Artificial Intelligence (AI) has revolutionized many industries, including finance, security, and fraud detection. However, it has also given cybercriminals powerful new tools to execute sophisticated payment fraud schemes. Fraudsters now leverage AI to automate attacks, bypass security measures, and create convincing deception tactics.
Deloitte’s Center for Financial Services projects that generative AI could enable fraud losses to reach $40 billion in the United States by 2027, up from $12.3 billion in 2023, representing a compound annual growth rate of 32 percent.
How Cybercriminals Use AI for Payment Fraud
Cybercriminals leverage AI-driven tactics to execute sophisticated payment fraud schemes. Here are five of the most prevalent methods they use:
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AI-Powered Phishing Attacks
AI can generate highly personalized, convincing phishing emails and messages that mimic legitimate communications from real individuals and organizations. By using publicly available information from LinkedIn, social media, or data breaches, AI can personalize phishing emails to tailor messages for specific targets at scale.
Cybercriminals use AI-generated phishing emails to steal login credentials, credit card details, and banking information, leading to unauthorized transactions and account takeovers.
Fraudsters use natural language processing (NLP) to craft emails with fewer grammatical errors, making them harder to detect.
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Deepfake Technology for Social Engineering
Powered by AI, deepfake technology creates realistic audio and video impersonations of executives, financial officers, or customers to authorize fraudulent payments.
In business email compromise (BEC) scams, an AI-generated voice can speak with unsuspecting victims to trick them into processing unauthorized transactions, such as wire transfers and invoice fraud.
In 2024, a staff member of the UK engineering firm Arup was tricked by a video call that included an AI-generated deepfake of the Chief Financial Officer. The ill-fated staff member ended up transferring approximately $25 million to the perpetrators.
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Synthetic Identity Fraud
AI can generate fake identities using a combination of real and fake information, such as stolen social security numbers, with AI-generated names and addresses, making fraudulent transactions harder to detect. These synthetic identities are used to open bank accounts, apply for credit cards, secure loans, and make purchases before disappearing.
According to a Federal Reserve Payments Fraud Insights report, synthetic identity fraud is the fastest-growing financial crime in the U.S., costing banks over $6 billion annually.
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Automated Bot Attacks on Payment Systems
AI-powered bots can run credential-stuffing attacks by testing thousands of stolen usernames and passwords to gain unauthorized access to accounts. Bots can also conduct card testing attacks, where cybercriminals use small purchases to verify that stolen credit card details are valid before making larger transactions.
Bots can systematically file fraudulent claims at scale, exploiting dispute resolution systems, drain merchant revenues, overwhelm customer support, and manipulate payment ecosystems.
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Bypassing Fraud Detection Systems
Scammers use AI to analyze fraud detection patterns, identify weaknesses in security protocols, and adapt attack strategies accordingly. AI tools generate realistic but fraudulent purchase behaviors, mimicking legitimate customers to bypass anomaly detection systems.
Similar to how fraud-detection systems may use AI to learn from financial transactions to detect anomalous behavior, cybercriminals have developed AI-based programs that learn from declined transactions and adjust parameters to increase approval rates for fraudulent purchases.
How Businesses Can Stay Ahead
To combat the growing threat of AI-generated payment fraud, businesses must adopt proactive strategies that strengthen security, detect fraudulent activity in real-time, and stay ahead of evolving cyber threats. Here are six strategies to prevent AI-driven fraud:
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Implement AI-Driven Fraud Detection Systems
Traditional rule-based fraud detection is no longer enough. AI-powered fraud prevention systems can analyze vast amounts of transaction data in real-time and detect suspicious activity with high accuracy. Businesses should deploy machine learning models to detect unusual transaction patterns and anomalies that indicate fraud.
AI-based fraud detection can flag unusual transaction behaviors, such as rapid purchases from multiple locations or inconsistent spending patterns. Adaptive AI security systems evolve with emerging fraud tactics, making it harder for criminals to bypass detection.
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Strengthen Multi-Layered Authentication
AI-generated deepfakes and credential-stuffing attacks make traditional passwords and one-time passcodes (OTPs) vulnerable. Businesses must implement multiple layers of authentication to verify transactions securely.
Implement multi-factor authentication (MFA) with biometrics (uses fingerprints, facial recognition, or voice verification to confirm user identity) and behavioral analytics (analyzes unique user behaviors, such as typing speed, mouse movements, and device usage, to detect fraud).
Passwordless authentication eliminates traditional passwords, reducing the risk of credential-based attacks like phishing, brute-force attacks, and credential stuffing. Instead, it relies on cryptographic authentication methods, such as biometrics, security keys, or device-based authentication.
By implementing Fast Identity Online 2 (FIDO2), a key enabler of passwordless authentication, and Web Authentication API (WebAuthn), which uses biometrics and hardware security keys, organizations can enhance security while improving user experience, reducing reliance on passwords, and eliminating phishing as an attack vector.
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Enhance Employee Training and Awareness
AI-generated phishing emails and deepfake scams target employees, deceiving them into authorizing fraudulent payments or revealing sensitive information. Conduct ongoing security awareness training to update employees on AI-based fraud threats and how to recognize phishing or social engineering attempts. Encourage employees to report suspected deepfakes through secure channels.
Implement simulated phishing tests using AI-generated attacks to gauge employee readiness and improve fraud awareness.
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Leverage Blockchain and Tokenization for Payments
AI-powered payment fraud often exploits weaknesses in traditional payment processing. Blockchain and tokenization reduce these risks by securing transaction data and integrity and minimizing exposure to fraud.
Blockchain’s decentralized, immutable ledger enhances transaction integrity, making it harder to alter records retroactively. It prevents specific fraud types (e.g., tampering with transaction records), but does not address all fraud vectors (e.g., phishing, stolen credentials, or social engineering).
Blockchain alone does not directly counter AI-powered fraud (e.g., deepfakes, synthetic identities), and works best alongside AI-driven monitoring and authentication tools. Blockchain adoption in mainstream payment systems is still limited due to scalability, cost, and regulatory challenges. It also does not eliminate the need for fraud detection layers (e.g., AI monitoring, behavioral analytics, etc.).
Tokenization converts sensitive payment information (e.g., credit card numbers) into unique tokens that cannot be used outside the intended transaction. It also helps safeguard cardholder data and can minimize the scope of encryption requirements, such as those outlined in PCI DSS, while enhancing the protection of personal data and ensuring compliance with data privacy regulations like the GDPR.
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Employ Real-Time Fraud Monitoring
AI-powered payment fraud happens within seconds. Businesses must have real-time monitoring and response systems to detect and stop fraudulent transactions before they are processed.
AI-driven monitoring tools constantly scan transactions and flag irregularities based on behavior, location, and device usage. AI-powered fraud detection systems should provide instant alerts on suspicious activities. Automated fraud response systems can block suspicious transactions, freeze compromised accounts, and alert users instantly.
Tools like Splunk, Darktrace, and AWS Fraud Detector provide real-time analytics by leveraging machine learning, AI, and big data processing to detect, analyze, and respond to threats as they emerge.
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Collaborate and Share Threat Intelligence
Cybercriminals continually evolve their AI-powered fraud methods. Partner with financial institutions, cybersecurity firms, and law enforcement to stay informed and ahead of threats.
Standards like Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Indicator Information (TAXII) are designed to improve the sharing of cyber threat intelligence (CTI) among organizations, government agencies, and cybersecurity teams.
Information Sharing and Analysis Centers (ISACs) are trusted organizations that gather, analyze, and share actionable threat intelligence with their members. They also provide tools and resources to help mitigate risks and strengthen resilience against cyber threats. FS-ISAC, or Financial Services – Information Sharing and Analysis Center, enhances cybersecurity and resilience within the global financial system, safeguarding financial institutions and the individuals who rely on them.
Utilize fraud intelligence databases and industry-wide alerts to prevent attacks before they happen.
AI-powered payment fraud is evolving rapidly, and cybercriminals are using cutting-edge technology to exploit vulnerabilities in payment systems. Investing in AI security tools, continuous monitoring, and employee training will be key to staying ahead in the fight against AI-powered payment fraud.
CampusGuard assists organizations combat AI-driven fraud by offering audits, AI model deployment, and employee training courses. Contact us for guidance on how we can help protect your organization.
