Decision Analytics | Financial Risk Management

-- 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 Amanda Roth

Last week, we discussed how pricing with competition is important to ensure sound decision practices are being implemented in the domains of loan pricing and profitability.  The extreme of pricing too high for the market can obviously be detrimental to your organization.  The other extreme can be just as dangerous.

Pricing for your profitability, regardless of what the competition is charging in your area, has a few potential issues associated with it regarding management of risk.  For example, the statistics state you can charge 5 percent in your “A” tier and still be profitable, but the competition is charging 7.5 percent for the same tier.  You may be thinking that by offering 5 percent you will attract the “best of the best” to your organization.  However, what your statistics may not be showing you is the risk outside of your applicant base.  If you significantly change the customers you are bringing in, does your risk increase as well, ultimately increasing the cost associated with each loan?   Increased costs will reduce or even eliminate the profitability you had expected.

A second potential issue is setting the expectation within the marketplace.  It is often understood with the consumers that when changes occur to the interest rate at the federal level, there will be changes at their local financial institution.  These changes are often very small.  By undercutting your competition by such an extreme amount, your customers may question any attempts to raise rates more than 50bp, if you do experience increased costs as a result of the earlier situation or any other factors.  A safer strategy would be to charge between 6.5 percent and 7 percent, which allows you to obtain some of the best customers, ensure stability within the market, and take advantage of additional profitability while it is available.  This is definitely a winning strategy for all -- and an important consideration as you develop your portfolio risk management objectives.

-- 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 Kelly Kent

As the economic environment changes on what feels like a daily basis, the importance of having information about consumer credit trends and the future direction of credit becomes invaluable for planning and achieving strategic goals. I recently had the opportunity to speak with members of the collections industry about collections strategy and collections change management -- and discussed the use of business intelligence data in their industry. I was surprised at how little analysis was conducted in terms of anticipating strategic changes in economic and credit factors that impact the collections business. Mostly, it seems like anecdotal information and media coverage is used to get ‘a feeling’ for the direction of the economy and thus the collections industry.

Clearly, there are opportunities to understand these high-level changes in more detail and as a result, I wanted to review some business intelligence capabilities that Experian offers – and to expand on the opportunities I think exist to for collections firms to leverage data and better inform their decisions:

* Experian possesses the ability to capture the entire consumer credit perspective, allowing collections firms to understand trends that consider all consumer relationships.

* Within each loan type, insights are available by analyzing loan characteristics such as, number of trades, balances, revolving credit limits, trade ages, and delinquency trends. These metrics can help define market sizes, relative delinquency levels and identify segments where accounts are curing faster or more slowly, impacting collectability.

* Layering in geographic detail can reveal more granular segment trends, creating segments for both macro and regional-level credit characteristics.

* Experian Business Intelligence has visibility to the type of financial institution, allowing for a market by market view of credit patterns and trends.

* Risk profiling by VantageScore can shed light on credit score trends, breaking down larger segments into smaller score-based segments and identifying pockets of opportunity and risk.

I’ll continue to consider the opportunities for collections firms to leverage business intelligence data in subsequent blogs, where I’ll also discuss the value of credit forecasting to the collections industry.

 

--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 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 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 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.

--by Wendy Greenawalt

Optimization has become a "buzz word" in the financial services marketplace, but some organizations still fail to realize all the possible business applications for optimization. As credit card lenders scramble to comply with the pending credit card legislation, optimization can be a quick and easily implemented solution that fits into current processes to ensure compliance with the new regulations.

Optimizing decisions
Specifically, lenders will now be under strict guidelines of when an APR can be changed on an existing account, and the specific circumstances under which the account must return to the original terms. Optimization can easily handle these constraints and identify which accounts should be modified based on historical account information and existing organizational policies.

APR account changes can require a great deal of internal resources to implement and monitor for on-going performance. Implementing an optimized strategy tree within an existing account management strategy will allow an organization to easily identify consumer level decisions.  This can be accomplished while monitoring accounts through on-going batch processing.

New delivery options are now available for lenders to receive optimized strategies for decisions related to:

• Account acquisition
• Customer management
• Collections

Organizations who are not currently utilizing this technology within their  processes should investigate the new delivery options. Recent research suggests optimizing decisions can provide an improvement of 7-to-16 percent over current processes.

--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 Kennis Wong

It's true that intent is difficult to prove. It's also true that financial situations change. That's why financial institutions have not, yet, successfully fought off first-party fraud. However, there are some tell-tale signs of intent when you look at the consumer's behavior as a whole, particularly across all his/her financial relationships.

For example, in a classic bust out case, you would see that the consumer, with pristine credit history, applies for more and more credit cards while maintaining a relatively low balance and utilization across all issuers. If you graph the number of credit cards and number of credit applications over time, you would see two hockey-stick lines. When the accounts go bad, they do so at almost the same time. This pattern is not always apparent at the time of origination, that's why it's important to monitor frequently for account review and fraud database alerts.

On the other hand, consumers with financial difficulties have different patterns. They might have more credit lines over time, but you would see that some credit lines may go delinquent while others don't. You might also see that consumers cure some lines after delinquencies…you can see their struggle of trying to pay.

Of course the intent "pattern" is not always clear. When dealing with fraudsters in fraud account management, even with the help of the fraud database, fraud trends and fraud alert, change their behaviors and use new techniques.

--by Matt Ehrlich

On Friday, October 30th, the FTC again delayed enforcement of the “Red Flags” Rule – this time until June 1, 2010 – for financial institutions and creditors subject to the FTC’s enforcement.   Here’s the official release: http://www.ftc.gov/opa/2009/10/redflags.shtm. 

But this doesn’t mean, until then, businesses get a free pass.  The extension doesn’t apply to other federal agencies that have enforcement responsibilities for institutions under their jurisdiction.  And the extension also doesn’t alleviate an institution’s need to detect and respond to address discrepancies on credit reports.

Red Flag compliance

Implementing best practices to address the identity theft under the Red Flags Rule is not just the law, it’s good business.  The damage to reputations and consumer confidence from a problem gone unchecked or worse yet – unidentified – can be catastrophic.  I encourage all businesses – if they haven’t already done so – to use this extension as an opportunity to proactively secure a Red Flags Rule to ensure Red Flag compliance.  It’s an investment in protecting their most important asset – the customer.

--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 Mike Sutton

In today’s collections environment, the challenges of meeting an organization’s financial objectives are more difficult than ever.  Case volumes are higher, accounts are more difficult to collect and changing customer behaviors are rendering existing business models less effective.

When responding to recent events, it is not uncommon for organizations to take what may seem to be the easiest path to success — simply hiring more staff. Perhaps in the short-term there may appear to be cash flow improvements, but in most cases, this is not the most effective way to cope with long-term business needs. As incremental staff is added to compensate for additional workloads, there is a point of diminishing return on investment and that can be difficult to define until after the expenditures have been made. Additionally, there are almost always significant operational improvements that can be realized by introducing new technology.  Furthermore, the relevant return on investment models often forecast very accurately.

So, where should a collections department consider investing to improve financial results? The best option may not be the obvious choice, and the mere thought can make the most seasoned collections professionals shutter at the thought of replacing the core collections system with modern technology. That said, let’s consider what has changed in recent years and explore why the replacement proposition is not nearly as difficult or costly as in the past.

Collection Management Software
The collections system software industry is on the brink of a technology evolution to modern and next-generation offerings. Legacy systems are typically inflexible and do not allow for an effective change management program. This handicap leaves collections departments unable to keep up with rapidly changing business objectives that are a critical requirement in surviving these tough economic times. Today’s collections managers need to reduce operational costs while improving these objectives: reducing losses, improving cash flow and promoting customer satisfaction (particularly with those who pose a greater lifetime profit opportunity).  The next generation collections software squarely addresses these business problems and provides significant improvement over legacy systems. Not only is this modern technology now available, but the return on investment models are extremely compelling and have been proven in markets where successful implementations have already occurred.

As an example of modern collections technologies that can help streamline operations, check out the overview and brief demonstration that is on this link:

www.experian.com/decision-analytics/tallyman-demo.html.
 

--by Mike Sutton

I recently interviewed a number of Experian clients to determine how they believe their organizations and industry peers will prioritize collections process improvement over the next 24 months. Additional contributions were collected by written surveys. Here are several interesting observations:

All of the categories mentioned above were considered important by every respondent, but the most urgent priorities were not consistent across industries.

Red Flags Rule and commercial accounts

-- by Kristan Keelan

Most financial institutions are well underway in complying with the FTC’s ID Theft Red Flags Rule by:

1.  Identifying covered accounts  
2.  Determining what red flags need to be monitored
3.  Implementing a risk based approach 

However, one of the areas that seems to be overlooked in complying with the rule is the area of commercial accounts.  Did your institution include commercial accounts when identifying covered accounts?  You’re not alone if you focused only on consumer accounts initially.

Keep in mind that commercial credit and deposit accounts also can be included as covered accounts when there is a “reasonably foreseeable risk” of identity theft to customers or to safety and soundness.

Start by determining if there is a reasonably foreseeable risk of identity theft in a business or commercial account, especially in small business accounts.   Consider the risk of identity theft presented by the methods used to open business accounts, the methods provided to access business accounts, and previous experiences with identity theft on a business account.

I encourage you to revisit your institution’s compliance program and review whether commercial accounts have been examined closely enough.

-- by Kristan Keelan

What do you think of when you hear the word “fraud”?  Someone stealing your personal identity?  Perhaps the recent news story of the five individuals indicted for gaining more than $4 million from 95,000 stolen credit card numbers?  It’s unlikely that small business fraud was at the top of your mind.   Yet, just like consumers, businesses face a broad- range of first- and third-party fraud behaviors, varying significantly in frequency, severity and complexity. Business-related fraud trends call for new fraud best practices to minimize fraud.

First let’s look at first-party fraud.  A first-party, or victimless, fraud profile is characterized by having some form of material misrepresentation (for example, misstating revenue figures on the application) by the business owner without  that owner’s intent or immediate capacity to pay the loan item.  Historically, during periods of economic downturn or misfortune, this type of fraud is more common.  This intuitively makes sense — individuals under extreme financial pressure are more likely to resort to desperate measures, such as misstating financial information on an application to obtain credit.  

Third-party commercial fraud occurs when a third party steals the identification details of a known business or business owner in order to open credit in the business victim’s name.  With creditors becoming more stringent with credit-granting policies on new accounts, we’re seeing seasoned fraudsters shift their focus on taking over existing business or business owner identities.

Overall, fraudsters seem to be migrating from consumer to commercial fraud.   I think one of the most common reasons for this is that commercial fraud doesn’t receive the same amount of attention as consumer fraud.  Thus, it’s become easier for fraudsters to slip under the radar by perpetrating their crimes through the commercial channel.   Also, keep in mind that businesses are often not seen as victims in the same way that consumers are.  For example, victimized businesses aren’t afforded the protections that consumers receive under identity theft laws, such as access to credit information.   These factors, coupled with the fact that business-to-business fraud is approximately three-to-ten times more “profitable” per occurrence than consumer fraud, play a role in leading fraudsters increasingly toward commercial fraud.
 

-- by Kelly Kent

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

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

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

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

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

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