Tag: synthetic ID

Part four in our series on Insights from Vision 2016 fraud and identity track It was a true honor to present alongside Experian fraud consultant Chris Danese and Barbara Simcox of Turnkey Risk Solutions in the synthetic and first-party fraud session at Vision 2016. Chris and Barbara, two individuals who have been fighting fraud for more than 25 years, kicked off the session with their definition of first-party versus third-party fraud trends and shared an actual case study of a first-party fraud scheme. The combination of the qualitative case study overlaid with quantitative data mining and link analysis debunked many myths surrounding the identification of first-party fraud and emphasized best practices for confidently differentiating first-party, first-pay-default and synthetic fraud schemes. Following these two passionate fraud fighters was a bit intimidating, but I was excited to discuss the different attributes included in first-party fraud models and how they can be impacted by the types of data going into the specific model. There were two big “takeaways” from this session for me and many others in the room. First, it is essential to use the correct analytical tools to find and manage true first-party fraud risk successfully. Using a credit score to identify true fraud risk categorically underperforms. BustOut ScoreSM or other fraud risk scores have a much higher ability to assess true fraud risk. Second is the need to for a uniform first-party fraud bust-out definition so information can be better shared. By the end of the session, I was struck by how much diversity there is among institutions and their approach to combating fraud. From capturing losses to working cases, the approaches were as unique as the individuals in attendance This session was both educational and inspirational. I am optimistic about the future and look forward to seeing how our clients continue to fight first-party fraud.

This article first appeared in Baseline Magazine Since it is possible for cyber-criminals to create a synthetic person, businesses must be able to differentiate between synthetic and true-party identities. Children often make up imaginary friends and have a way of making them come to life. They may come over to play, go on vacation with you and have sleepover parties. As a parent, you know they don’t really exist, but you play along anyway. Think of synthetic identities like imaginary friends. Unfortunately, some criminals create imaginary identities for nefarious reasons, so the innocence associated with imaginary friends is quickly lost. Fraudsters combine and manipulate real consumer data with fictitious demographic information to create a “new” or “synthetic” individual. Once the synthetic person is “born,” fraudsters create a financial life and social history that mirrors true-party behaviors. The similarities in financial activities make it difficult to detect good from bad and real from synthetic. There really is no difference in the world of automated transaction processing between you and a synthetic identity. Often the synthetic “person” is viewed as a thin or shallow file consumer— perhaps a millennial. I have a hard time remembering all of my own passwords, so how do organized “synthetic schemes” keep all the information usable and together across hundreds of accounts? Our data scientists have found that information is often shared from identity to identity and account to account. For instance, perhaps synthetic criminals are using the same or similar passwords or email addresses across products and accounts in your portfolio. Or, perhaps physical address and phone records have cross-functional similarities. The algorithms and sciences are much more complex, but this simplifies how we are able to link data, analytics, strategies and scores. Identifying the Business Impact of Synthetic-Identity Fraud Most industry professionals look at synthetic-identity fraud as a relatively new fraud threat. The real risk runs much deeper in an organization than just operational expense and fraud loss dollars. Does your fraud strategy include looking at all types of risk, compliance reporting, and how processes affect the customer experience? To identify the overall impact synthetic identities can have on your institution, you should start asking: Are you truly complying with "Know Your Customer" (KYC) regulations when a synthetic account exists in your active portfolio? Does your written "Customer Identification Program" (CIP) include or exclude synthetic identities? Should you be reporting this suspicious activity to the compliance officer (or department) and submitting a suspicious activity report (SAR)? Should you charge off synthetic accounts as credit or fraud losses? Which department should be the owner of suspected synthetic accounts: Credit Risk, Collections or Fraud? Do you have run any anti-money laundering (AML) risk when participating in money movements and transfers? Depending on your answers to the above questions, you may be incurring potential risks in the policies and procedures of synthetic identity treatment, operational readiness and training practices. Since it is possible to create a synthetic person, businesses must be able to differentiate between synthetic and true-party identities, just as parents need to differentiate between their child's real and imaginary friends.

Customer Experience during the holiday shopping season During the holidays, consumers transact at a much greater rate than any other time of the year. Many risk-management departments respond by loosening the reins on their decision engines to improve the customer experience — and to ensure that this spike does not trigger a response that would impede a holiday shopper’s desire to grab one more stocking stuffer or a gift for a last-minute guest. As a result, it also is the busy season for fraudsters, and they use this act of goodwill toward your customers to improve their criminal enterprise. Ultimately, you are tasked with providing a great customer experience to your real customers while eliminating any synthetic ones. Recent data breaches resulted in large quantities of personally identifiable information that thieves can use to create synthetic identities being published on the Dark Web. As this data is related to real consumers, it can be difficult for your identity-authentication solution to determine that these identities have been compromised or fabricated, enabling fraudsters to open accounts with your organization. Experian’s Identity Element Network™ can help you determine when synthetic identities are at work within your business. It evaluates nearly 300 data-element combinations to determine if certain elements appear in cyberspace frequently or are being used in combination with data not consistent with your customer’s identity. This proven resource helps you manage fraud across the Customer Life Cycle and hinder the damage that identity thieves cause. Identity Element Network examines a vast attribute repository that grows by more than 2 million transactions each day, revealing up-to-date fraud threats associated with inconsistent or high-risk use of personal identity elements. Our goal is to provide the comfort of knowing that you are transacting with your real customers. Don’t get left in the cold this holiday season — fraudsters are looking for opportunities to take advantage of you and your customers. Contact your Experian account executive to learn how Identity Element Network can help make sure you are not letting fraudsters exploit the customer experience intended for your real customers. Learn more about the delicate balance between customer and criminal by viewing our fraud e-book.

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: Heather Grover In my previous blog, I covered top of mind issues that our clients are challenged with related to their risk based authentication efforts and fraud account management. My goal in this blog is to share many of the specific fraud trends we have seen in recent months, as well as those that you – our clients and the industry as a whole – are experiencing.  Management of risk and strategies to minimize fraud is on your mind. 1. Migration of fraud from Internet to call centers - and back again. Channel specific fraud is nothing new. Criminals prefer non-face-to-face channels because they can preserve anonymity, while increasing their number of attempts. The Internet has been long considered a risky channel, because many organizations have built defenses around transaction velocity checks, IP address matching and other tools. Once fraudsters were unable to pass through this channel, the call center became the new target, and path of least resistance. Not surprisingly, once the industry began to address the call center, fraud began to migrate, yet again. Increasingly we hear that the interception and compromise of online credentials due to keystroke loggers and other malware is on the rise. 2. Small business fraud on the rise. As the industry has built defenses in their consumer business, fraudsters have again migrated -- this time to commercial products. Historically, small business has not been a target for fraud, which is changing. We see and hear that, while similar to consumer fraud in many ways, small business fraud is often more difficult to detect many times due to “shell businesses” that are established. 3. Synthetic ID becoming less of an issue.  As lenders tighten their criteria, not only are they turning down those less likely to pay, but their higher standards are likely affecting Synthetic ID fraud, which many times creates identities with similar characteristics that mirror “thin file” consumers. 4. Family fraud continues. We have seen consumers using the identities of members of their family in an attempt to gain and draw down credit. These occurrences are nothing new, but   sadly this continues in the current economic environment. Desperate parents use their children’s identities to apply for new credit, or other family may use an elderly person’s dormant accounts with a goal of finding a short term lifeline in a bad credit situation. 5. Fraud increasing from specific geographic regions. Some areas are notorious for perpetrating fraud – not too long ago it was Nigeria and Russia. We have seen and are hearing that the new hot spots are Vietnam and other Eastern Europe countries that neighbor Russia. 6. Falsely claiming fraud. There has been an increase of consumers who claim fraud to avoid an account going into delinquency. Given the poor state of many consumers credit status, this pattern is not unexpected. The challenge many clients face is the limited ability to detect this occurrence. As a result, many clients are seeing an increase in fraud rates. This misclassification is masking what should be bad debt.