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By: Ken Pruett The majority of the customers I meet with use some sort of Velocity Checks to assist with their Fraud and Compliance process. However, there are still quite a few that do not, especially when opening up New Business Accounts. Historical data checks have proven to be an effective form of identity theft prevention for both Consumer fraud and Commercial Fraud. We see scenarios where a perpetrator will have one successful penetration of a business and opens up a fraudulent account. They then try and replicate this against the same business. All of the information may be different, with the exception of one element, often the phone number. Without velocity checks, this may not be identified at the time the account is being opened. More sophisticated rings try to be more creative in their fraudulent attempts. They may gain access to a consumers information and then go and apply at a variety of entities. They are more careful, so they never attempt to target the same business twice. They are aware that many companies have velocity checks, so they do not want to take a chance of having their information questioned. At a minimum, the use of in-house velocity checks should be a standard process for you fraud detection measures. Typical data elements to check against are; name (business or consumer), address, phone number, and Social Security Number. A fraud best practice would be to use a tool that provides velocity checks and incorporates the information into a fraud prevention tool. There are tools that provide checks across multiple businesses and this typically provides the best level of protection. By looking at inquiry information across multiple businesses, you are able to help prevent being a victim of some of the more sophisticated rings. Don’t find yourself being the easiest target. Once you get hit, it could snowball and you may be victimized multiple times. We all know there is no way to stop all of the fraud, but let’s not make it too easy on the perpetrators. Try and find a way to use some sort of velocity checks in your process to at least minimize your fraud risk.

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.


