Decision Analytics | Covered Accounts

-- by Kari Michel

What is Basil II?  Basel II is the international convergence of Capital Measurement and Capital Standards. It is a revised framework and is the second iteration of an international standard of laws. The purpose of Basel II is to create an international standard that banking regulators can use when creating regulations about how much capital banks need to put aside to guard against the types of financial and operations risk banks face.  Basel II ultimately implements standards to assist in maintaining a healthy financial system. 

The business challenge

The framework for Basel II compels the supervisors to ensure that banks implement credit rating techniques that represent their particular risk profile.  Besides the risk inputs (Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD)) calculation, the final Basel accord includes the “use test” requirement which is the requirement for a firm to use an advanced approach more widely in its business and met merely for calculation of regulatory capital.

Therefore many financial institutions are required to make considerable changes in their approach to risk management (i.e. infrastructure, systems, processes, data requirements).  Experian is a leading provider of risk management solutions -- products and services for the new Basel Capital Accord (Basel II).  Experian’s approach includes consultancy, software, and analytics tailored to meet the lender’s Basel II requirements.

 

- 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 Matt Ehrlich

My last entry covered the benefits of consortium databases and industry collaboration in general as a proven and technologically feasible method for combating fraud across industries.  They help minimize fraud losses.  So – with some notable exceptions – why are so few industries and companies using fraud consortiums and known fraud databases?

In my experience, the reasons typically boil down to two things: reluctance to share data and perception of ROI.  I say "perception of ROI" because I firmly believe the ROI is there – in fact it grows with the number of consortium participants. 

First, reluctance to share data seems to stem from a few areas. One is concern for how that data will be used by other consortium members.  This is usually addressed through compelling reciprocation of data contribution by all members (the give to get model) as well as strict guidelines for acceptable use. 

In today’s climate of hypersensitivity, another concern – rightly so – is the stewardship of Personally Identifiable Information (PII).  Given the potentially damaging effects of data breaches to consumers and businesses, smart companies are extremely cautious and careful when making decisions about safeguarding consumer information.  So how does a data consortium deal with this?  Firewalls, access control lists, encryption, and other modern security technologies provide the defenses necessary to facilitate protection of information contributed to the consortium. 

So, let’s assume we’ve overcome the obstacles to sharing one’s data.  The other big hurdle to participation that I come across regularly is the old “what’s in it for me” question.  Contributors want to be sure that they get out of it what they put into it.  Nobody wants to be the only one, or the largest one, contributing records. 

In fact, this issue extends to intracompany consortiums as well.  No line of business wants to be the sole sponsor just to have other business units come late to the party and reap all the benefits on their dime.  Whether within companies or across an industry, it’s obvious that mutual funding, support, equitable operating rules, and clear communication of benefits – to those contributors both big and small – is necessary for fraud consortiums to succeed. 

To get there, it’s going to take a lot more interest and participation from industry leaders.  What would this look like? I think we’d see a large shift in companies’ fraud columns: from “Discovered” to “Attempted”.  This shift would save time and money that could be passed back to the legitimate customers.  More participation would also enable consortiums to stay on top of changing technology and evolving consumer communication styles, such as email, text, mobile banking, and voice biometrics to name a few.

-- by Matt Ehrlich

There was a recent discussion among members of the Anti Fraud experts group on LinkedIn regarding collaboration among financial institutions to combat fraud.  Most posters agreed on the benefits of such collaboration but were cynical when it came to anything of substance, such as a shared data network, getting off the ground.  I happen to agree with some of the opinions on the primary challenges faced in getting cross industry (or even single industry!) cooperation to prevent both consumer and commercial fraud.  Those being: 1) sharing data and 2) return on investment.

Despite the challenges, there are some fraud prevention and “negative” file consortium databases available in the market as fraud prevention tools.  They’re often used in conjunction with authentication products in an overall risk based authentication / fraud deterrence strategy. Some are focused on the Demand Deposit Account (DDA) market, such as Fidelity’s DebitBureau, while others, like Experian’s own National Fraud Database, address a variety of markets.  Early Warning Services has a database of both “account abuse” – aka DDA financial mismanagement – and fraud records.  Still others like Ethoca and the UK’s 192.com seem focused on merchant data and online retailers.  

Regardless of the consortium, they share some common traits.  Most:

- fall under Fair Credit Reporting Act regulation
- are used in the acquisition phase as part of the new account decision
- require contribution of data to access the shared data network

Given the seemingly general reluctance to participate in fraud consortiums, as evidenced by the group described above, how do we assess value in these consortium databases?  Well, for one, most U.S. banks and credit unions participate in and contribute customer behavior data to a consortium.  Safe to say, then, that the banking industry has recognized the value of collaboration and sharing data with each other – if not exclusively to minimize fraud losses but at least to manage potential risk at acquisition.  I’m speaking here of the DDA financial mismanagement data used under the guiding principle of “past performance predicts future results”. 

Consortium data that includes confirmed fraud records make the value of collaboration even more clear: a match to one of these records compels further investigation and a more cautious review of the transaction or decision.  With this much to gain, why aren’t more companies and industries rushing to join or form a consortium?

In my next post, I’ll explore the common objections to joining consortiums and what the future may look like.

 

-- by Kari Michel

We've recently discussed management of risk, collections strategy, credit attributes, and the like for the bank card, telco, and real estate markets. This blog will provide insights into the trends of the automotive finance market as of third quarter 2009.  In terms of credit quality, the market has been relatively steady in year-over-year comparisons.  The subprime group saw the biggest change in risk distribution from 3Q08, with a -3.74 percent shift.

Overall, balances have declined to just over $673 billion (- 4 percent).  In 3Q09, banks held the largest total of outstanding automotive balances of $241 billion (with captive auto next at $203 billion).  Credit unions had the largest increase from 3Q08 (with $5 billion) and the finance/other group had the largest decrease in balances (- $23 billion).

How are automotive loans performing?  Total 30- and 60-day delinquencies are still on the rise, but the rate of increase of 30-day delinquencies appears to be slowing.



New originations are dominating in the Prime plus market (66 percent), up by 10 percent.  Lending criteria has tightened and, as a result, we see scores on both new and used vehicles continue to increase.  For new buyers, over 83 percent are Prime plus.  For used buyers, over 53 percent are Prime plus.  The average credit score changed from 762 in 3Q08 to 775 in 3Q09 -- up 13 points for new vehicles.  For used vehicles in the same time period: 670 to 684, up 14 points.

Lastly, let’s take a look at how financing has changed from 3Q08 to 3Q09.  The financed amounts and monthly payments have dropped year-over-year as well as the average term and average rate.


Source:  State of the Automotive Finance Market, Third Quarter 2009 by Melinda Zabritski, director of Automotive Credit at Experian and Experian-Oliver Wyman Market Intelligence Reports

 

--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 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 Kelly Kent

A recent article in the Boston Globe talked about the lack of incentive for banks to perform wide-scale real estate loan modifications due to the lack of profitability for lenders in the current government-led program structure. The article cited a recent study by the Boston Federal Reserve that noted up to 45 percent of borrowers who receive loan modifications end up in arrears again afterwards. On the other hand, around 30 percent of borrowers cured without any external support from lenders - leading them to believe that the cost and effort required modifying delinquent loans is not a profitable or not required proposition.

Adding to this, one of the study’s authors was quoted as saying “a lot of people you give assistance to would default either way or won’t default either way.”

The problem that lenders face is that although they have the knowledge that certain borrowers are prone to re-default, or cure without much assistance – there has been little information available to distinguish these consumers from each other.  Segmenting these customers is the key to creating a profitable process for loan modifications, since identification of the consumer in advance will allow lenders to treat each borrower in the most efficient and profitable manner.

In considering possible solutions, the opportunity exists to leverage the power of credit data, and credit attributes to create models that can profile the behaviors that lenders need to isolate. Although the rapid changes in the economy have left many lenders without a precedent behavior in which to model, the recent trend of consumers that re-default is beginning to provide lenders with correlated credit attributes to include in their models.

Credit attributes were used in a recent study on strategic defaulters by the Experian-Oliver Wyman Market Intelligence Reports, and these attributes created defined segments that can assist lenders with implementing profitable loan modification policies and decisioning strategies.

 

--by Heather Grover

In my previous entry, I covered how fraud prevention affected the operational side of new DDA account opening. To give a complete picture, we need to consider fraud best practices and their impact on the customer experience.

As earlier mentioned, the branch continues to be a highly utilized channel and is the place for “customized service.” In addition, for retail banks that continue to be the consumer's first point of contact, fraud detection is paramount IF we should initiate a relationship with the consumer. Traditional thinking has been that DDA accounts are secured by deposits, so little risk management policy is applied. The reality is that the DDA account can be a fraud portal into the organization’s many products.

Bank consolidations and lower application volumes are driving increased competition at the branch – increased demand exists to cross-sell consumers at the point of new account opening. As a result, banks are moving many fraud checks to the front end of the process: know your customer and Red Flag guideline checks are done sooner in the process in a consolidated and streamlined fashion. This is to minimize fraud losses and meet compliance in a single step, so that the process for new account holders are processed as quickly through the system as possible.

Another recent trend is the streamlining of a two day batch fraud check process to provide account holders with an immediate and final decision. The casualty of a longer process could be a consumer who walks out of your branch with a checkbook in hand – only to be contacted the next day to tell that his/her account has been shut down. By addressing this process, not only will the customer experience be improved with  increased retention, but operational costs will also be reduced.

Finally, relying on documentary evidence for ID verification can be viewed by some consumers as being onerous and lengthy. Use of knowledge based authentication can provide more robust authentication while giving assurance of the consumer’s identity. The key is to use a solution that can authenticate “thin file” consumers opening DDA accounts. This means your out of wallet questions need to rely on multiple data sources – not just credit. Interactive questions can give your account holders peace of mind that you are doing everything possible to protect their identity – which builds the customer relationship…and your brand.

--by Heather Grover

In past client and industry talks, I’ve discussed the increasing importance of retail branches to the growth strategy of the bank. Branches are the most utilized channel of the bank and they tend to be the primary tool for relationship expansion. Given the face-to-face nature, the branch historically has been viewed to be a relatively low-risk channel needing little (if any) identity verification – there are less uses of robust risk-based authentication or out of wallet questions.

However, a now well-established fraud best practice is the process of doing proper identity verification and fraud prevention at the point of DDA account opening. In the current environment of declining credit application volumes and approval across the enterprise, there is an increased focus on organic growth through deposits.  Doing proper vetting during DDA account openings helps bring your retail process closer in line with the rest of your organization’s identity theft prevention program. It also provides assurance and confidence that the customer can now be cross-sold and up-sold to other products.

A key industry challenge is that many of the current tools used in DDA are less mature than in other areas of the organization. We see few clients in retail that are using advanced fraud analytics or fraud models to minimize fraud – and even fewer clients are using them to automate manual processes - even though more than 90 percent of DDA accounts are opened manually.

A relatively simple way to improve your branch operations is to streamline your existing ID verification and fraud prevention tool set:

1. Are you using separate tools to verify identity and minimize fraud?

Many providers offer solutions that can do both, which can help minimize the number of steps required to process a new account;

2. Is the solution realtime?

To the extent that you can provide your new account holders with an immediate and final decision, the less time and effort you’ll spend after they leave the branch finalizing the decision;

3. Does the solution provide detail data for manual review?

This can help save valuable analyst time and provider costs by limiting the need to do additional searches.

In my next post, we’ll discuss how fraud prevention in DDA impacts the customer experience.

--by Keir Breitenfeld

Account management fraud risks: I “think” I know who I’m dealing with…

Risk of fraudulent account activity does not cease once an application has been processed with even the most robust authentication products and tools available. 

These are a few market dynamics are contributing to increased fraud risk to existing accounts:

-          The credit crunch is impacting bad guys too! Think it’s hard to get approved for a credit account these days? The same tightened lending practices good consumers now face are also keeping fraudsters out of the “application approval” process too. While that may be a good thing in general, it has caused a migratory focus from application fraud to account takeover fraud. 

-          Existing and viable accounts are now much more appealing to fraudsters given a shortage of application fraud opportunities, as financial institutions have reduced solicitation volume.

A few other interesting challenges face organizations with regards to an institution’s ability to minimize fraud losses related to existing accounts:

-  Social engineering — the "human element" is inherent in a call center environment and critical from a customer experience perspective. This factor offers the opportunity for fraudsters to manipulate representatives to either gain unauthorized access to accounts or, at the very least, collect consumer and account information that may help them perpetrate fraud later.

- Automatic Number Identification (ANI) spoofing — this technology allows a caller to alter the true displayable number from which he or she is calling to a falsely portrayed number. It's difficult, if not impossible, to find a legitimate use for this technology. However, fraudsters find this capability quite useful as they try to circumvent what was once a very effective method of positively authenticating a consumer based on a "good" or known incoming phone number. With ANI spoofing in play, many call centers are now unable to confidently rely on this once cost-effective and impactful method of authenticating consumers.

--Kelly Kent

In a recent presentation conducted by The Tower Group, “2010 Top 10 Business Drivers, Strategic Responses, and IT Initiatives in Bank Cards,” the conversation covered many of the challenges facing the credit card business in 2010.  When discussing the shift from “what it was," to “what it is now” for many issues in the card industry, one specific point caught my attention – the perception of unused credit lines – and the change in approach from lenders encouraging balance load-up to the perception that unused credit lines now represent unknown vulnerability to lenders.

Using market intelligence assets at Experian, I thought I would take a closer look at some of the corresponding data credit score profile trends to see what color I could add to this insight. Here is what I found:

• Total unused bankcard limits have decreased by $750 billion from Q3 2008 to Q3 2009
• By risk segment, the largest decline in unused limits has been within the VantageScore® A consumer – the super prime consumer – where unused limits have dropped by $420 billion
• More than 82 percent of unused limits reside with VantageScore A and B consumers – the super-prime and prime consumer segments

So what does this mean to risk management today? If you subscribe to the approach that unused limits now represent unknown vulnerability, then this exposure does not reside with traditional “risky” consumers, rather it resides with consumers usually considered to be the least risky. 

So this is good news, right? Well, maybe not.

Vintage analysis of recent credit trends shows that vulnerability within the top score tiers might represent more risk than one would suspect. Delinquency trends for VantageScore A and B consumers within recent vintages (2006 through 2008) show deteriorating rates of delinquency from each year’s vintage to the next. Despite a shift in loan origination volumes towards this group, the performance of recent prime and super-prime originations shows deterioration and underperformance against historical patterns.

If The Tower Group’s read on the market is correct, and unused credit now represents vulnerability and not opportunity, it would be wise for lenders to reconsider where and how yesterday’s opportunity has become today’s risk.

--Kari Michel

Lenders are looking for ways to improve their collections strategy as they continue to deal with unprecedented consumer debt, significant increases in delinquency, charge-off rates and unemployment and, declining collectability on accounts.

Improve collections
To maximize recovered dollars while minimizing collections costs and resources, new collections strategies are a must. The standard assembly line “bucket” approach to collection treatment no longer works because lenders can not afford the inefficiencies and costs of working each account equally without any intelligence around likelihood of recovery. Using a segmentation approach helps control spend and reduces labor costs to maximize the dollars collected. Credit based data can be utilized in decision trees to create segments that can be used with or without collection models. For example, below is a portion of a full decision tree that shows the separation in the liquidation rates by applying an attribute to a recovery score. 

This entire segment has an average of 21.91 percent liquidation rate. The attribute applied to this score segment is the aggregated available credit on open bank card trades updated within 12 months. By using just this one attribute for this score band, we can see that the liquidation rates range from 11 to 35 percent. Additional attributes can be applied to grow the tree to isolate additional pockets of customers that are more recoverable, and identify segments that are not likely to be recovered. From a fully-developed segmentation analysis, appropriate collections strategies can be determined to prioritize those accounts that are most likely to pay, creating new efficiencies within existing collection strategies to help improve collections.

--by Monica Bellflower

I received a call on my cell phone the other day. It was my bank calling because a transaction outside of my normal behavior pattern tripped a flag in their fraud models. “Hello!" said the friendly, automated voice, “I’m calling from [bank name] and we need to talk to you about some unusual transaction activity on your account, but before we do, I need to make sure Monica Bellflower has answered the phone. We need to ask you a few questions for security reasons to protect your account. Please hold on a moment.” 

At this point, the IVR (Interactive Voice Response) system invoked a Knowledge Based Authentication session that the IVR controlled. The IVR, not a call center representative, asked me the Knowledge Based Authentication questions and confirmed the answers with me. 

When the session was completed, I had been authenticated, and the friendly, automated voice thanked me before launching into the list of transactions to be reviewed. Only when I questioned the transaction was I transferred, immediately – with no hold time, to a human fraud account management specialist. The entire process was seamless and as smooth as butter.

Using IVR technology is not new, but using IVR to control a Knowledge Based Authentication session is one way of controlling operational expenses. An example of this is reducing the number of humans that are required, while increasing the ROI made in both the Knowledge Based Authentication tool and the IVR solution. 

From a risk management standpoint, the use of decisioning strategies and fraud models allows for the objective review of a customer’s transactions, while employing fraud best practices. After all, an IVR never hinted at an answer or helped a customer pass Knowledge Based Authentication, and an IVR didn't get hired in a call center for the purpose of committing fraud.  

These technologies lend themselves well, to fraud alerts and identity theft prevention programs, and also to account management activities. Experian has successfully integrated Knowledge Based Authentication with IVR as part of relationship management and/or risk management solutions. 

--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 Jeff Bernstein

In my last blog, I discussed the basic concept of a maturation curve, as illustrated below:

Exhibit 1

In Exhibit 1, we examine different vintages beginning with those loans originated by year during Q2 2002 through Q2 2008. The purpose of the vintage analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as delinquency maturation curve.

The X-axis represents a timeline in months, from month of origination.  Furthermore, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio.  Those vintage analyses that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards.

So how can you use a maturation curve as a useful portfolio management tool?

As a consultant, I spend a lot of time with clients trying to understand issues, such as why their charge-offs are higher than plan (budget).  I also investigate whether the reason for the excess credit costs are related to collections effectiveness, collections strategy, collections efficiency, credit quality or a poorly conceived budget.

I recall one such engagement, where different functional teams within the client’s organization were pointing fingers at each other because their budget evaporated. One look at their maturation curves and I had the answers I needed. I noticed that two vintages per year had maturation curves that were pointed due north, with a much steeper curve than all other months of the year. Why would only two months or vintages of originations each year be so different than all other vintage analyses in terms of performance?

I went back to my career experiences in banking, where I worked for a large regional bank that ran marketing solicitations several times yearly. Each of these programs was targeted to prospects that, in most instances, were out-of-market, or in other words, outside of the bank’s branch footprint.

Bingo! I got it! The client was soliciting new customers out of his
market, and was likely getting adverse selection. While he targeted the “right” customers – those with credit scores and credit attributes within an acceptable range, the best of that targeted group was not interested in accepting their offer, because they did not do business with my client, and would prefer to do business with an in-market player.

Meanwhile, the lower grade prospects were accepting the offers, because it was a better deal than they could get in-market. The result was adverse selection...and what I was staring at was the "smoking gun" I’d been looking for with these two-a-year vintages (vintage analysis) that reached the moon in terms of delinquency.

That’s the value of building a maturation curve analysis – to identify
specific vintages that have characteristics that are more adverse than others.  I also use the information to target those adverse populations and track the performance of specific treatment strategies aimed at containing losses on those segments. You might use this to identify which originations vintages of your home equity portfolio are most likely to migrate to higher levels of delinquency; then use credit bureau attributes to identify specific borrowers for an early lifecycle treatment strategy.

As that beer commercial says – “brilliant!”