The landscape of financial fraud is rapidly changing due to the influence of artificial intelligence, making fraudulent activities more advanced and difficult to identify. With a staggering 80% rise in fraud incidents over the past three years, only 22% of companies have implemented AI-driven defenses. Stuart Wilkie, Head of Commercial Finance at Anglo Scottish Finance, sheds light on this shifting threat landscape and offers insights on how both organizations and individuals can combat the problem.
Addressing financial fraud has escalated in complexity in recent years, primarily due to the growing use of AI (artificial intelligence). A recent study by Signicat reveals that AI influences 42% of all financial fraud attempts, yet only 22% of companies have incorporated AI defenses. This gap raises significant concerns, though it is not a recent phenomenon.
In both the pre-ChatGPT world and following its 2022 introduction, the deployment of AI in fraudulent schemes has increased. A 2022 analysis by Cifas reported an 84% surge in cases where AI was used to infiltrate the security systems of banks.
AI simplifies the execution of fraudulent acts for swindlers, thereby contributing to a general uptick in fraud incidents. Signicat’s findings also indicate that the total number of fraud attempts has skyrocketed, with an 80% increase over the last three years. This alarming rise can be partially attributed to AI’s role in facilitating financial crimes, along with other external factors.
Which types of AI-driven financial fraud are most prevalent, and how can both individuals and institutions defend themselves against such threats?
Synthetic identity fraud
A significant portion of AI-assisted financial fraud can be classified as synthetic identity fraud. In this scheme, fraudsters employ AI to fabricate identities using a mix of real and fictitious information, allowing them to secure loans, credit lines, or even public benefits.
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Fraudsters leverage AI’s capability to quickly analyze large datasets to construct realistic profiles that mirror demographic patterns. Generative AI also assists in creating believable credit histories, making these profiles nearly indistinguishable from real individuals during standard verification processes.
The U.S. Government Accountability Office (GAO) estimates that over 80% of all new account fraud can be traced back to synthetic identity fraud, highlighting the urgent need for enhanced security measures.
Deepfaking
The increasing use of biometrics for security has lessened our dependency on passwords. For many, this shift is beneficial since it alleviates the pressure of remembering numerous passwords, as facial recognition or fingerprints can now facilitate mobile banking and social media access.
Yet, the advent of generative AI has simplified the process for fraudsters to circumvent these security measures using deepfakes (images, audio, or videos modified or generated by AI portraying real or fictional individuals).
When combined with personal information, like national insurance numbers or addresses, deepfakes are discovering vulnerabilities in financial institutions’ security protocols, allowing fraudsters unauthorized access to bank accounts and more.
Fake customer service
Generative AI not only aids scammers in impersonating banking customers to gain account access but also assists in targeting customers by posing as customer service representatives. In the past, spotting fraudulent texts or emails was usually straightforward due to spelling errors or a tone inconsistent with the bank.
Now, with generative AI chatbots in play, crafting emails that sound identical to those of a bank is much simpler—scammers can easily match the corporate tone and will never make typographical errors.
This facet of financial fraud extends beyond emails; many instances of AI-generated content have led to the creation of entire fake websites designed to mimic reputable banking institutions.
Combating AI fraud
Fortunately, as fraudsters exploit AI to commit crimes, banking and financial institutions are harnessing machine learning to detect fraudulent activities—and they are becoming increasingly adept at it. For instance, HSBC collaborated with Google in 2021 to develop an AI system designed to uncover financial crimes.
Their Dynamic Risk Assessment system is becoming more precise; initially, there were many false positives, but these decreased by 60% from 2021 to 2024. The more accurate these systems become, the better the chances of eradicating financial fraud entirely.
In general, banks are successfully enhancing their biometric security systems to combat deepfaking; by identifying more scammers via their own machine-learning technologies, they can swiftly improve detection.
However, it’s not solely about combating fraud at an institutional level. Preventing fraud effectively requires education—empowering bank customers to recognize emerging scams and avoid falling victim.
Nonetheless, rising AI and technological advancements complicate matters, changing the fraud landscape almost daily. When individuals receive communications from their bank through any channel—be it email, phone call, or otherwise—they must critically evaluate the requests made. Most banks will never require specific personal details, so it is crucial for individuals to remain vigilant at all times.
Stuart Wilkie, Head of Commercial Finance at Anglo Scottish
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