The Age Of AI (Artificial Intelligence) And New Fraud Patterns – 2024

Fraud Patterns In The Age Of AI (Artificial Intelligence)

Exploring the Continuing Impact of AI on Fraud, Scams, and Cybercrime

Criminology – A SCARS Insight

•  SCARS Editorial Team – Society of Citizens Against Relationship Scams Inc.

Article Abstract

In the ever-evolving landscape of fraud, the integration of generative artificial intelligence (AI) has led to significant shifts in fraud patterns, affecting targets, frequency, and sophistication levels. With the increased use and availability of AI tools in the hands of scammers, fraudsters, and cybercriminals, the scope of criminal activity has expanded, allowing perpetrators to target a broader range of individuals and organizations with more sophisticated scams.

The democratization of scams has led to a surge in the frequency of fraudulent activities, as the low cost and accessibility of AI-driven tools enable perpetrators to automate and streamline their operations.

Moreover, AI-powered tactics have facilitated a noticeable shift in the sophistication of scams, with perpetrators leveraging advanced techniques such as the creation of realistic fake content, sophisticated phishing attacks, personalized social engineering tactics, and evasion techniques to deceive victims and evade detection.

As fraudsters continue to adapt to technological advancements, organizations must adopt proactive measures, including leveraging advanced analytics, machine learning algorithms, and robust security protocols, to safeguard against evolving fraud patterns in the digital age.

Additionally, there is a growing knowledge gap between real professional experts and amateurs, highlighting the need for organizations supporting victims to integrate expertise from fields such as psychology, criminology, victimology, and social science to effectively address the challenges posed by AI-enabled fraud.

Fraud Patterns In The Age Of AI (Artificial Intelligence) - 2024

The Evolution of Fraud Patterns in the Age of Artificial Intelligence (AI) and how it is affecting the Evolution of Scams

In the ever-evolving landscape of fraud, scams, cybercrime, and cybersecurity, the emergence of easily available generative artificial intelligence (AI) has brought about significant shifts in fraud/scam/cybercrime patterns.

As technology advances, fraudsters/scammers/cybercriminals adapt their tactics to exploit vulnerabilities and maximize illicit gains.

Here are the three most prominent changes we have observed in fraud patterns since the widespread availability of Generative AI:

AI Enables Expanded Targets

With the increased use and availability of generative AI (artificial intelligence) in the hands of scammers, fraudsters, and cybercriminals, the landscape of criminal activity has expanded significantly. Generative Artificial Intelligence, such as ChatGPT, Google Gemini, and similar models, enables perpetrators to automate and scale their operations, targeting a broader range of individuals and organizations with more sophisticated scams and fraudulent schemes.

Traditionally, fraudsters targeted a small number of individuals or organizations where they could extract substantial sums of money with minimal effort. However, with the proliferation of Generative AI-driven (artificial intelligence) tools and automation, the unit cost of launching fraudulent attacks has plummeted, enabling perpetrators to cast a wider net. Today, virtually anyone with an online presence is susceptible to scams or fraud schemes. Whether it’s phishing emails, social engineering tactics, or impersonation scams, fraudsters exploit the vulnerabilities of individuals, small businesses, and large corporations alike. The democratization of scams and fraud has shifted the focus from high-value targets to a mass-market approach, where stealing small amounts from numerous individuals yields comparable profits at lower risk.

One notable aspect of expanded targets is the democratization of scams and fraud. Previously, fraudsters tended to target high-value individuals or institutions where they could maximize their gains. However, with the low cost and accessibility of generative AI tools, the unit cost of launching an attack has decreased significantly. Now, perpetrators can target virtually anyone, regardless of their socioeconomic status or financial standing. This shift has led to an increase in the frequency of attacks, as scammers cast a wider net to exploit as many potential victims as possible.

Generative AI has facilitated various types of crimes, including:

  • Phishing and Social Engineering: Scammers use generative AI (artificial intelligence) to create highly convincing phishing emails, text messages, and social media posts designed to deceive recipients into disclosing sensitive information or clicking on malicious links. These messages often mimic communication from trusted sources, such as banks, government agencies, or reputable companies, making them difficult for recipients to discern as fraudulent.
  • Impersonation Scams: Generative AI (artificial intelligence) enables scammers to impersonate individuals or entities convincingly. They can create fake social media profiles, emails, or websites that closely resemble legitimate counterparts, tricking victims into believing they are interacting with a trusted source. This tactic is commonly used in romance scams, where perpetrators pose as potential romantic partners to establish rapport and exploit victims emotionally and financially.
  • Fraudulent Content Creation: Perpetrators use generative AI (artificial intelligence) to generate fake reviews, testimonials, news articles, or product listings to promote fraudulent schemes or manipulate public perception. By generating authentic-looking content at scale, scammers can lend credibility to their scams and deceive unsuspecting individuals into making ill-informed decisions.
  • Financial Fraud: Generative AI (artificial intelligence) enables scammers to create fake invoices, receipts, financial statements, or investment proposals to defraud individuals or organizations. By leveraging sophisticated language generation capabilities, perpetrators can craft convincing documents that deceive victims into transferring funds or disclosing sensitive financial information.
  • Identity Theft and Synthetic Identity Fraud: With generative AI, scammers can create synthetic identities by combining real and fabricated personal information to open fraudulent accounts, obtain credit cards, or apply for loans. These synthetic identities are used to commit various forms of fraud, including credit card fraud, tax refund fraud, or healthcare fraud, often resulting in significant financial losses for victims.
  • Tech Support Scams: Scammers employ generative AI (artificial intelligence) to create fake tech support websites, pop-up messages, or phone scripts to trick users into believing their devices are infected with malware or viruses. Victims are then manipulated and coerced into paying for unnecessary technical support services or providing remote access to their devices, enabling scammers to steal sensitive information, intrude into business networks, or install malicious software.
  • Deepfake Technology: While not exclusively reliant on generative AI, deepfake technology leverages similar principles to create highly realistic audio and video content that misleads and deceives viewers. Deepfakes can be used to impersonate individuals, manipulate public opinion, or fabricate evidence, posing significant risks to individuals’ reputations, privacy, and security. It can do this as static images, fake videos, or even in real-time on zoom calls now.

The increased use of generative AI (artificial intelligence) by scammers, fraudsters, and cyber criminals has amplified the scope and sophistication of criminal activity, posing significant challenges for individuals, businesses, and law enforcement agencies. As these technologies continue to evolve, combating fraudulent schemes and protecting against digital threats will require ongoing vigilance, technological innovation, and collaborative efforts across sectors.

AI Enables Increased Frequency

With the increased use and availability of generative AI (artificial intelligence) in the hands of scammers, fraudsters, and cybercriminals, there has been a very notable uptick in the frequency of fraudulent activities. Generative AI, such as language models like ChatGPT and image synthesis algorithms, empowers perpetrators to automate and streamline their operations, enabling them to carry out scams at scale and with greater efficiency.

In the past, financial institutions primarily faced threats from organized fraud rings executing targeted attacks. However, the rise of AI-driven fraud/scam tactics has ushered in a new era characterized by a surge in “layman” attackers. These individuals, often lacking sophisticated technical skills, leverage readily available AI (artificial intelligence) tools, chatbots, or online tutorials to perpetrate fraud on a large scale. As a result, banks and other institutions contend with a relentless onslaught of fraudulent activities, ranging from account takeovers and identity theft to payment fraud and synthetic identities. The frequency and diversity of attacks have overwhelmed traditional fraud detection systems, necessitating innovative strategies to combat the rising tide of financial crime. In effect, it is going to take artificial intelligence to counter artificial intelligence!

One of the key ways in which AI contributes to the increased frequency of fraudulent activities is through automation. Traditionally, perpetrating scams required significant manual effort, from crafting convincing messages to interacting with potential victims. However, with generative AI, scammers can automate the entire process, generating vast quantities of fraudulent content with minimal human intervention. For example, AI-powered chatbots can engage in conversations with multiple targets simultaneously, tailoring messages to each recipient to maximize the likelihood of success.

Also, generative AI allows scammers to personalize their attacks in ways that were previously impractical. By analyzing large datasets of personal information scraped from social media, online forums, or data breaches, AI (artificial intelligence) algorithms can generate highly targeted messages that exploit individuals’ specific interests, demographics, or vulnerabilities. This level of social engineering personalization increases the effectiveness of scams, making them more difficult for recipients to recognize as fraudulent and avoid.

AI enables scammers to adapt and evolve their tactics rapidly in response to countermeasures implemented by security professionals or changes in the threat landscape. Machine learning algorithms can analyze the effectiveness of different scam variants in real time, identifying successful strategies and iteratively improving upon them. This iterative process of optimization allows scammers to stay one step ahead of detection mechanisms, ensuring the continued success of their fraudulent activities.

The scalability of generative AI (artificial intelligence) facilitates the mass deployment of fraudulent and deceptive campaigns across multiple channels simultaneously. Scammers can leverage AI-generated content to inundate potential victims with phishing emails, social media messages, or fake advertisements, and fake profiles, increasing the likelihood of successful engagements. This widespread distribution amplifies the impact of fraudulent activities, reaching a larger audience and yielding higher returns for perpetrators.

The increased use of generative AI by scammers, fraudsters, and cybercriminals has led to a surge in the frequency of fraudulent activities by automating processes, personalizing attacks, enabling rapid adaptation, and facilitating the mass deployment of scams. As AI technologies continue to advance, addressing the challenges posed by fraudulent activities will require concerted efforts from cybersecurity professionals, law enforcement agencies, and technology providers to develop effective AI-based countermeasures and safeguard individuals and organizations against digital threats.

AI Enables Shifting Sophistication

The increased use and availability of generative AI (artificial intelligence) among scammers, fraudsters, and cybercriminals have resulted in a noticeable shift in the sophistication of fraudulent activities. Generative AI, powered by machine learning algorithms, enables perpetrators to create more convincing and intricate scams, posing significant challenges for detection and mitigation efforts.

While AI has democratized fraud, it has also led to a paradoxical decline in average sophistication levels among perpetrators. Many fraudsters now rely on off-the-shelf code generators and AI-powered bots to automate their attacks, reducing the need for advanced technical expertise. Consequently, the barrier to entry for committing fraud has lowered, attracting a broader pool of opportunistic individuals. However, amidst this proliferation of amateur fraudsters, sophisticated attackers exploit the chaos by concealing their activities within the noise generated by less skilled perpetrators. As a result, fraud detection teams face the daunting task of distinguishing between routine fraud attempts and highly orchestrated, covert attacks, such as those that come from nation-state actors.

One way in which AI (artificial intelligence) contributes to the shifting sophistication of scams is through the creation of highly realistic fake content. For instance, AI algorithms can generate convincing counterfeit documents, such as identification cards, bank statements, or invoices, which are indistinguishable from genuine ones to the untrained eye – at least online. These forged documents are often used in various fraud schemes, including identity theft, loan fraud, or account takeover, allowing scammers to bypass traditional verification processes and deceive individuals or organizations.

Generative AI facilitates the creation of sophisticated phishing attacks that mimic legitimate communication channels with unprecedented accuracy. By analyzing large datasets of genuine emails, text messages, or social media posts, AI algorithms can generate plausible replicas that replicate the tone, style, and formatting of authentic communications. These AI-generated phishing emails or messages often contain malicious links or attachments designed to trick recipients into disclosing sensitive information or downloading malware, compromising their security and privacy.

AI-powered social engineering attacks leverage psychological lures and manipulation techniques to exploit human vulnerabilities effectively. By analyzing vast amounts of social media data, online activity, or publicly available information, AI algorithms can profile individuals’ behavior, preferences, and relationships to craft tailored messages that resonate with their interests or concerns. These personalized social engineering and manipulative tactics, coupled with convincing narratives or emotional appeals, increase the likelihood of successful engagements and decrease recipients’ skepticism, making them more susceptible to manipulation.

AI-driven evasion techniques enable scammers to circumvent traditional security measures and detection mechanisms effectively. For example, AI algorithms can generate polymorphic malware that continuously mutates its code to evade signature-based antivirus software or intrusion detection systems. Similarly, AI-powered chatbots can engage in dynamic conversations with security professionals, mimicking human behavior and adapting responses to avoid detection during security assessments or penetration testing.

Overall, the increased use of generative AI among scammers, fraudsters, and cybercriminals has led to a shift in the sophistication of fraudulent activities by enabling the creation of realistic fake content, sophisticated phishing attacks, personalized social engineering tactics, and evasion techniques. Addressing the challenges posed by these advanced AI-driven scams requires collaborative efforts from cybersecurity professionals, law enforcement agencies, and technology providers to develop robust defense mechanisms, enhance threat intelligence capabilities, and raise awareness among individuals and organizations about the risks associated with AI-enabled fraud.


The integration of AI technology has revolutionized the landscape of fraud, reshaping targets, frequency, and sophistication levels. As fraudsters adapt to technological advancements, organizations must adopt proactive measures to fortify their defenses and mitigate risks effectively. By leveraging advanced analytics, machine learning algorithms, and robust security protocols, businesses can stay one step ahead of evolving fraud patterns and safeguard their assets in the digital age.

Another interesting side effect of this artificial intelligence revolution is the vastly expanding knowledge gap between real professional experts and the amateurs who claim expertise. Learning about scams by looking at stolen photos and being told to watch for bad grammar is very obsolete. Organizations that support victims now need real psychological professionals, criminologists, victimologists, and social scientists on their team to have any chance to stay current with the evolving artificial intelligence landscape.

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