As the pandemic rages on, crime and money laundering risks continue to evolve at a rapid pace in the commercial lending space. However, firms can mitigate these risks by applying advanced technologies and using a data-driven approach.
In a new blog post, Quantexa head of fraud solutions banking and capital markets David Manley highlighted the key pain point of the increase in fraud perpetrators. When large amounts of money are involved, nefarious actors are waiting in the wings, hoping to seize upon these opportunities. Commercial lending has now been moved forward into the spotlight as one of the convergence points of credit, reputational, and financial crime risk.
The LexisNexis Risk Solutions 2019 lending fraud survey uncovered increasing fraud levels across all sizes of financial services during a two-year period, with small and mid-sized financial institutions experiencing higher levels of fraud. It found that estimated monetary losses of overall revenue for smaller banks and credit unions amount to 4.5% and 5.8% for digital lenders compared to 2.9% for larger institutions. Higher rates of lending fraud experienced by small banks, credit unions, and digital lenders who focus on streamlining loan processing are the result of lower technology investments and greater reliance on human assessment of risk, it said.
Indeed, one of the key reasons behind the escalation of lending fraud is that despite most institutions having anti-fraud divisions, key resources are often fragmented across the organisation. Essential data and expertise are typically distributed across teams, with little to no coordination.
Banks and government agencies are now recognising the value of sharing data to help indicate fraud risk. In the US, the Social Security Administration in 2020 began allowing banks to have access to Social Security numbers and limited personal identity information in order to score customer applications for fraud risk via its eCBSV system.
However, this approach has a few disadvantages, such as the fact that telecommunications companies are not currently allowed access. Manley pointed out that fraudsters can often exploit this by establishing telecom accounts, gaining credit on those accounts, and using them to build the appearance of legitimacy for the synthetic identities they create. Consequently, financial institutions need to use more advanced methods to score each new online application for fraud risk, not just credit risk.
Detailing on some of the ways fraudsters scam companies, Manley said criminals apply for new bank accounts with attractive pre-approved benefits including overdrafts, loan facilities and/or credit cards, which allow funds to be withdrawn quickly. But have no intention of repayment. In most cases, the fraudsters apply using synthetic identities which involves using a mix of legitimate and fraudulent information; some representing stolen personal data from real consumers, and some fabricated.
Other methods are ‘bust out fraud’ cases where the victims in these cases are typically other businesses and financial institutions. In these instances, Manley wrote, criminals set up or purchase existing shell companies. They then mimic the activities of a legitimate business by creating financial transactions with fake invoices and payments, creating the illusion of payroll activity, and establishing accounts with legitimate businesses. After establishing multiple accounts and credit lines and building trust, the criminals maximize their withdrawals and ‘bust out’ – then disappear.
The investigation in February by Europol, for instance, reported the arrest of 105 individuals in multiple countries, who used a bust-out fraud scheme to steal an estimated €12m from banks in the US.
It’s indeed no secret that preventing lending fraud can be a challenge. Conversely, regulatory expectations for financial institutions have become more complex and simultaneously more ambiguous as new guidance is issued for emerging risks.
Manley said that deploying advanced analytics technology and a data-driven approach is the silver bullet to combat all these types of fraud. By leveraging alternative data for lending, FinTechs can improve their ability to immediately pinpoint loan applicants faking their identities by flagging non-existent or fictitious businesses. “[Quantexa’s] Network graphing and link analysis can indicate links to other entities that indicate fraud risk, while also revealing patterns of behaviour and anomalies that indicate fraud risk,” he wrote.
Furthermore, a wide range of internal and publicly available data can be used, ranging from social media data to physical addresses to email accounts, IP addresses, phone numbers and more. It can also incorporate information from multiple regions, sources and past vendor relationships to assess the validity of the applicant’s identity.
Manley added, “When managed well, analytics can significantly reduce fraud losses from both first-party lending fraud, mortgage fraud, and business lending fraud such as bust out fraud, as well as other types of fraud. And it can do this without hampering a banks’ ability to process new customer applications in a timely manner and to deliver a successful customer experience for consumers.”
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