Artificial intelligence is transforming the financial sector in unprecedented ways. While AI enables organizations to improve operational efficiency, customer experience, and fraud detection capabilities, it is also creating new opportunities for cybercriminals. Generative AI technologies can now produce realistic images, videos, voices, and identities at scale, fundamentally changing the nature of financial fraud.
Financial institutions and regulated organizations should pay close attention to this shift. Traditional fraud schemes based on stolen credentials or identify theft are evolving into sophisticated AI-enabled attacks involving synthetic identities, deepfakes, and automated social engineering. As fraudsters gain access to increasingly powerful AI tools, organizations must rethink how they verify identities, manage risk, and maintain trust.
How are Deepfakes being used in financial fraud?
Deepfakes are AI-generated synthetic media that manipulates or creates realistic images, videos, and audio recordings. Rapid improvements in AI technology have significantly increased the realism of synthetic content, making fake voices, images, and videos more convincing and easier to produce than ever before. The 2025 Annual Report of the Belgian Financial Intelligence Processing Unit (CTIF-CFI) confirms that these technologies are already being exploited by criminals. According to the report, AI-generated deepfakes of well-known personalities are being used to lure victims onto fraudulent investment platforms, while readily available phishing kits enable increasingly sophisticated scams to be launched with little technical expertise.
The implications for fraud are significant. Criminals have successfully used AI-generated executive voices to convince employees to authorize fraudulent payments. In 2024, an employee of a Hong Kong engineering firm was deceived into transferring approximately $25 million after participating in a video conference where AI-generated impersonations of senior executives appeared to authorize the transactions. Deepfake technology is also increasingly used for remote onboarding fraud, where fake videos and manipulated biometric data are presented during identity verification processes. In another case, a Dutch man reportedly used deepfake technology to bypass facial recognition checks and open 46 bank accounts in an important bank under other people’s identities.
What is synthetic identity fraud and why is it so difficult to detect?
Synthetic identity fraud is emerging as one of the most difficult forms of financial crime to detect. Unlike traditional identity theft, synthetic fraud combines genuine and fabricated information to create entirely new identities that do not belong to real individuals.
AI significantly accelerates this process. Fraudsters can use generative models to create realistic identities, supporting documents, profile images, and behavioral patterns that appear legitimate during customer onboarding. These synthetic identities can gradually establish credit histories, open accounts, and conduct transactions before being used to commit large-scale fraud.
AI significantly accelerates this process. Fraudsters can use generative models to create realistic identities, supporting documents, profile images, and behavioral patterns that appear legitimate during customer onboarding. These synthetic identities can gradually establish credit histories, open accounts, and conduct transactions before being used to commit large-scale fraud.
This presents major challenges for Know Your Customer (KYC) programs and customer due diligence processes. Traditional verification methods often focus on validating documents or static personal information. However, synthetic identities may pass these checks because the individual data elements appear authentic when viewed in isolation.
Recent research by the University of Technology Sydney and the Georgia Institute of Technology suggests that effective synthetic identity detection requires a combination of behavioural analytics, network analysis, and cross-channel monitoring. By analysing relationships between accounts, devices, addresses, and transactions, advanced AI models can uncover seemingly unrelated activities that are actually part of coordinated fraud schemes.
How is AI challenging compliance and financial regulation?
AI-enabled fraud is evolving faster than traditional compliance controls were designed to handle. Deepfakes, synthetic identities, and AI-powered impersonation attacks challenge rule-based monitoring and manual investigations, making it increasingly difficult for financial institutions to detect sophisticated fraud.
Although Europe has significantly strengthened its regulatory framework, most regulations were not designed specifically to address AI-generated fraud. Instead, frameworks such as the Digital Operational Resilience Act (DORA), the Sixth Anti-Money Laundering Directive (AMLD6), and the Markets in Crypto-Assets Regulation (MiCA) establish governance, resilience, and risk management requirements that encourage organizations to adopt more dynamic, technology-driven fraud controls.
For example, DORA promotes continuous monitoring and stronger cyber resilience, while AMLD6 reinforces risk-based customer due diligence and transaction monitoring. As AI-generated identities become increasingly convincing, financial institutions are moving beyond one-time identity verification by incorporating behavioural analytics, biometric authentication, network analysis, and continuous risk assessments. Similarly, MiCA strengthens governance in crypto markets, where AI-powered fraud is becoming an increasing concern.
At the supervisory level, the creation of the Anti-Money Laundering Authority (AMLA), the National Bank of Belgium (NBB)'s focus on technology governance, and the FATF's 2025 Mutual Evaluation Report on Belgium all reflect a broader shift toward risk-based supervision. While regulators rarely require organizations to use AI explicitly, the level of monitoring, adaptability, and operational resilience they expect is becoming difficult to achieve without AI-powered analytics.
However, using AI introduces new compliance challenges. Many advanced models operate as "black boxes," making it difficult to explain why a transaction or customer was flagged. As a result, AI governanceis becoming just as important as AI itself. Financial institutions must ensure that AI systems are transparent, explainable, regularly monitored for bias, and fully auditable. Strong model governance and documented decision-making are essential to meet regulatory expectations while maintaining customer trust.
How can organizations protect themselves against AI-driven fraud?
As fraudsters increasingly use AI to automate attacks and create convincing fake identities, organizations are turning to AI to strengthen their own defenses. Machine learning, behavioural analytics, network analysis, and anomaly detection can identify suspicious patterns that traditional rule-based systems often overlook, enabling faster and more proactive fraud detection.
However, adopting AI is no longer just a technology decision: it is also a governance and compliance challenge. With the introduction of the EU AI Act, organizations using AI for high-risk activities such as biometric identification, identity verification, and fraud detection must ensure their systems are transparent, well-governed, and subject to appropriate human oversight. Compliance teams will increasingly need to demonstrate not only that AI is effective, but also that its decisions can be explained,audited, and monitored over time.
An effective fraud strategy begins with stronger identity verification. Rather than relying solely on documents or one-time authentication, organizations should combine traditional KYC controls with biometric verification, document authenticity checks, behavioural authentication, and device intelligence. This layered approach makes it significantly more difficult for fraudsters using synthetic identities or deepfakes to bypass onboarding controls.
Continuous monitoringis equally important. AI-powered User and Entity Behaviour Analytics (UEBA) allows organizations to detect unusual login behaviour, suspicious transactions, device changes, or other anomalies throughout the customer lifecycle, helping identify account takeover attempts and evolving fraud schemes before significant losses occur.
AI can also uncover hidden fraud networks by analysing relationships between customers, devices, accounts, addresses, and transactions. These insights enable investigators to detect coordinated fraud schemes that would be extremely difficult to identify manually.
Ultimately, technology alone is not enough. Successfully combating AI-driven fraud requires close collaboration between compliance, fraud, cybersecurity, risk, and technology teams. Organizations that combine AI-powered detection with strong governance, regulatory alignment, and human expertise will be best positioned to respond to an increasingly sophisticated fraud landscape.
Are we winning the fight againt AI-Driven Financial Crime?
Artificial intelligence will continue to transform both financial crime and fraud prevention. As AI tools become more accessible, fraudsters will develop increasingly convincing attacks, while financial institutions will continue investing in AI-powered detection, behavioural analytics, and automated compliance.
At the same time, regulation will continue to evolve. Frameworks such as the EU AI Act,DORA, and the growing role of AMLA demonstrate that European regulators increasingly recognize the importance ofgovernance, operational resilience, and trustworthy AI. Future regulatory developments will likely place even greater emphasis on AI governance, explainability, data quality, and cross-border cooperation.
Yet an important question remains: are these efforts enough?
Despite decades of increasingly stringent anti-money laundering regulation and international cooperation led by the Financial Action Task Force (FATF), the amount of criminal assets ultimately seized has remained relatively limited compared with the estimated scale of global financial crime. This raises a broader question about the effectiveness of existing approaches. If organizations continue relying primarily on reactive controls, can they realistically keep pace with criminals who are now leveraging AI to operate faster, cheaper, and at greater scale?
Despite more than three decades of increasingly stringent AML regulation and international standards, the recovery of criminal assets remains strikingly low. Europol estimates that only around 1.1% of criminal proceeds are ultimately confiscated in the EU, while the FATF continues to report low levels of asset recovery effectiveness across most jurisdictions. This raises an important question: are compliance frameworks keeping pace with increasingly sophisticated financial crime?
For compliance, risk, and fraud professionals, the challenge is therefore no longer simply implementing new technologies or complying with new regulations. It is about continuously questioning whether existing controls remain effective against rapidly evolving threats. The organizations that will be best prepared are those that treat AI not only as a technology investment, but as a strategic capability supported by strong governance, human expertise, and a willingness to continuously adapt.
