The intersection of artificial intelligence (AI) and financial crime is transforming illicit activities and the methods to combat them. AI technologies provide sophisticated tools for criminals while also offering unprecedented opportunities for financial institutions and regulatory bodies to enhance defenses, detect suspicious activities, and prevent financial crime.

This report examines the dual-edged impact of AI on financial crime, exploring escalating threats, emerging typologies of AI-driven illicit activities, challenges faced by stakeholders, and the potential of AI in enhancing fraud detection and prevention. It also addresses ethical considerations and regulatory adaptations needed in this evolving environment.

Proactive and collaborative strategies are essential to harness AI for good while mitigating its misuse in financial crime.

The Dual-Edged Sword of AI in Financial Crime

AI's rapid evolution has permeated the financial sector, enhancing customer experiences and operational efficiency. However, sophisticated AI tools can be exploited for financial crime, with criminals leveraging AI for advanced attacks, automated fraudulent schemes, and evasion of traditional detection methods.

Conversely, AI offers a powerful arsenal for financial institutions and regulatory bodies to strengthen defenses, identify illicit activities, and safeguard the financial system. This report analyzes this interplay, exploring AI as a double-edged sword in fraud, money laundering, and other illicit financial activities. It examines recent trends, emerging threats, technological solutions, ethical dilemmas, and regulatory responses to provide a holistic understanding of this evolving landscape.

The Rise of AI-Enabled Financial Crime: Trends and Statistics

AI is significantly transforming the financial crime landscape, increasing both the sophistication and scale of illicit activities. In November 2024, FinCEN highlighted an increase in suspicious activity reports involving deepfake media in fraud schemes. DPRK-linked hackers stole approximately USD 800 million in 2024, with potential for AI to scale such operations.

Despite these concerns, the Napier AI / AML Index 2024-2025 estimates global economies could save $3.13 trillion annually by using AI to detect and prevent money laundering and terrorist financing.

Global cross-border payment volumes are projected to exceed $200 trillion by 2025, increasing the complexity of AML compliance. Global cybercrime damage is expected to reach $10.5 trillion annually by 2025, with AI-driven phishing and ransomware as contributing factors.

A 2023 PwC survey indicated 62% of financial institutions used AI/ML for AML, expected to rise to 90% in 2025. AuthenticID found identity fraud increased to 2.1% of transactions in 2024, linked to AI adoption by cybercriminals.

BioCatch found over half of financial institutions lost between $5 million and $25 million to AI-based threats in 2023. AI systems are increasingly gaining access to essential tools, and AI models are being programmed to optimize specific goals. Europol warned in 2025 that AI is turbocharging organized crime.

Table 1: Recent Statistics on AI-Enabled Financial Crime (2024-2025)

Emerging Typologies of AI-Driven Financial Crime

Emerging threats include deepfakes and synthetic media fraud, where AI creates realistic fake videos and audio for impersonation and extortion.

Criminals are increasingly leveraging AI to create highly realistic fake videos and audio recordings. This technology allows them to impersonate executives, often in what is termed CEO fraud, where a deepfake of a company's leader is used to convince employees to transfer funds to fraudulent accounts.

Another significant emerging typology is synthetic identity fraud. This involves criminals using AI to combine legitimate and fake personal information to create entirely new, fictitious identities.

These synthetic identities are then used to open fraudulent bank, credit card, cryptocurrency, and other financial accounts. What makes this type of fraud particularly challenging is that these identities often have a seemingly legitimate credit history built up gradually over time, making them difficult to distinguish from genuine individuals. The gradual establishment of creditworthiness allows fraudsters to potentially conduct substantial financial crimes before their activities are detected, as these identities can pass many traditional fraud detection checks.

Automated and enhanced cyberattacks use AI to automate phishing campaigns and adapt malware. This could involve the use of AI-powered synthetic ID generators to create the fake identities needed to open numerous accounts, as well as the automation of cryptocurrency account creation, which can significantly accelerate the velocity at which illicit funds can be moved and obscured.

Challenges Faced by Financial Institutions and Regulatory Bodies

The rise of AI-driven financial crime presents major challenges for both financial institutions and regulatory bodies. One of the most pressing issues is keeping up with the rapid pace of technological change. Criminals are quick to adopt AI tools to enhance their illicit activities, often outpacing the development of countermeasures. This creates a constant arms race, where institutions struggle to defend against emerging threats that traditional security measures may not catch.

The realism of AI-generated content introduces yet another problem—distinguishing genuine customer interactions from fraud. Deepfakes and synthetic media can convincingly mimic voices and faces, making it difficult even for trained professionals to spot impersonations. Traditional verification methods, like voice or video ID checks, are becoming less reliable, pushing the need for stronger, AI-based authentication tools.

A significant hurdle is the lack of skilled professionals who understand both AI technologies and financial crime typologies. This talent gap limits the effective deployment of advanced security solutions. Bridging it will require targeted training and the development of interdisciplinary expertise within both the public and private sectors.

Regulatory uncertainty also adds complexity. Many jurisdictions are still developing AI-related policies, and the global landscape remains fragmented. Without consistent frameworks, institutions face difficulties implementing AI tools while staying compliant. Clearer, more unified regulations are essential to promote safe and responsible innovation.

Opportunities: Harnessing AI for Enhanced Fraud Detection and Prevention

Despite the significant challenges posed by AI-driven financial crime, artificial intelligence also presents unprecedented opportunities to enhance fraud detection, prevention, and investigation within the financial sector.

AI offers opportunities for enhanced accuracy in:

• Detecting suspicious activity

• Reduction of false positives

• Real-time transaction monitoring

• AML compliance using AI

• Predictive analytics for proactive risk identification

• Improved customer due diligence (CDD)

• Improved Know Your Customer (KYC) processes

• AI-powered text analysis (NLP)

• Collaboration and information sharing

• Automation of reporting and compliance tasks

• Anomaly detection beyond financial transactions

Adapting Regulatory Frameworks to the Age of AI in Financial Crime

Regulatory frameworks are evolving to address AI-driven financial crime. Some jurisdictions are introducing AI-specific regulations, focusing on transparency, explainability, bias mitigation, fairness, data governance, privacy, risk management, and oversight.

Future trends include outcome-based and risk-based regulations, international collaboration and harmonization of AI regulations. AI will create new roles requiring different skill sets, focusing on the development, maintenance, and oversight of AI systems. Financial institutions need to consider the workforce implications of AI adoption and invest in upskilling and reskilling their employees to adapt to the changing demands of an AI-driven compliance environment.

Given the transnational nature of financial crime, especially in the digital age, increased international collaboration and harmonization of AI regulations in the financial sector are also expected. Global efforts will be necessary to address the cross-border challenges posed by AI-driven illicit activities, and financial institutions operating internationally should prepare for potential convergence in regulatory requirements.

Successful Applications of AI in Preventing and Mitigating Financial Crime

Despite the challenges, numerous financial institutions and organizations are already successfully leveraging artificial intelligence to enhance their capabilities in preventing and mitigating financial crime.

Arva AI's platform offering allows companies the ability to manage alerts and exceptions at a fraction of the time using advanced AI tooling that can be leveraged for KYB, Transaction Monitoring and Screening.

• Arva AI's Platform offering improves and scales alert resolution and exception management by allowing AI agents to automate manual work for faster and deeper financial crime reviews.

• HSBC’s Dynamic Risk Assessment system finds more instances of financial crime and reduces false positives.

• Appgate’s Detect Transaction Anomaly (DTA) prevented significant fraud and reduced false alerts.

• NatWest uses AI for scam classification, payment detection, beneficiary profiling, and rule-performance management.

• ING uses AI for anomaly detection. AI is used for KYC/CDD automation and reduction in false positives.

• DARPA’s A3ML program aims to develop algorithms for automated money laundering detection.

Table 2: Successful Applications of AI in Financial Crime Prevention

The Future Landscape: Anticipating AI-Driven Financial Crime and Countermeasures

The future of financial crime prevention in the age of artificial intelligence will depend on the ongoing innovation in both AI-driven criminal tactics and the corresponding countermeasures. Enhanced collaboration and information sharing between all stakeholders, coupled with a commitment to developing a skilled workforce capable of understanding and addressing these evolving threats, will be essential in safeguarding the integrity of the financial system and protecting individuals and institutions from the harmful impacts of financial crime. As AI continues to evolve, so too must our strategies and our resolve in harnessing its power for good while mitigating its potential for misuse in the ongoing fight against illicit finance.

Future trends include:

• Increased sophistication and scale of AI-enabled financial crime

• Autonomous AI-driven attacks

• Exploitation of DeFi platforms

• Advancements in AI-powered countermeasures

AI presents both opportunities and challenges in financial crime. Financial institutions must leverage AI for fraud detection and prevention while addressing ethical implications and risks. Regulatory bodies must adapt frameworks to foster innovation and ensure transparency, fairness, and accountability. Enhanced collaboration, information sharing, and developing a skilled workforce are essential for safeguarding the financial system and mitigating financial crime.