The emergence of artificial intelligence (AI) has given rise to a troubling new type of fraud that is extending digital impersonation beyond just dating platforms and reality TV and into the financial realm. Right now, fraudsters are increasingly using tools
such as ChatGPT and Generative AI to create deep-fake personas, which they use to deceive financial institutions into granting loans, opening accounts, and making transactions.
Here's a detailed look into how AI is being used to create deep-fake personas for financial fraud, the impact of identity fraud on financial institutions, and what the future may hold for this evolving threat.
The Role of AI in Identity Fraud
Thanks to AI, creating deep-fake or synthetic personas that can be used to deceive financial institutions is easier than ever before. With AI, fraudsters can create deep-fake personas that are almost indistinguishable from real ones, complete with a mix
of fabricated and stolen profile pictures, biographies, social network profiles, social security numbers, driver's licenses, and other documents. These identities can be used to open bank accounts, apply for loans, and carry out financial transactions.
As alluded to above, one of the key challenges with AI-generated deep-fake personas is that they are incredibly difficult to detect from a real persona. For example, they can have a long history of credit and payment activities, which may appear legitimate.
That’s because fraudsters use AI to simulate behaviors consistent with a real person's credit profile, such as establishing credit, making payments, and building a credit history over time. As a result, these IDs appear to be legitimate, so much so that financial
institutions cannot differentiate between a deep-fake persona and a legitimate one, which leads to an increased risk of fraud.
The challenge is exacerbated by the fact that fraudsters are using algorithms to generate large numbers of deep-fake personas quickly and easily. They are also leveraging AI to continually improve the quality of these identities, making them even more difficult
to detect over time.
The Impact of Identity Fraud on Financial Institutions
Identity fraud incidents are rising fast, and it includes fraudsters who are impersonating real (not just fake) customers to steal their money. Over the first half of 2023, the
Identity Theft Resource Center (ITRC) has tracked 1,393 compromises. That’s higher than the total compromises reported every year between 2005 and 2020 except for 2017. As they state in their research, “This puts 2023 on pace to set a record for the number
of data compromises in a year, passing the all-time high of 1,862 compromises in 2021.”
When it comes to industries most affected, financial services trails only healthcare, and the impact on these institutions can be severe and includes significant financial losses, reputational damage, and compliance risks. One of the biggest financial impacts
is the loss of revenue resulting from defaulted loans, charge-offs, and other financial losses. In a typical scenario, identity fraud is used to secure loans or credit lines. These are then used for fraudulent purposes, leaving financial institutions holding
the bag for the losses. And these losses can be significant, particularly when the fraudulent activity is carried out on a large scale which is a trend we see happening now—According to
Electronics Payments International, in North America, the proportion of deepfakes more than doubled from 2022 to Q1 2023.
Identity fraud can also damage a financial institution's reputation. When it is discovered that a financial institution has been defrauded by deep-fake personas, customers can lose trust in its ability to safeguard their financial information and funds.
This can lead to lost business and decreased revenue over time.
Finally, identity fraud can create compliance risks for financial institutions. Regulations require these institutions to verify the identity of their customers to prevent fraud, money laundering, and other financial crimes. If a financial institution is
found to be non-compliant with these regulations, it can result in significant penalties and fines.
The Future of Digital Catfishing and Identity Fraud
As AI technology advances, the threat of digital catfishing and identity fraud is only expected to grow, especially as fraudsters employ more sophisticated AI tools to create deep-fake personas that are even harder to detect.
One potential area of concern is the use of AI to create deep-fake biometric data, such as facial recognition or voice prints. With this technology, fraudsters could create deep-fake personas that are convincing on paper and in person, making it even more
difficult for financial institutions to detect and prevent fraud.
Another potential area of concern is the use of AI to automate the entire process of synthetic identity creation and fraud, from generating fake data to executing fraudulent transactions. This could make identity fraud more scalable and challenging to detect,
as fraudsters could quickly create and use hundreds or even thousands of deep-fake personas simultaneously.
To combat these emerging threats, financial institutions must continue investing in fraud detection and prevention technology. This includes AI-based solutions that can detect patterns and anomalies in data which may indicate synthetic identity fraud. They
must also work more closely with regulators and law enforcement, sharing data and intelligence and developing best practices for combating digital catfishing and identity fraud.
The first step in fighting back begins with protecting your customers' personally identifiable information (PII)—institutions must anonymize and depersonalize all PII to protect each person’s data privacy.
The next step in fighting digital catfishing requires collaboration between financial institutions, data providers, government agencies, and law enforcement. By working together, these stakeholders can share information and intelligence, develop best practices,
and create a united front against this emerging threat.
By sharing data on known fraudsters and suspicious transactions, financial institutions can create a more comprehensive view of the threat landscape and identify emerging patterns and trends in ways that aren’t possible with information siloes. This can
help them better detect fraud before it causes significant financial losses and create faster responses to prevent fraud from happening in the first place.
To facilitate this information-sharing, financial institutions can work with solutions that offer a centralized platform for sharing fraud data safely, securely, and in real time. Machine learning algorithms are critical to this platform, allowing businesses
to analyze large volumes of data and detect patterns and anomalies that may indicate fraudulent activity. This can help financial institutions better detect and prevent identity fraud and then quickly respond to emerging threats. By pooling their resources
and expertise, institutions can create a more effective defense against digital catfishing and identity fraud.
In addition to using ML for fraud detection and prevention, financial institutions can also use it to improve their customer authentication and identity verification processes. By analyzing customer data and behavior, financial institutions can better distinguish
between legitimate and fraudulent customers. They can also help them streamline the identity verification process to create a better customer experience.
The Increasing sophistication and prevalence of these scams require that individuals, organizations, and governments create a united front to combat them effectively. It is also crucial that they remain vigilant, informed, and take necessary precautions
to protect their customers and their sensitive information from falling into the wrong hands. By collaborating and sharing information, we can work towards developing innovative solutions and strategies to stay ahead of the ever-evolving threat landscape of
digital fraud. Together, we can create a safer digital world for everyone.