Credit fraud, the bedrock of every scam, is constructed on a foundation of lies and misrepresentation. It's the financial equivalent of a masquerade, where someone dons a false identity at some stage in the lending process. The consequences of this deception are costly, both financially and socially, which explains why the fight against this malicious activity is more crucial than ever.
Common Types of Credit Fraud
Let's explore the common types of scams that are frequently encountered:
1. Personal Loan Fraud: Here, the loan applicant deliberately falsifies or exaggerates their financial status to secure a loan they wouldn't normally qualify for.
2. Third-Party Fraud: This type of fraud transpires when a person willingly provides their personal information to another individual to commit fraud. The accomplice could be a family member or a friend. In some cases, the victim whose data is being misused might not even be aware of their involvement in the fraudulent borrowing scheme.
3. Identity Theft: Also known as third-party credit fraud, this happens when an individual uses a fake or stolen identity to secure a loan, with no intention of repayment.
4. Loan Stacking: This is a situation where a single borrower applies for multiple loans within a short period of time, without any intention of repaying them.
Despite industry insiders warning that basic identity verification is no longer sufficient to prevent these types of frauds, many institutions are still reliant on these outdated methods.
As identified in Javelin's Study on Digital Lending Fraud: "Relying on simple verification of basic elements of personal data to simultaneously comply with the requirements of the Customer Identification Program and fraud risk management is no longer enough to counter fraudsters."
A New Era of Fraud Prevention: Digital Footprint Analysis
(Based on Mikhaylo Pavlyuk, the CCO of 3DiVi Inc. story).
"I want to share a case where I, alongside a team of bank specialists, implemented a new identity verification system to combat credit fraud. We managed to detect the "digital footprint" of scammers, effectively halting the flow of fraudulent loans.
Earlier this year, a small bank approached my company for a demo of our facial recognition platform. They had been noticing an increasing number of loan defaults at their retail outlets. Consequently, they were considering a biometric identification system to combat the potential threats of fraud.
The bank's first challenge was quantifying the extent of the problem. Without an accurate estimation of losses, making the decision to invest in a new project proved difficult.
Our proposed solution involved analyzing their existing database for duplicate photos, photos with missing faces, photos with celebrities, and a few other 'secret sauce' variables. This was possible as alongside loan application forms, photos of borrowers were provided.
The findings astounded the bank's management. They had no idea they had been granting loans to the likes of Angelina Jolie, Brad Pitt, and even world leaders! Although amusing, the real concern was the significant number of people we found who had the same face but different documents in their applications. This triggered an investigation by the bank's security service, which subsequently led to an organized criminal group being apprehended by law enforcement.
This analysis took just over a week and gave the bank an accurate estimation of its losses, allowing it to implement our biometric identification system with more knowledge and confidence."
To summarize, if you possess a photo database but have yet to implement biometric identification processes, it might be time to consider doing so. We are ready to provide an initial analysis swiftly and without charge. You may be surprised to discover that the likes of Elon Musk is seeking a loan through your smartphone!
P.S.: Special thanks to Mikhaylo Pavlyuk, the CCO of 3DiVi Inc., for assistance in preparing the information for this article.
