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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. To learn more, visit the Experian website at: https://www.experian.com/decision-analytics/fraud-detection.html?cat1=fraud-management&cat2=detect-and-reduce-fraud). Trust me, Knowledge Based Authentication with IVR is only the beginning. However, the rest will have to wait; right now my high-tech, automated refrigerator is calling to tell me I'm out of butter.

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.


