Generative AI: How to Approach Risk Management In This New Era of Fraud

We are witnessing a significant surge in AI-enabled fraud and scams, and when coupled with both existing and newly surfaced data breaches, it poses a substantial new threat to the financial industry.
Picture of Ravi Sandepudi

Ravi Sandepudi

July 25, 2023

Recently, global law enforcement agencies released a report highlighting the potential misuse of large language models (LLMs), such as ChatGPT, by criminals. The paper offers insights into the emerging threats and the preventive measures required to safeguard against them.

LLMs are rapidly transforming diverse industries, including the dynamic fintech sector. Yet, this revolutionary technology is more than just catalyzing legitimate business sectors. Fraudsters and scam artists are keen to harness the transformative power of these models for their illicit gains.

This alarming trend compounds an already escalating issue of fraud. The FBI recorded over 800,000 fraud complaints in 2022, leading to astounding losses surpassing $10.3 billion. Coordinated account attacks skyrocketed by a remarkable 307% from 2019 to 2021 (Q3 2021 Digital Trust & Safety Index), resulting in an unprecedented $11.4 billion financial loss. This has severely damaged customer trust and tarnished corporate reputations.

We are witnessing a significant surge in AI-enabled fraud and scams, and when coupled with both existing and newly surfaced data breaches, it poses a substantial new threat to the financial industry.

Generative AI Fueling the Democratization of Fraud

The so-called “democratization of fraud” can be observed for several years. Technological advancements like easy-to-access, anonymous peer-to-peer networks (e.g., the so-called dark web), public fraud-related chat channels on platforms such as Telegram and Discord, and the open-source availability of sophisticated fraud tools make it easier than ever for anyone to commit online fraud.

LLMs and generative AI’s capabilities are astounding and set to propel this development into a completely new dimension.

Generative AI provides fraudsters with opportunities that were either previously inaccessible or highly challenging to leverage. From fraud mentoring at scale through chat-bots, the easy creation of convincing deep fakes and synthetic identity documents that trick license verification services, to undermining biometric authentication, we are just at the dawn of what’s to come. Even traditional fraud scams are reaching new heights, amplified by this innovative technology. For instance, consider the distressing case where a mother was blackmailed by criminals who used AI-generated voice simulations of her daughter.

Document verification and voice authentication will soon be the first lines of defense to fall and must be accompanied by additional layers to be a suitable defense mechanism. With rapid advancements in AI-powered tools capable of generating photorealistic images and audio based on a few seconds of voice data, we will likely see fraudsters producing realistic-looking documents and biometric data at scale. While the industry is debating global standards to increase the trust in digital documents, this development will take years, if successful, and provides fraudsters ample time to launch unprecedented attacks.

This New Era of Fraud Requires a Multi-layered, AI-Powered Approach to Fraud Prevention

Confronted with these fundamental threats, traditional and outdated fraud prevention measures are woefully inadequate. It’s high time every financial institution prioritized a substantial overhaul of its fraud prevention strategy. Old-school systems will soon become unuseful, easily sidestepped by the sophisticated techniques employed by fraudsters. Single-layered fraud strategies, such as those entirely based on document verification, will quickly be bypassed. Moreover, many existing systems aren’t designed to handle the scale enabled by this new wave of automation technology – and just break under the speed and velocity of the attacks.

In our view, the next generation of fraud management tools will have to be built on three key principles:


A flexible and swiftly adaptable rule-based layer to rapidly detect and anticipate patterns. Rules provide a simple yet highly effective first layer of defense for fraud detection. They allow for human anticipation of potential fraud attacks and proactive adjustments to the defense layer. In fact, some of the best fraud-prevention companies worldwide have whole teams creating rules and mechanisms by anticipating fraudsters’ behaviors.


A multi-channel approach that monitors user behavior across the entire customer journey. The most effective fraud systems consider data in its context, combining information across a user’s journey checkpoints to create a holistic view. This ensures that a single attack, like synthetic document creation bypassing the IDV layer, will not succeed at the payout stage – or that an account takeover is unsuccessful when the bank account information or password is changed.

Powered by AI

Lastly, self-adapting AI-based prevention and monitoring solutions that continuously learn from changing data and adapt to evolving fraud patterns based on expert feedback. In the future, the interaction between fraud specialists and AI-powered systems will play a critical role in detecting and preventing fraudulent attacks on a large scale more rapidly.


The rapid evolution of generative AI will inadvertently fuel the “democratization” of fraud, enabling individuals and small groups to perpetrate scams and attacks that were once exclusive to large, organized crime syndicates. As long as there are profits to be made, fraud will continue to evolve. Financial institutions must revamp their defense systems today, with fraudsters leveling up their tactics with new technologies. The increasing number of fraudulent attacks, scams, data breaches, and economic losses due to fraud is alarming. Given the fascinating possibilities that the rise of AI enables, we are just seeing the beginning.

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