A recent report shows how deepfakes can easily trick automated liveness checks used by banks and other businesses to help verify users’ identities. The top 10 KYC suppliers’ identity checks were examined for potential vulnerabilities by the security company Sensity, which specializes in recognizing attacks utilizing artificial intelligence-generated faces. To pass vendors’ liveness testing, Sensity employed deepfakes to replicate a target face onto an ID card that would be scanned and then copy that same face into a video stream of a potential attacker. Francesco Cavalli, chief operating officer at Sensity, noted that nine of the top ten solutions were extremely susceptible to deepfake attacks.
This AI-driven technology has become a major threat in terms of circulating false information and escalating identity fraud. The situation worsened when COVID-19 was implemented since it made it possible for criminals to exploit businesses’ and consumers’ blind spots and increase fraud and identity theft. Deepfake fraud increased as a result of the pandemic, and it now presents serious difficulties for financial institutions and fintech firms that must precisely authenticate and verify identities.
Businesses must automate the identity verification process in order to better detect and battle fraud as fraudsters continue to employ tools like deepfakes to trick identity verification systems and obtain unauthorized access to digital assets and online accounts.
What exactly is a deepfake?
The terms “deep learning” and “fake” are the roots of the word “deepfake,” which refers to a particular branch of artificial intelligence. They are, in essence, deep learning-fabricated fake videos. Deep learning, which is regarded as a subset of artificial intelligence, refers to combinations of algorithms that possess the capacity to learn and make intelligent judgments on their own.
Essentially, a computer software looks for facial similarities between faces and connects them to real people. The trick takes advantage of how easily people are led to trust what they initially see. Most human eyes won’t ever detect the almost seamless video.
When fraud detection is defeated by deepfake technology
Financial crime, including fraud, has grown over time, but the growth of deepfake fraud in particular poses serious risks and creates a range of security issues for everyone. Deepfakes are used by fraudsters for a variety of things, including impersonating public figures and job candidates. Although theoretically possible for some time, deepfakes have just recently attracted significant media interest.
The accessibility of deepfake software has made the contentious technology considerably more popular. Everyone has simple access to phone apps and computer software that can create fake information, from common consumers with no technical skills to state-sponsored actors. The technology is a particularly pernicious fraud vector since it is getting harder for both people and fraud detection tools to discern between real video or audio and deepfakes.
The deepfakes’ rising fraud risks
Deepfake technology is used by fraudsters to repeatedly commit identity theft and fraud, causing havoc in a variety of businesses. Numerous sectors are susceptible to deepfakes, but those that deal with significant volumes of personally identifiable information (PII) and client assets are particularly at risk.
Financial institutions and fintech businesses are vulnerable to a variety of identity frauds since they handle consumer data when onboarding new clients and opening new accounts. Deepfakes are a tool that fraudsters can use to attack these companies, resulting in identity theft, bogus claims, and new account fraud. Successful fraud attempts may be used to create a large number of false identities, enabling crooks to launder money or take over financial accounts. Deepfakes have the potential to seriously harm businesses through monetary loss, harm to their reputations, and poorer customer experiences.
Loss of money: Early in the year 2020, a bank manager in Hong Kong got a call claiming to be from a client asking for permission to transfer funds for an impending transaction. Bad actors defrauded the bank of $35 million using speech-generated AI software that mimicked the client’s voice. Once transferred, the money couldn’t be traced.
Management of reputation: Deepfake misinformation damages a company’s reputation in ways that are difficult to fix. Successful fraud attempts that cause monetary loss may have a detrimental effect on customers’ trust in and perception of a business generally, making it challenging for businesses to defend their position.
Effect on the customer experience: Businesses were pushed by the pandemic to stop sophisticated fraud efforts while maintaining positive customer experiences. Customers will have unpleasant experiences at almost every stage of the customer journey if companies fail to rise to the challenge and become fraud-ridden. To identify and protect against deepfake fraud efforts from the start, businesses must add new layers of defense to their onboarding procedures.
How businesses can fight deep-pocketed fraud
While businesses may effectively protect themselves from deepfake fraud and lessen the impact of future identity-based attacks, current techniques of fraud detection cannot verify 100% of true identities online. In order to identify third-party fraud, fake identities, and impersonation efforts, financial institutions and fintech businesses must exercise extra caution while onboarding new customers. Businesses can correctly identify deepfakes and stop more fraud with the right technology.
Businesses must confirm identification with deep multi-dimensional liveness tests, which estimate liveness by evaluating the quality of selfies and estimating depth cues for face authentication, in addition to checking PII during the onboarding process. In many instances, fraudsters may try to pass themselves off as individuals by using real PII in conjunction with a headshot that doesn’t reflect the person’s true identity. The usage of manual processes and faulty traditional identity verification increases the attack surface available to malicious actors. In identity verification, deepfake technology can easily get beyond flat photos and even liveness checks. In actuality, only 65% of the deepfakes that were assessed were picked up by the competition’s winning algorithm, developed by Meta AI.
Graph-defined digital identity verification can help with this. Customers are given trust in the identities of the people with whom they are transacting business, and their risk of fraud is decreased by continuously sourcing digital data during the photo validation process. Also, businesses that obtain a comprehensive and precise understanding of consumer identity are able to identify more loyal customers and are less likely to fall victim to deepfake attempts.
Conclusion
Even though it can be challenging to avoid all forms of fraud, security teams can halt deepfake technology in its tracks by moving past outdated methods and using identity verification procedures with predictive AI/ML analytics to reliably spot fraud and foster online trust.
With FTx Identity, your customers can have peace of mind that their accounts are protected from fraud and their data is secure. Our innovative age verification technology (AVT) prevents the sale of age-restricted products to minors while integrating seamlessly with any desktop or mobile application for an effortless user experience. Customers can benefit from an easy self-signup and online ID verification so they can buy age-restricted products with ease while protecting their personal information, and retailers can stay compliant with regulations and sell age-restricted products with ease without the fear of losing their license.
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