Market fluctuations, shifting consumer behavior and competition from digital entrants continue to challenge lenders. At the same time, the crowded marketplace means consumers have more options and thus, more control of their lending decisions, increasing their expectation for seamless Amazon-like experiences. As they address the growing pressure to accelerate and streamline origination, outperform the competition and maintain compliance, lenders also can’t forget about taking steps to minimize risk, especially as fraud surges.
Among the types of fraud targeting lenders, the frequency and high cost of Synthetic Identity Fraud (SIF) is a growing concern. IDology’s Eighth Annual Fraud Report noted that 43% of fraud executives across multiple industries reported increases in synthetic fraud in 2020, up from 40% in 2019. Additional research shows that only 22% of Americans claim to know what SIF is.
While widespread across industries, SIF is particularly problematic for financial institutions which experience $50-$250MM in losses each year due to this elusive form of fraud. Thirty-five percent of bankers cite SIF as one of the most prevalent fraud types.
Synthetic identities are made up of real and fake personally identifiable information (PII). For example, the home address associated with the identity will exist on a map, but the corresponding SSN is unrelated to the person living there. Because of this, synthetic accounts appear legitimate and often go unnoticed until it’s too late.
Although fabricating identities out of thin air is not a new tactic, research shows that SIF continues to be difficult to detect. Seventy-two percent of financial institutions report that synthetic identities are more challenging to identify and address than identity theft.
How Synthetic Identities are Made
Unfortunately, those who often suffer the most from SIF are vulnerable populations, including young, elderly, and deceased individuals. In addition to being vulnerable to SIF, data from IDology shows that all three groups have seen a progressive increase in SIF rates.
Fraudsters often use the social security numbers of young people to build synthetic identities because they haven’t begun transacting yet. Additionally, most parents are not actively monitoring their children’s credit reports for fraud. Experiencing identity theft at a young age can result in an inability to receive financial aid for college, buy their first car, or rent an apartment. The graph below illustrates how threats to the identities of young people have increased year over year.
IDology transaction data also shows an increase in the number of elderly identity alerts over time. Fraudsters are drawn to the elderly for a variety of reasons. Limitations in mobility or cognition may make them more vulnerable to manipulation. As the chart below shows, identity thieves know older people often have greater wealth and the potential to deliver an increasingly enticing payoff.
Synthetic identities from deceased individuals are also on the rise. According to Consumer Affair’s 2021 identity theft statistics, these synthetic identities often remain under the radar, with family members not noticing the theft until months or years have passed. Consumer Affairs estimates that more than 2.5 million identities are stolen from deceased individuals each year. Given the trajectory of this type of fraud is also increasing, banks have even more reason to stay focused on the threat of SIF. Consumer Affairs estimates that more than 2.5 million identities are stolen from deceased individuals each year, giving banks even more of a reason to stay focused on the threat of SIF.
How Can Financial Institutions Gain the Upper Hand?
While there are no silver bullets, the following three elements are critical for maintaining compliance and detecting and deterring SIF without adding unnecessary friction to the customer experience:
#1 Multiple, diverse layers of data. Leading businesses analyze several different data types and sources across multiple integrated layers of identity intelligence to identify signs of SIF. Because criminals have easy access to a treasure trove of compromised credit data from breaches, static data sources are not enough to spot SIF.
Diversifying the attributes collected beyond static KYC /CIP matching to include alerts on newer identity records found, location intel such as geolocation, email and address deliverability, and mobile service and device elements is crucial for a deeper view of a potential synthetic identity.
#2 Cross-industry fraud intelligence. Effectively fighting fraud is a group effort. Consortium networks provide visibility into fraud trends, data and loss across industries and channels for insight that can help pinpoint potential synthetic identities. Of fraud executives recently surveyed by Aite Group, 82% believe data exchanges and consortia are effective in combating SIF.
#3 Machine learning backed by human fraud expertise. By applying supervised machine learning (ML) to the identity verification process, lenders have the power to analyze massive amounts of digital transaction data and recognize patterns that can help spot SIF. Forty-four percent of respondents to IDology’s 8th Annual Fraud Report cite AI as a best practice for fighting SIF.
While machines are great at detecting trends that have already been identified as suspicious, they’re unable to detect novel forms of fraud. When ML is coupled with human expertise, lenders get the best of both worlds and can enhance anti-fraud protocols while creating new, more usable data sets that optimize identity verification efforts moving forward.
As lenders take steps to protect against SIF, it’s important to note that instituting too many fraud controls can backfire by adding unnecessary friction to the customer experience. With no shortage of options to choose from, a cumbersome and difficult onboarding process can lead to abandonment. An identity verification solution that analyzes multiple diverse data sources, provides visibility into cross-industry fraud intelligence and offers the powerful combination of human fraud expertise and ML can make all the difference. To learn more, download our whitepaper, Leveling the Playing Field: Using Tech to Balance Fraud Prevention with Frictionless Digital Experiences.