone would ask about these risk management terms…and someone did. The obvious answer is that the “A” and the “O” are reversed. But, there’s more to it than that. First, let’s see how the acronyms were derived. RORAC is Return on Risk-Adjusted Capital. RAROC is Risk-Adjusted Return on Capital. Both of these five-letter abbreviations are a step up from ROE.

This is natural, I suppose, since ROE, meaning Return on Equity of course, is merely a three-letter profitability ratio. A serious breakthrough in risk management and profit performance measurement will have to move up to at least six initials in its abbreviation. Nonetheless, ROE is the jumping-off point towards both RORAC and RAROC.

ROE is generally Net Income divided by Equity, and ROE has many advantages over Return on Assets (ROA), which is Net Income divided by Average Assets. I promise, really, no more new acronyms in this post.

The calculations themselves are pretty easy. ROA tends to tell us how effectively an organization is generating general ledger earnings on its base of assets.  This used to be the most popular way of comparing banks to each other and for banks to monitor their own performance from period to period. Many bank executives in the U.S. still prefer to use ROA, although this tends to be those at smaller banks.

ROE tends to tell us how effectively an organization is taking advantage of its base of equity, or risk-based capital. This has gained in popularity for several reasons and has become the preferred measure at medium and larger U.S. banks, and all international banks. One huge reason for the growing popularity of ROE is simply that it is not asset-dependent. ROE can be applied to any line of business or any product. You must have “assets” for ROA, since one cannot divide by zero. Hopefully your Equity account is always greater than zero. If not, well, lets just say it’s too late to read about this general topic.

The flexibility of basing profitability measurement on contribution to Equity allows banks with differing asset structures to be compared to each other.  This also may apply even for banks to be compared to other types of businesses. The asset-independency of ROE can also allow a bank to compare internal product lines to each other. Perhaps most importantly, this permits looking at the comparative profitability of lines of business that are almost complete opposites, like lending versus deposit services. This includes risk-based pricing considerations. This would be difficult, if even possible, using ROA.

ROE also tells us how effectively a bank (or any business) is using shareholders equity. Many observers prefer ROE, since equity represents the owners’ interest in the business. As we have all learned anew in the past two years, their equity investment is fully at-risk. Equity holders are paid last, compared to other sources of funds supporting the bank. Shareholders are the last in line if the going gets rough. So, equity capital tends to be the most expensive source of funds, carrying the largest risk premium of all funding options. Its successful deployment is critical to the profit performance, even the survival, of the bank. Indeed, capital deployment, or allocation, is the most important executive decision facing the leadership of any organization.

So, why bother with RORAC or RAROC? In short, it is to take risks more fully into the process of risk management within the institution. ROA and ROE are somewhat risk-adjusted, but only on a point-in-time basis and only to the extent risks are already mitigated in the net interest margin and other general ledger numbers. The Net Income figure is risk-adjusted for mitigated (hedged) interest rate risk, for mitigated operational risk (insurance expenses) and for the expected risk within the cost of credit (loan loss provision).

The big risk management elements missing in general ledger-based numbers include: market risk embedded in the balance sheet and not mitigated, credit risk costs associated with an economic downturn, unmitigated operational risk, and essentially all of the strategic risk (or business risk) associated with being a banking entity. Most of these risks are summed into a lump called Unexpected Loss (UL). Okay, so I fibbed about no more new acronyms. UL is covered by the Equity account, or the solvency of the bank becomes an issue.

RORAC is Net Income divided by Allocated Capital. RORAC doesn’t add much risk-adjustment to the numerator, general ledger Net Income, but it can take into account the risk of unexpected loss. It does this, by moving beyond just book or average Equity, by allocating capital, or equity, differentially to various lines of business and even specific products and clients. This, in turn, makes it possible to move towards risk-based pricing at the relationship management level as well as portfolio risk management.  This equity, or capital, allocation should be based on the relative risk of unexpected loss for the different product groups. So, it’s a big step in the right direction if you want a profitability metric that goes beyond ROE in addressing risk. And, many of us do.

RAROC is Risk-Adjusted Net Income divided by Allocated Capital. RAROC does add risk-adjustment to the numerator, general ledger Net Income, by taking into account the unmitigated market risk embedded in an asset or liability. RAROC, like RORAC, also takes into account the risk of unexpected loss by allocating capital, or equity, differentially to various lines of business and even specific products and clients. So, RAROC risk-adjusts both the Net Income in the numerator AND the allocated Equity in the denominator. It is a fully risk-adjusted metric or ratio of profitability and is an ultimate goal of modern risk management. 

So, RORAC is a big step in the right direction and RAROC would be the full step in management of risk. RORAC can be a useful step towards RAROC. RAROC takes ROE to a fully risk-adjusted metric that can be used at the entity level.  This  can also be broken down for any and all lines of business within the organization. Thence, it can be further broken down to the product level, the client relationship level, and summarized by lender portfolio or various market segments. This kind of measurement is invaluable for a highly leveraged business that is built on managing risk successfully as much as it is on operational or marketing prowess.

Please refer to my blogs five and six for more information about ROE and the term “unpredictable variability:”  http://www.decisionanalyticsblog.experian.com/blog/risk-based-pricing-2

--by Keir Breitenfeld
 
Many compliance regulations such the Red Flags Rule, USA Patriot Act, and ESIGN require specific identity elements to be verified and specific high risk conditions to be detected. However, there is still much variance in how individual institutions reconcile referrals generated from the detection of high risk conditions and/or the absence of identity element verification. With this in mind, risk-based authentication, (defined in this context as the “holistic assessment of a consumer and transaction with the end goal of applying the right authentication and decisioning treatment at the right time") offers institutions a viable strategy for balancing the following competing forces and pressures:

• Compliance – the need to ensure each transaction is approved only when compliance requirements are met;
• Approval rates – the need to meet business goals in the booking of new accounts and the facilitation of existing account transactions;
• Risk mitigation – the need to minimize fraud exposure at the account and transaction level.

A flexibly-designed risk-based authentication strategy incorporates a robust breadth of data assets, detailed results, granular information, targeted analytics and automated decisioning. This allows an institution to strike a harmonious balance (or at least something close to that) between the needs to remain compliant, while approving the vast majority of applications or customer transactions and, oh yeah, minimizing fraud and credit risk exposure and credit risk modeling.

 Sole reliance on binary assessment of the presence or absence of high risk conditions and identity element verifications will, more often than not, create an operational process that is overburdened by manual referral queues. There is also an unnecessary proportion of viable consumers unable to be serviced by your business. Use of analytically sound risk assessments and objective and consistent decisioning strategies will provide opportunities to calibrate your process to meet today’s pressures and adjust to tomorrow’s as well.
 
 
 

--by Roger Ahern

The value of a good decision can generate $150 or more in customer net present value, while the cost of a bad decision can cost you $1,000 or more.  For example, acquiring a new and profitable customer by making good prospecting and approval and pricing decisions and decisioning strategies may generate $150 or much more in customer net present value and help you increase net interest margin and other key metrics.  While the cost of a bad decision (such as approving a fraudulent applicant or inappropriately extending credit that ultimately results in a charge-off) can cost you $1,000 or more.

Why is risk management decisioning important?

This issue is critical because average-sized financial institutions or telecom carriers make as many as eight million customer decisions each year (more than 20,000 per day!).  To add to that, very large financial institutions make as many as 50 billion customer decisions annually.  By optimizing decisions, even a small 10-to-15 percent improvement in the quality of these customer life cycle decisions can generate substantial business benefit. 

Experian recommends that clients examine the types of decisioning strategies they leverage across the customer life cycle, from prospecting and acquisition, to customer management and collections.  By examining each type of decision, you can identify those opportunities for improvement that will deliver the greatest return on investment by leveraging credit risk attributes, credit risk modeling, predictive analytics and decision-management software.

--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 Tracy Bremmer

There has been a lot of hype these days about people strategically defaulting on their mortgage loans. In other words, a consumer is underwater on their house and so he/she makes a strategic decision to walk away from it. In these instances, the consumer is current on all of their non-mortgage accounts, but because the value of their home is less than what they owe, they make the decision to default on their mortgage loan.

Experian and Oliver Wyman teamed up to really dig into this population and determine these issues:

• Does this population really exist?
• If so, what are the characteristics of this population, such as assessing credit risk or bankruptcy scores?
• How should loan modification strategies be differentiated based on this population?

This blog will be one of a three-part series that addresses these questions. Let’s begin with the first question.

1.  Does this population really exist?
The quick answer is yes – this population does indeed exist. In fact, in 2008 strategic defaulters represented 18 percent of all mortgage defaults, up 500 percent from 2004. When we conducted our study we found there were varying populations that also existed when it came to mortgage defaults. In fact, we classified mortgage defaulters into five categories: strategic defaulter, cash flow manager, distressed defaulter, no non-real estate trades, and pay-downs.

We defined these populations as follows:

• Strategic defaulter - Borrowers who are delinquent on their mortgages, even when they can afford the payment, because their loan balance exceeds the value of their home,
• Cash flow manager - Borrowers facing delinquency issues with their mortgage because of temporary distress, but continue to make payments on all credit obligations,
• Distressed defaulter - Borrowers facing potential affordability issues that go delinquent on their mortgage along with other credit obligations,
• No non-real estate trades – Borrowers who are delinquent on their mortgage, however they do not have any other non-mortgage trades to evaluate if they have strategically defaulted or are in distress,
• Pay-downs – Borrowers who pay down their mortgage loan.

In my next blog, I will address the characteristic differences in behavior between these populations. Specifically, I will evaluate what characteristics make strategic defaulters stand out from the rest and what is unique about the cash flow managers.

Source: Experian-Oliver Wyman Market Intelligence Reports; Understanding Strategic Default in Mortgage topical study / webinar. August 2009.

--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 divided 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 Wendy Greenawalt 

In the last installment of my three part series dispelling credit attribute myths, we’ll discuss the myth that the lift achieved by utilizing new attributes is minimal, so it is not worth the effort of evaluating and/or implementing new credit attributes. First, evaluating accuracy and efficiency of credit attributes is hard to measure. Experian data experts are some of the best in the business and, in this edition, we will discuss some of the methods Experian uses to evaluate attribute performance.

When considering any new attributes, the first method we use to validate statistical performance is to complete a statistical head-to-head comparison. This method incorporates the use of KS (Kolmogorov–Smirnov statistic), Gini coefficient, worst-scoring capture rate or odds ratio when comparing two samples. Once completed, we implement an established standard process to measure value from different outcomes in an automated and consistent format. While this process may be time and labor intensive, the reward can be found in the financial savings that can be obtained by identifying the right segments, including:

• Risk models that better identify “bad” accounts and minimizing losses
• Marketing models that improve targeting while maximizing campaign dollars spent
• Collections models that enhance identification of recoverable accounts leading to more recovered dollars with lower fixed costs

Credit attributes
Recently, Experian conducted a similar exercise and found that an improvement of 2-to-22 percent in risk prediction can be achieved through the implementation of new attributes. When these metrics are applied to a portfolio where several hundred bad accounts are now captured, the resulting savings can add up quickly (500 accounts with average loss rate of $3,000 = $1.5M potential savings). These savings over time more than justify the cost of evaluating and implementing new credit attributes.

 

--by Wendy Greenawalt

This blog kicks off a three part series exploring some common myths regarding credit attributes. Since Experian has relationships with thousands of organizations spanning multiple industries, we often get asked the same types of questions from clients of all sizes and industries. One of the questions we hear frequently from our clients is that they already have credit attributes in place, so there is little to no benefit in implementing a new attribute set.

Our response is that while existing credit attributes may continue to be predictive, changes to the type of data available from the credit bureaus can provide benefits when evaluating consumer behavior. To illustrate this point, let’s discuss a common problem that most lenders are facing today-- collections. Delinquency and charge-off continue to increase and many organizations are having difficulty trying to determine the appropriate action to take on an account because consumer behavior has drastically changed regarding credit attributes.

New codes and fields are now reported to the credit bureaus and can be effectively used to improve collection-related activities. Specifically, attributes can now be created to help identify consumers who are rebounding from previous account delinquencies. In addition, lenders can evaluate the number and outstanding balances of collection or other types of trades.  This can be achieved while considering the percentage of accounts that are delinquent and the specific type of accounts affected after assessing credit risk. The utilization of this type of data helps an organization to make collection decisions based on very granular account data.  This is done while considering new consumer trends such as strategic defaulters. Understanding all of the consumer variables will enable an organization to decide if the account should be allowed to self-cure.  If so, immediate action should be taken or modification of account terms should be contemplated. Incorporating new data sources and updating attributes on a regular basis allows lenders to react to market trends quickly by proactively managing strategies.