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By: Andrew Gulledge Bridgekeeper: “What is the air-speed velocity of an unladen swallow?” King Arthur: “What do you mean?  An African or European swallow?” Here are some additional reasons why the concept of an “average fraud rate” is too complex to be meaningful. Different levels of authentication strength Even if you have two companies from the same industry, with the same customer base, the same fraudsters, the same natural fraud rate, counting fraud the same way, using the same basic authentication strategies, they still might have vastly different fraud rates.  Let’s say Company A has a knowledge-based authentication strategy configured to give them a 95% pass rate, while Company B is set up to get a 70% pass rate.  All else being equal, we would expect Company A to have a higher fraud rate, by virtue of having a less stringent fraud prevention strategy.  If you lower the bar you’ll definitely have fewer false positives, but you’ll also have more frauds getting through.  An “average fraud rate” is therefore highly dependent on the specific configuration of your fraud prevention tools. Natural instability of fraud behavior Fraud behavior can be volatile.  For openers, one fraudster seldom equals one fraud attempt.  Fraudsters often use the same techniques to defraud multiple consumers and companies, sometimes generating multiple transactions for each.  You might have, for example, a hundred fraud attempts from the same computer-tanned jackass.  Whatever the true ratio of fraud attempts to fraudsters is, you can be confident that your total number of frauds is unlikely to be representative of an equal number of unique fraudsters.  What this means is that the fraud behavior is even more volatile than your general consumer behavior, including general fraud trends such as seasonality.  This volatility, in and of itself, correlates to a greater degree of variance in fraud rates, further depleting the value of an “average fraud rate” metric. Limited fraud data It’s also worth noting that we only know which of our authentication transactions end up being frauds when our clients tell us after the fact.  While plenty of folks do send us known fraud data (thus opening up the possibility of invaluable analysis and consulting), many of our clients do not.  Therefore even if all of the aforementioned complexity were not the case, we would still be limited in our ability to provide global benchmarks such as an “average fraud rate.” Therefore, what? This is not to say that there is no such thing as a true average fraud rate, particularly at the industry level.  But you should take any claims of an authoritative average with a grain of salt.  At the very least, fraud rates are a volatile thing with a great deal of variance from one case to the next.  It is much more important to know YOUR average fraud rate, than THE average fraud rate.  You can estimate your natural fraud rate through a champion/challenger process, or even by letting the floodgates open for a few days (or however long it takes to gather a meaningful sample of known frauds), then letting the frauds bake out over time.  You can compare the strategy fraud rates and false positive ratios of two (or more) competing fraud prevention strategies.  You can track your own fraud rates and fraud trends over time. There are plenty of things you can do to create standardize metrics of fraud incidence, but good heavens for the next person to ask me what our average fraud rate is, the answer is “No.”

By: Andrew Gulledge I hate this question. There are several reasons why the concept of an “average fraud rate” is elusive at best, and meaningless or misleading at worst. Natural fraud rate versus strategy fraud rate The natural fraud rate is the number of fraudulent attempts divided by overall attempts in a given period. Many companies don’t know their natural fraud rate, simply because in order to measure it accurately, you need to let every single customer pass authentication regardless of fraud risk. And most folks aren’t willing to take that kind of fraud exposure for the sake of empirical purity. What most people do see, however, is their strategy fraud rate—that is, the fraud rate of approved customers after using some fraud prevention strategy. Obviously, if your fraud model offers any fraud detection at all, then your strategy fraud rate will be somewhat lower than your natural fraud rate. And since there are as many fraud prevention strategies as the day is long, the concept of an “average fraud rate” breaks down somewhat. How do you count frauds? You can count frauds in terms of dollar loss or raw units. A dollar-based approach might be more appropriate when estimating the ROI of your overall authentication strategy. A unit-based approach might be more appropriate when considering the impact on victimized consumers, and the subsequent impact on your brand. If using the unit-based approach, you can count frauds in terms of raw transactions or unique consumers. If one fraudster is able to get through your risk management strategy by coming through the system five times, then the consumer-based fraud rate might be more appropriate. In this example a transaction-based fraud rate would overrepresent this fraudster by a factor of five. Any fraud models based on solely transactional fraud tags would thus be biased towards the fraudsters that game the system through repeat usage. Clearly, however, different folks count frauds differently. Therefore, the concept of an “average fraud rate” breaks down further, simply based on what makes up the numerator and the denominator. Different industries. Different populations. Different uses. Our authentication tools are used by companies from various industries. Would you expect the fraud rate of a utility company to be comparable to that of a money transfer business?  What about online lending versus DDA account opening? Furthermore, different companies use different fraud prevention strategies with different risk buckets within their own portfolios. One company might put every customer at account opening through a knowledge based authentication session, while another might only bother asking the riskier customers a set of out of wallet questions. Some companies use authentication tools in the middle of the customer lifecycle, while others employ fraud detection strategies at account opening only. All of these permutations further complicate the notion of an “average fraud rate.” Different decisioning strategies Companies use an array of basic strategies governing their overall approach to fraud prevention. Some people hard decline while others refer to a manual review queue.  Some people use a behind-the-scenes fraud risk score; others use knowledge based authentication questions; plenty of people use both. Some people use decision overrides that will auto-fail a transaction when certain conditions are met. Some people use question weighting, use limits, and session timeout thresholds. Some people use all of the out of wallet questions; others use only a handful. There is a near infinite possibility of configuration settings even for the same authentication tools from the same vendors, which further muddies the waters in regards to an “average fraud rate.” My next post will beat this thing to death a bit more.

As E-Government customer demand and opportunity increases, so too will regulatory requirements and associated guidance become more standardized and uniformly adopted.  Regardless of credentialing techniques and ongoing access management, all enrollment processes must continue to be founded in accurate and, most importantly, predictive risk-based authentication. Such authentication tools must be able to evolve as new technologies and data assets become available, as compliance requirements and guidance become more defined, and as specific fraud threats align with various access channels and unique customer segments. A risk-based fraud detection system allows institutions to make customer relationship and transactional decisions based not on a handful of rules or conditions in isolation, but on a holistic view of a customer’s identity and predicted likelihood of associated identity theft.  To implement efficient and appropriate risk-based authentication procedures, the incorporation of comprehensive and broadly categorized data assets must be combined with targeted analytics and consistent decisioning policies to achieve a measurably effective balance between fraud detection and positive identity proofing results. The inherent value of a risk-based approach to authentication lies in the ability to strike such a balance not only in a current environment, but as that environment shifts as do its underlying forces. The National Institute of Standards and Technology, in special publication 800-63, defines electronic authentication (E-authentication) as “the process of establishing confidence in user identities electronically presented to an information system”. Since, as stated in publication 800-63, “individuals are enrolled and undergo an identity proofing process in which their identity is bound to an authentication secret, called a token”, it is imperative that identity proofing is founded in an approach that generates confidence in the authentication process. Experian believes that a risk-based approach that can separate valid from invalid identities using a combination of data and proven quantitative techniques is best. As “individuals are remotely authenticated to systems and applications over an open network, using a token in an authentication protocol”, enrollment processes that drive ultimate provision of tokens must be implemented with an eye towards identity risk, and not simply a series of checks against one or more third party data assets. If the “keys to the kingdom” are housed in the ongoing use of tokens provided by Credentials Service Providers (CRA) and binding credentials to that token, trusted Registration Authorities (RA) must employ highly predictive identity proofing techniques designed to segment true, low-risk identities from identities that may have been manipulated, fabricated, or in true-form are subject to fraudulent use, abuse or victimization. Many compliance-oriented authentication requirements (ex. USA PATRIOT Act, FACTA Red Flags Rule) and resultant processes hinge upon identity element (ex. name, address, Social Security number, phone number) validation and verification checks. Without minimizing the importance of performing such checks, the purpose of a more risk-based approach to authentication is to leverage other data sources and quantitative techniques to further assess the probability of fraudulent behavior.

-- by, Andrew Gulledge One of the quickest and easiest ways to reduce fraud in your portfolio is to incorporate question weighting into your out of wallet question strategy. To continue the use of knowledge based authentication without question weighting is to assign a point value of 100 points to each question. This is somewhat arbitrary (and a bit sloppy) when we know that certain questions consistently perform better than others. So if a fraudster gets 3 easier questions right, and 1 harder question wrong they will have an easier time passing your authentication process without question weighting. If, on the other hand, you adopt question weighting as part of your overall risk based authentication approach, that same fraudster would score much worse on the same KBA session. The 1 question that they got wrong would have cost them a lot of points, and the 3 easier questions they got right wouldn’t have given them as many points. Question weighting based on known fraud trends is more punitive for the fraudsters. Let’s say the easier questions were worth 50 points each, and the harder question was worth 150 points. Without question weighting, the fraudster would have scored 75% (300 out of 400 points). With question weighting, the fraudster would have scored 50% (150 out of 300 points correct). Your decisioning strategy might well have failed him with a score of 50, but passed him with a score of 75. Question weighting will often kick the fraudsters into the fail regions of your decisioning strategy, which is exactly what risk based authentication is all about. Consult with your fraud account management representative to see if you are making the most out of your KBA experience with the intelligent use of question weighting. It is a no-brainer way to improve your overall fraud prevention, even if you keep your overall pass rate the same. Question weighting is an easy way to squeeze more value of your knowledge based authentication tool.  

-- by, Andrew GulledgeThe intelligent use of question weighting in KBA should be a no-brainer for anyone using out of wallet questions. Here’s the deal: some authentication questions consistently give fraudsters a harder time than other questions. Why not capitalize on that knowledge?Question weighting is where each question type has a certain number of points associated with it. So a question that fraudsters have an easier time with might be worth only 50 points, while a question that fraudsters often struggle with might be worth 150 points. So the KBA score ends up being the total points correct divided by the total possible points. The point is to make the entire KBA session more punitive for the bad guys.Fraud analytics are absolutely essential to the use of intelligent question weighting. While fraud prevention vendors should have recommended question weights as part of their fraud best practices, if you can provide us with as many examples as possible of known fraud that went through the out of wallet questions, we can refine the best practice question weighting model to work better for your specific population.Even if we keep your pass rate the same, we can lower your fraud rate. On the other hand, we can up your pass rate while keeping the fraud rate consistent.  So whether your aim it to reduce your false positive rate (i.e., pass more of the good consumers) or to reduce your fraud rate (i.e., fail more of the fraudsters), or some combination of the two, question weighting will help you get there.

By: Margarita Lim Consumer data has increasingly become commoditized over the years. There’s a lot of it and it’s arguably more easily obtainable. Social Security number and date of birth information was once considered confidential information. Today, those data elements in addition to traditional consumer data such as name, address and phone number are more publicly available (either legitimately or illegitimately). The advent and popularity of social network Internet sites have also made considerable information about a person’s life – both professional and personal, available for anyone’s viewing pleasure. So the question is…how much is too much information? If you’re a consumer who is particular about privacy, then you’ll have a lower threshold. On the other hand, if you’re a business trying to minimize fraud losses, then you’re at the other end of the spectrum - you can never have enough information to help prevent fraud – especially when you’re trying to keep up with fraud trends. Data is a key element in fraud prevention. Experian has access to many data assets and has a reputation for providing high quality fraud products in the marketplace. The data we use in our fraud products comes from multiple sources and sets us apart from our competitors because corroborated data is more reliable than data from a single source.  Having access to multiple data sources is especially beneficial in our Knowledge Based Authentication product where the different sources provide data that is critical to generating out of wallet questions. Since companies rely on our fraud products to comply with the government’s Red Flag Rules and support Identity Theft Prevention Programs, it is extremely important that we have as much data as possible in our arsenal to thwart fraudsters’ activities and prevent consumers from being victimized by criminals. Keep in mind that these programs are only as good as the data used to confirm a person’s identity. Although information can be a double-edged sword, I don’t think one can have too much information especially when the goal is to minimize fraud.  

In my last entry I mentioned how we’re working with more and more clients that are ramping up their fraud and compliance processes to ensure Red Flag compliance. But it’s not just the FACT Act Identity Theft Program requirements that are garnering all the attention.  As every financial institution is painfully aware, numerous compliance requirements exist around the USA PATRIOT Act and Know Your Customer, Anti-Money Laundering, e-Signature and more. Legislation for banks, lenders, and other financial services organizations are only likely to increase with President Obama’s appointment of Elizabeth Warren to the new Bureau of Consumer Financial Protection. Typically FI’s must perform due diligence across more than one of these requirements, all the while balancing the competing pressures of revenue growth, customer experience, fraud referral rates, and risk management. Here’s a case where we were able to offer a solution to one client’s complex needs.  Recently, we were approached by a bank’s sales channel that needed to automate their Customer Information Program (CIP). The bank’s risk and compliance department had provided guidelines based on their interpretation of due diligence appropriate for CIP and now the Sales group had to find a tool that could facilitate these guidelines and decision appropriately. The challenge was doing so without a costly custom solution, not sacrificing their current customer service SLA’s, and being able to define the criteria in the CIP decisioning rather than a stock interpretation. The solution was to invest in a customer authentication product that offered flexible, adaptable “off the shelf” decisioning along with knowledge based authentication, aka out of wallet questions. The fact that the logic was hosted reduced costly and time consuming software and hardware implementations while at the same time allowing easy modification should their CIP criteria change or pass and review rates need to be tweaked. The net result? Consistent customer treatment and objective application of the CIP guidelines, more cross selling confidence, and the ability to refer only those applicants with fraud alerts or who did not meet the name, address, SSN, and DOB check for further authentication.

Working with clients in the financial sector means keeping an eye toward compliance and regulations like the Gramm-Leach-Bliley Act (GLB), the Fair Credit Reporting Act (FCRA) or Fair and Accurate Credit Transactions Act (FACTA). It doesn’t really matter what kind of product it is, if a client is a financial institution (FI) of some kind, one of these three pieces of legislation is probably going to apply. The good part is, these clients know it and typically have staff dedicated to these functions. In my experience, where most clients need help is in understanding which regulations apply or what might be allowed under each. The truth is, a product designed to minimize fraud, like knowledge based authentication, will function the same whether using FCRA regulated or non-FCRA regulated data. The differences will be in the fraud models used with the product, the decisioning strategies set-up, the questions asked and the data sources of those questions. Under GLB it is acceptable to use fraud analytics for detection purposes, as fraud detection is an approved GLB exception. However, under FCRA rules, fraud detection is not a recognized permissible purpose (for accessing a consumer’s data). Instead, written instructions (of the consumer) may be used as the permissible purpose, or another permissible purpose permitted under FCRA; such as legitimate business need due to risk of financial loss. Fraud best practices dictate engaging with clients, and their compliance teams, to ensure the correct product has been selected based on client fraud trends and client needs. A risk based authentication approach, using all available data and appropriately decisioning on that data, whether or not it includes out of wallet questions, provides the most efficient management of risk for clients and best experience for consumers.

Quite a scary new (although in some ways old) form of identity theft in the headlines recently. Here’s a link to the article, which talks about how children’s dormant Social Security numbers are being found and sold by companies online under the guise of CPN’s – aka credit profile numbers or credit protection numbers. Using deceased, “found”, or otherwise illicitly obtained Social Security numbers is not something new. Most identity theft prevention programs consider deceased and non-issued ranges as identity theft red flags under the FACTA Red Flag guidelines. In fact, Experian’s and any good identity verification tool is going to check against the Social Security Administration’s list of numbers listed as deceased as well as ensure the submitted number is in an SSA valid issue range – providing fraud alerts if not. A child’s valid but dormant Social Security number, however, would not flag as either. The two things I find most troubling here are: One, the sellers have found a way around the law by not calling them Social Security numbers and calling them CPN’s instead. That seems ludicrous! But, in fact, the article goes on to state that “Because the numbers exist in a legal gray area, federal investigators have not figured out a way to prosecute the people involved”. Two, because of the anonymity and the ability to quickly set up and abandon “shop”, the online marketplace is the perfect venue for both buyer and seller to connect with minimal risk of being caught. What can we as consumers and businesses take away from this? As consumers, we’re reminded to be ever vigilant about the disclosure of not only OUR Social Security number but that of our family members as well. For businesses, it’s a reminder to take advantage of additional identity verification and fraud prediction tools, such as Experian’s Precise ID, Knowledge IQ, and BizID, when making credit decisions or opening accounts rather than relying solely on consumer credit scores. Knowledge IQ’s knowledge based authentication offers out of wallet questions that may help ensure you’re dealing with the true consumer.

There are a number of people within the industry heralding the death of knowledge based authentication. To those people I would say, “In my humble opinion you are as wrong as those recent tweets proclaiming the death of Bill Cosby.” Before anyone’s head spins around, let me explain. When I talk about knowledge based authentication and out of wallet questions, I mean it in the truest sense, a la dynamic questions presented as a pop quiz and not the secret questions you answered when you set-up an account. Dynamic knowledge based authentication presents questions are generated from information known about the consumer, concerning things the true consumer would know and a fraudster wouldn’t. The key to success, and the key to good questions, is the data, which I have said many, many times before. The truth is every tool will let some fraud through; otherwise, you’re keeping too many good customers away. But if knowledge based authentication truly fails, there are two places to look: Data: There are knowledge based authentication providers who rely solely on public record data for their KBA solutions. In my opinion, that data is a higher data risk segment for compromise. Experian’s knowledge based authentication practice is disciplined and includes a mix of data. Our research has shown us that a question set should, ideally, include questions that are proprietary, non-credit, credit and innovative. Yes, it may make sense to include some public record data in a question set, but should it be the basis for the entire question set? Providers who can rely on their own data, or a strategic combination of data sources, rather than purchasing it from one of the large data aggregators are, in my opinion, at an advantage because fraudsters would need to compromise multiple sources in order to “game the system.” Actual KBA use: Knowledge based authentication works best as part of a risk management strategy where risk based authentication is a component within the framework and not the single, determining factor for passing a consumer. Our research has shown that clients who combine fraud analytics and a score with knowledge based authentication can increase authentication performance from 20% - 30% or more, depending on the portfolio and type of fraud (ID Fraud vs. First Party, etc.)… and adding a score has the obvious benefit of increasing fraud detection, but it also allows organizations to prioritize review rates efficiently while protecting the consumer experience. So before we write the obituary of KBA, let’s challenge those who tinker with out of wallet products, building lists of meaningless questions that a 5th grader could answer. Embrace optimized decisions with risk based authentication and employ fraud best practices in your use of KBA.

In “An ounce of prevention is worth a pound of cure” Kristan Frend touched on the vulnerabilities faced by members of our Armed Services. That post made me think about recent fraud trends.  Over the course of this spring and summer, I attended a few conferences and at one of these events something a bit disturbing occurred – a staff member for one of the exhibitors was victimized during the event. The individual’s wallet, containing cash and credit cards, was stolen along with the person’s passport and the victim didn’t realize it until they received their wake-up call the next morning. The few people who heard about it wondered “How could this happen at an event of industry professionals?” The answer is simple.  Even industry professionals are every-day consumers, vulnerable to attack. As part of our Knowledge Based Authentication practice, Experian engages in blind focus group interviews with “every-day consumers” facilitated by an independent consulting group on Experian’s behalf. What we learn during those sessions informs our best practices for many of the fraud products and guides our process for new question generation in Knowledge Based Authentication. It is also an eye-opening experience. Through our research we have learned that participant consumers are now more aware and accepting of Knowledge Based Authentication than in past years. Knowledge Based Authentication has become a bellwether, consumers expect it. They also expect organizations they deal with to have an Identity Theft Prevention Program – and the ability to recognize when something “just isn’t right” about a situation. However, few participants cited a comprehensive strategy to protect themselves against identity theft, and even fewer actually demonstrated a commitment to follow a strategy, even when they had one. During open and honest conversation in a relaxed setting, participants revealed their true behavior. Many admitted they still use the same password for all their accounts, write their passwords down, and keep copies of their passwords in easily accessible places, such as a purse or a wallet, a desk drawer or an online application. The bottom line is this: Most people will attempt to do what they think they should to protect themselves from identity theft, including shredding or tearing up mail offers, selectively using credit cards and/or monitoring their garbage. However, if the process is too cumbersome or if it requires that they remember too much, they will default to old habits. As Kristan pointed out, thieves may increasingly rely on computer attacks to gather data, but many still resort to low-tech methods like dumpster diving, mail tampering, and purse and wallet theft to obtain privacy sensitive information. When that purse or wallet contains not only personally identifiable information, but also account passwords, the risk levels are significantly higher. Cyber attacks are a threat, but a consumer’s own behavior may be just as risky. As for the victim in this story… a very sharp desk clerk at a neighboring hotel thought it strange that someone was checking-in for a number of days without a reservation at full rate and without luggage, which started the ball rolling and led to the perpetrator being caught and the victim getting everything back except for some cash that had been spent at a coffee merchant. Clearly, this close call didn’t turn-out as badly as it could have.

Recently, the Commerce Department reported that consumer spending levels continued to rise in February, increasing for the fifth straight month *, while flat income levels drove savings levels lower. At the same time, media outlets such as Fox Businesses, reported that the consumer “shopping cart” ** showed price increases for the fourth straight month. Somewhat in opposition to this market trend, the Q4 2009 Experian-Oliver Wyman Market Intelligence Reports reveal that the average level of credit card debt per consumer decreased overall, but showed increases in only one score band. In the Q4 reports, the score band that demonstrated balance increases was VantageScore® credit score A – the super prime consumer - whose average balance went up $30 to $1,739. In this time of economic challenge and pressure on household incomes, it’s interesting to see that the lower credit scoring consumers display the characteristics of improved credit management and deleveraging; while at the same time, consumers with credit scores in the low-risk tiers may be showing signs of increased expenses and deteriorated savings. Recent delinquency trends support that low-risk consumers are deteriorating in performance for some product vintages. Even more interestingly, Chris Low, Chief Economist at FTN Financial in New York was quoted as saying "I guess the big takeaway is that consumers are comfortably consuming again. We have positive numbers five months in a row since October, which I guess is a good sign,".  I suggest that there needs to be more analysis applied within the details of these figures to determine whether consumers really are ‘comfortable’ with their spending, or whether this is just a broad assumption that is masking the uncomfortable realities that lie within.

By: Ken Pruett I want to touch a bit on some of the third party fraud scenarios that are often top of mind with our customers: identity theft; synthetic identities; and account takeover. Identity Theft Identity theft usually occurs during the acquisition stage of the customer life cycle. Simply put, identity theft is the use of stolen identity information to fraudulently open up a new account.  These accounts do not have to be just credit card related. For example, there are instances of people using others identities to open up wireless phone and utilities accounts Recent fraud trends show this type of fraud is on the rise again after a decrease over the past several years.  A recent Experian study found that people who have better credit scores are more likely to have their identity stolen than those with very poor credit scores. It does seem logical that fraudsters would likely opt to steal an identity from someone with higher credit limits and available purchasing power.  This type of fraud gets the majority of media attention because it is the consumer who is often the victim (as opposed to a major corporation). Fraud changes over time and recent findings show that looking at data from a historical perspective is a good way to help prevent identity theft.  For example, if you see a phone number being used by multiple parties, this could be an indicator of a fraud ring in action.  Using these types of data elements can make your fraud models much more predictive and reduce your fraud referral rates. Synthetic Identities Synthetic Identities are another acquisition fraud problem.  It is similar to identity theft, but the information used is fictitious in nature.  The fraud perpetrator may be taking pieces of information from a variety of parties to create a new identity.  Trade lines may be purchased from companies who act as middle men between good consumers with good credit and perpetrators who creating new identities.   This strategy allows the fraud perpetrator to quickly create a fictitious identity that looks like a real person with an active and good credit history. Most of the trade lines will be for authorized users only.  The perpetrator opens up a variety of accounts in a short period of time using the trade lines. When creditors try to collect, they can’t find the account owners because they never existed.  As Heather Grover mentioned in her blog, this fraud has leveled off in some areas and even decreased in others, but is probably still worth keeping an eye on.  One concern on which to focus especially is that these identities are sometimes used for bust out fraud. The best approach to predicting this type of fraud is using strong fraud models that incorporate a variety of non-credit and credit variables in the model development process.  These models look beyond the basic validation and verification of identity elements (such as name, address, and social security number), by leveraging additional attributes associated with a holistic identity -- such as inconsistent use of those identity elements. Account Takeover Another type of fraud that occurs during the account management period of the customer life cycle is account takeover fraud.  This type of fraud occurs when an individual uses a variety of methods to take over an account of another individual. This may be accomplished by changing online passwords, changing an address or even adding themselves as an authorized user to a credit card. Some customers have tools in place to try to prevent this, but social networking sites are making it easier to obtain personal information for many consumers.  For example, a person may have been asked to provide the answer to a challenge question such as the name of their high school as a means to properly identify them before gaining access to a banking account.  Today, this piece of information is often readily available on social networking sites making it easier for the fraud perpetrators to defeat these types of tools. It may be more useful to use out of wallet, or knowledge-based authentication and challenge tools that dynamically generate questions based on credit or public record data to avoid this type of fraud.  

By: Wendy Greenawalt In my last few blogs, I have discussed how optimization can be leveraged to make improved decisions across an organization while considering the impact that opimizing decisions have to organizational profits, costs or other business metrics. In this entry, I would like to discuss how optimization is used to improve decisions at the point of acquisition, while minimizing costs. Determining the right account terms at inception is increasingly important due to recent regulatory legislation such as the Credit Card Act.  Doing so plays a role in assessing credit risk, relationship managment, and increasing out of wallet share. These regulations have established guidelines specific to consumer age, verification of income, teaser rates and interest rate increases. Complying with these regulations will require changes to existing processes and creation of new toolsets to ensure organizations adhere to the guidelines. These new regulations will not only increase the costs associated with obtaining new customers, but also the long term revenue and value as changes in account terms will have to be carefully considered. The cost of on-boarding and servicing individual accounts continues to escalate while internal resources remain flat. Due to this, organizations of all sizes are looking for ways to improve efficiency and decisions while minimizing costs. Optimizing decisions is an ideal solution to this problem. Optimized strategy trees (trees that optimize decisioning strategies) can be easily implemented into current processes to ensure lending decisions adhere to organizational revenue, growth or cost objectives as well as regulatory requirements.  Optimized strategy trees enable organizations to create executable strategies that provide on-going decisions based upon optimization conducted at a consumer level. Optimized strategy trees outperform manually created trees as they are created utilizing sophisticated mathematical analysis and ensure organizational objectives are adhered to. In addition, an organization can quantify the expected ROI of decisioning strategies and provide validation in strategies – before implementation. This type of data is not available without the use of a sophisticated optimization software application.  By implementing optimized strategy trees, organizations can minimize the volume of accounts that must be manually reviewed, which results in lower resource costs. In addition, account terms are determined based on organizational priorities leading to increased revenue, retention and profitability.

By: Tom Hannagan An autonomic movement describes an action or response that occurs without conscious control. This, I fear, may be occurring at many banks right now related to their risk-based pricing and profit picture for several reasons. First, the credit risk profile of existing customers is subject to continuous change over time. This was always true to some extent. But, as we’ve seen in the latest economic recession, there can be a sizeable risk level migration if enough stress is applied. It is most obvious in the case of delinquencies and defaults, but is also occurring with customers that have performing loans. The question is: how well are we keeping up with the behind-the-scenes changes risk ratings/score ranges? The changes in relative risk levels of our clients are affecting our risk-based profit picture -- and required capital allocation -- without conscious action on our part. Second, the credit risk profile of collateral categories is also subject to change over time. Again, this is not exactly new news. But, as we’ve seen in the latest real estate meltdown and dynamics affecting the valuation of financial instruments, to name two, there can be huge changes in valuation and loss ratios. And, this occurs without making one new loan.  These changes in relative loss-given-default levels are affecting our risk-based expected loss levels, risk-adjusted profit and capital allocation, in a rather autonomic manner. Third, aside from changes in risk profiles of customers and collateral types, the bank’s credit policy may change. The risk management analysis of expected credit losses is continuously (we presume) under examination and refinement by internal credit risk staff. It is certainly getting unprecedented attention by external regulators and auditors. These policy changes need to be reflected in the foundation logic of risk-based pricing and profit models. And that’s just in the world of credit risk. Fourth, there can also be changes in our operating cost structure, including mitigated operational risks, and product volumes that affect the allocation of risk-based non-interest expense to product groups and eventually to clients. Although it isn’t the fault of our clients that our cost structure is changing, for better or worse, we nonetheless expect them to bear the burden of these expenses based on the services we provide to them. Such changes need to be updated in the risk-based profit calculations. Finally, there is the market risk piece of risk management.  It is possible if not likely that our ALCO policies have changed due to lessons from the liquidity crisis of 2008 or the other macro economic events of the last two years. Deposit funds may be more highly valued, for instance. There may also be some rotation in assets from lending. Or, the level of reliance on equity capital may have materially changed. In any event, we are experiencing historically low levels for the price of risk-free (treasury rate curve) funding, which affects the required spread and return on all other securities, including our fully-at-risk equity capital. These changes are occurring apart from customer transactions, but definitely affect the risk-based profit picture of each existing loan or deposit account and, therefore, every customer relationship. If any, let alone all, of the above changes are not reflected in our risk-based performance analysis and reporting, and any pricing of new or renewed services to our customers, then I believe we are involved in autonomic changes in risk-based profitability.