The Evolution of Financial Fraud

By Greg Woolf

By Greg Woolf, CEO of FiVerity

The marriage of fraud and artificial intelligence (AI) is lethal. Right now, fraudsters are upping their games, leveraging new and innovative tools such as ChatGPT and Generative AI to wreak havoc on the financial world. Their goal? To create deep-fake personas that look so authentic that financial institutions are granting them loans, allowing them to open accounts, approving transactions, the list goes on.

Adding insult to injury, most don’t realize the damage inflicted upon them until it’s too late. This is the new reality financial institutions face today thanks to AI, which not only allows criminals to create deep-fake or synthetic personas but makes the process easier than ever.

This is troubling on many levels.

First, as I mentioned above, these fraudulent identities are virtually undistinguishable from authentic ones, and discerning the difference is a challenge, even to the trained professional. Here’s why—deep fake IDs include a long credit and payment history, exactly the information an institution would see with all their legitimate customers. Exacerbating the issue is that fraudsters are turning to algorithms to quickly create multiple deep-fake personas, which they can refine continually using AI to avoid detection.

Add it all up, and it’s no surprise that fraudsters are achieving significant levels of success and becoming more and more aggressive—according to a TransUnion 2023 State of Omnichannel Fraud Report, digital fraud attempts have increased 80% from 2019 to 2022, while rising 122% for digital transactions originating in the U.S. during that time.

You don’t need to be an expert to realize that the success of fraudsters spells trouble for financial institutions.

  • First and foremost are the financial losses that stem from defaulted loans, charge-offs, and more.
  • Next comes damaged reputations, which can tarnish a business where trust is one of THE key attributes that customers value most—how can a consumer be expected to choose a financial institution making front-page news because it was defrauded by deep-fake personas?
  • And don’t forget compliance. Financial institutions are required to verify the identity of their customers to prevent fraud, money laundering, and other financial crimes. Any failure to meet these mandates can come with a hefty fine and penalty.

Going From Bad….to Worse

If you think the above scenario sounds ominous, I have bad news. It’s only going to get worse. That’s because technology never sits still. It’s always advancing and growing in sophistication, and incidents of digital catfishing and identity fraud will reach new levels as fraudsters leverage these advancements. This will manifest itself in different ways. One will be the use of deep-fake biometric data. This includes facial recognition or voice prints. The result would be a deep fake persona that is convincing on multiple levels, on paper and in person. Just imagine the challenges businesses will face trying to distinguish the fraudulent from the legitimate.

Criminals will also leverage AI to automate the creation of synthetic identity creation. The result would be hundreds to thousands of deep-fake personas being created and used simultaneously. This scale would be unlike anything we have ever seen before.

Fighting back 

Fighting back starts with collaboration. Financial institutions must be committed to sharing information on known fraudsters and intelligence on suspicious transactions. By pooling the resources and expertise of all these institutions, they can identify emerging patterns and trends and better detect digital catfishing and identity fraud ways that aren’t possible with information siloes.

Working together, they can also devise best practices. This should include everything from how to best share data and intelligence, how to act before an incident causes significant financial losses, and how to prevent these incidents from happening in the first place.

For anyone wondering what will support this collaborative mode, your best bet is a centralized platform that enables the safe, secure, and real-time sharing of fraud data. The platform should leverage AI and machine learning algorithms, and here’s why. AI and ML make it possible for businesses to analyze huge libraries of data to detect patterns and anomalies that may indicate fraudulent activity. Some key use cases that can help spot fraud include:

  • Dynamic Profiling: Implement a system that dynamically profiles user activity and attributes such as name, email address, zip, and state. This means not merely looking for hard matches but understanding the normal behavioral patterns of users to spot anomalies.
  • Multi-Attribute Analysis: Why look at a single attribute when you can examine multiple attributes and the interrelationship between each? For example, a change in email address alone might not raise a flag. Many of us use more than one email address. But when that switch coincides with a change in state, further investigation may be necessary.
  • Machine Learning Adaptability: Leverage adaptive machine learning algorithms to gain insights from the constantly shifting tactics. As you gain new levels of knowledge, take what’s been learned and update detection protocols.
  • Time-based Monitoring: Implement time-based flags that trigger alerts when sudden changes in key attributes are made in a short timeframe. This helps to enable fast action while freeing teams from spending countless hours sifting through data to identify fraudulent activity.

All of these capabilities are hugely valuable, but I would be remiss if I didn’t spotlight your biggest resource in this fight, your fraud analysts. At the end of the day, the intuition of these experts is invaluable. We encourage businesses to continue plugging into their knowledge experience to conduct periodic manual reviews, especially in cases that the system flags as borderline.

At the end of the day, financial services businesses face a highly sophisticated threat that is escalating in frequency. This is not a battle that can be one in isolation. It required action that is equal parts collaboration and a commitment to tapping into the latest innovations. By gaining a better understanding of fraudsters, they can identify patterns as well as fraudulent accounts that can not only take preemptive action but also collaborate on methods to stay ahead of the ever-evolving threat landscape of digital fraud.


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