Tag: bust out fraud

Dormant fraud is an especially insidious form of account takeover fraud that often goes undetected until it’s too late. Learn how to protect your organization.

First-party fraud can be detected and prevented by using robust fraud risk management strategies and solutions.

Preventing fraud losses requires an understanding of the individual fraud types, how they differ from one another, and how this impacts potential solutions.

I’d like to explore a hybrid type – synthetic identity fraud – and how it can be the harder to detect than third- or first-party fraud.

In Experian’s recent perspective paper, Preventing synthetic identity fraud, we explore how SID differs from other types of fraud and how to prevent it.

Credit ghosting refers to the theft of a deceased person’s identity. According to the IRS, 2.5 million deceased identities are stolen each year.

Synthetic ID fraud is the fastest-growing type of financial crime in the US. The best offense means detecting synthetic identities before they're in place.

Experian is recognized as a leading security solution provider for fraud and identity solutions in order to protect customers and financial institutions

By: Kennis Wong On the surface, it’s not difficult to define existing account fraud. Obviously, it is fraud perpetrated against an existing account. But the way I see it, existing account fraud can be broken down into four types. The first type is account takeover fraud, which is what most organizations think as the de facto existing account fraud. This is when a real consumer using his or her own identity to open a legitimate account, but the account later on get taken over by an identity fraudster. The idea is that when the account was first established, it was created by the rightful person. But somewhere along the way, the account and identity information were compromised.  The fraudster uses the compromised information to engineer their way into the account. The second type is impersonation. Impersonation is somewhat similar to account takeover in the sense that it is also misusing the victim’s account. But the difference is that impersonation is more of a one or few times misuses of the account. Examples are a fraudulent use of a credit card or wire transfer. These are the obvious categories. But I think we should also think about these other categories. My definition of existing account fraud also includes this third type – identity fraud that was undetected during application. In other words, an account is established based on stolen identity.  Many organizations call this “new account fraud”, which I don’t have a problem with. But I think it’s really also existing account fraud, because –  is this existing account? The answer is yes. Is this fraud? Absolutely. It’s not that difficult, is it? Similarly, I am including first-party fraud in existing account fraud as well. A consumer can use his or her own identity to open an account, with an intention to default after the account is established. Example is bust out fraud. You see that this is an expanded definition of existing account fraud, because my focus is on detection. No matter at what point and how identity fraud comes in, it becomes an account in your organization, and that is where we need to discover the fraud. But at the end of the day, it’s not too important how to categorize or name the fraud - whether it's application fraud, existing account fraud, first party fraud or third party fraud, as long as organizations understand them enough and have a good way to detect them. Read more blog posts on existing account fraud.

At Experian’s recent client conference, Vision 2011, there was a refreshing amount of positive discussion and outlook on origination rates and acquisition strategies for growth. This was coming not only from industry analysts participating in the conference but from clients as well. As a consumer, I’d sensed the ‘cautious optimism’ that we keep hearing about because my mailbox(the ‘original’ one, not email) has slowly been getting more and more credit card offer letters over the last 6 months.   Does this mean a return to prospecting and ultimately growth for financial institutions and lenders? It’s a glimmer of hope, for sure, although most agree that we’re a long way from being out of the woods, particularly with unemployment rates still high and the housing market in dire shape. Soooo…..you may be wondering where I’m going with this…. Since my job is to support banks, lenders, utilities and numerous other businesses’ in their fraud prevention and compliance efforts, where my mind goes is: how does a return to growth – even slight – impact fraud trends and our clients’ risk management policies? While many factors remain to be seen, here are a few early observations: ·         Account takeover, bust out fraud, and other types of existing account fraud had been on the rise while application fraud had declined or stayed the same (relative to the decrease in new originations); with prospecting and acquisition activity starting to increase, we will likely see a resurgence in new account fraud attempts and methods. ·         Financial institutions and consumers are under increasing risk of malware attacks; with more sophisticated malware technology popping up every day, this will likely be a prime means for fraudsters to commit identity theft and exploit potentially easier new account opening policies. ·         With fraud loss numbers flat or down, the contracted fraud budgets and delayed technology investments by companies over the last few years are a point of vulnerability, especially if the acquisition growth rate jumps substantially.  

By: Kennis Wong  Data is the very core of fraud detection. We are constantly seeking new and mining existing data sources that give us more insights into consumers’ fraud and identity theft risk. Here is a way to categorize the various data sources. Account level - When organizations detect fraud, naturally they leverage the data in-house. This type of data is usually from the individual account activities such as transactions, payments, locations or types of purchases, etc. For example, if there’s a purchase $5000 at a dry cleaner, the transaction itself is suspicious enough to raise a red flag. Customer level - Most of the times we want to see a bigger picture than only at the account level. If the customer also has other accounts with the organization, we want to see the status of those accounts as well. It’s not only important from a fraud detection perspective, but it’s also important from a customer relationship management perspective. Consumer level - As Experian Decision Analytics’ clients can attest, sometimes it’s not sufficient to look only at the data within an organization but also to look at all the financial relationships of the consumer. For example, in the situation of bust out fraud or first-party fraud, if you only look at the individual account, it wouldn’t be clear whether a consumer has truly committed the fraud. But when you look at the behavior of all the financial relationships, then the picture becomes clear. Identity level - Fraud detection can go into the identity level. What I mean is that we can tie a consumer’s individual identity elements with those of other consumers to discover hidden inconsistencies and relationships. For example, we can observe the use of the same SSN across different applications and see if the phones or addresses are the same. In the account management environment, when detecting existing account fraud or account takeover, this level of linkage is very useful as more data becomes available after the account is open. Loading...

Experian recently contributed to a TSYS whitepaper focused on the various threats associated with first party fraud. I think the paper does a good job at summarizing the problem, and points out some very important strategies that can be employed to help both prevent first party fraud losses and detect those already in an institution’s active and collections account populations. I’d urge you to have a look at this paper as you begin asking the right questions within your own organization. Watch here The bad news is that first party fraud may currently account for up to 20 percent of credit charge-offs. The good news is that scoring models (using a combination of credit attributes and identity element analysis) targeted at various first party fraud schemes such as Bust Out, Never Pay, and even Synthetic Identity are quite effective in all phases of the customer lifecycle. Appropriate implementation of these models, usually involving coordinated decisioning strategies across both fraud and credit policies, can stem many losses either at account acquisition, or at least early enough in an account management stage, to substantially reduce average fraud balances. The key is to prevent these accounts from ending up in collections queues where they’ll never have any chance of actually being collected upon. A traditional customer information program and identity theft prevention program (associated, for example with the Red Flags Rule) will often fail to identify first party fraud, as these are founded in identity element verification and validation, checks that often ‘pass’ when applied to first party fraudsters.

By: Ken Pruett I find it interesting that the media still focuses all of their attention on identity theft when it comes to credit-related fraud.  Don’t get me wrong.  This is still a serious problem and is certainly not going away any time soon.  But, there are other types of financial fraud that are costing all of us money, indirectly, in the long run.  I thought it would be worth mentioning some of these today. Although third party fraud, (which involves someone victimizing a consumer), gets most of the attention, first party fraud (perpetrated by the actual consumer) can be even more costly.  “Never pay” and “bust out” are two fraud scenarios that seem to be on the rise and warrant attention when developing a fraud prevention program. Never Pay A growing fraud problem that occurs during the acquisition stage of the customer life cycle is “never pay”.  This is also classified as first payment default fraud.  Another term we often hear to describe this type of perpetrator is “straight roller”. This type of fraudster is best described as someone who signs up for a product or service -- and never makes a payment. This fraud problem occurs when a consumer makes an application for a loan or credit card. The consumer provides true identification information but changes one or two elements (such as the address or social security number).  He does this so that he can claim later that he did not apply for the credit.  When he’s granted credit, he often makes purchases close to the limit provided on the account.  (Why get the 32 inch flat screen TV when the 60 inch is on the next store shelf -- when you know you are not going to pay for it anyway?) These fraudsters never make any payments at all on these accounts. The accounts usually end up in collections. Because standard credit risk scores look at long term credit, they often are not effective in predicting this type of fraud.  The best approach is to use a fraud model specifically targeted for this issue. Bust Out Fraud Of all the fraud scenarios, bust out fraud is one of the most talked about topics when we meet with credit card companies.  This type of fraud occurs during the account management phase of the customer lifecycle.  It is characterized by a person obtaining credit, typically a loan or credit card, and maintaining a good credit history with the account holder for a reasonable period of time.  Just prior to the bust out point, the fraudster will pay off the majority of the balance, often by using a bad check.  She will then run the card up close to the limit again -- and then disappear. Losses for this type of fraud are higher than average credit card losses.  Losses between 150 to 200 percent of the credit limit are typical.  We’ve seen this pattern at numerous credit card institutions across many of their accounts. This is a very difficult type of fraud to prevent. At the time of application, the customer typically looks good from a credit and fraud standpoint.  Many companies have some account management tools in place to help prevent this type of fraud, but their systems only have a view into the one account tied to the customer.  A best practice for preventing this type of fraud is to use tools that look at all the accounts tied to the consumer -- along with other metrics such as recent inquiries.  When taking all of these factors into consideration, one can better predict this growing fraud type.