Introduction

Financial crime is changing faster than ever. With digital payments, cryptocurrencies, and global transactions becoming the norm, criminals have new ways to hide illicit funds. Traditional monitoring systems, which often rely on rigid rules, are struggling to keep up. This is where artificial intelligence (AI) comes in. More than just a buzzword, AI is starting to transform how financial institutions and regulators detect, investigate, and prevent financial crime.

The scale of financial crime is staggering. The International Monetary Fund estimates that between 2% and 5% of global GDP roughly 800 billion to 2 trillion US dollars is laundered every year (IMF, 2018). Despite massive investments in compliance, financial institutions continue to face record-breaking penalties. From 2008 to 2022, over 55 billion US dollars i

n anti-money laundering (AML) fines were issued worldwide, with Danske Bank, Santander, and even Binance among those penalized (Financial Times, 2022).

Criminals are taking advantage of online platforms, fast payment systems, and cryptocurrencies to move money across borders quickly. Detecting these schemes manually is time-consuming, expensive, and often ineffective.

How AI Helps Fight Financial Crime

AI offers something traditional systems cannot: the ability to process huge amounts of structured and unstructured data quickly and intelligently. Machine learning can spot unusual transaction patterns, such as deposits designed to avoid reporting thresholds. Natural language processing can scan contracts, emails, and even news reports for red flags. Graph analysis can uncover hidden links between people, companies, and accounts that might otherwise go unnoticed (EY, 2023).

Real-world examples are already emerging. Banks are using AI-powered AML systems to monitor millions of daily transactions more effectively, cutting down on false alarms while catching complex layering schemes. Payment providers apply AI to detect unusual spending in real time, protecting consumers from fraud. Even in the crypto space, blockchain analytics firms like Chainalysis and Elliptic are tracing illicit flows tied to ransomware, drug trafficking, and terrorist financing (World Economic Forum, 2022).

Innovation in Collaboration

One of the biggest challenges in financial crime prevention is sharing information across borders and institutions without violating privacy. A promising solution is federated learning. This approach allows banks to share AI models rather than raw data, making it possible to learn from broader patterns while protecting sensitive customer information. Already successful in healthcare, federated learning has shown up to 75% improvements in detecting suspicious activities when applied to financial crime (Shiffman et al., 2022).

National regulators are also beginning to embrace AI. In Malaysia, Bank Negara Malaysia has piloted AI tools to detect anomalies in money services businesses, improving the efficiency of supervisory work (BNM, 2025).

Risks and Challenges

Of course, AI is not without risks. Poorly trained models may reinforce bias, unfairly flagging certain groups. Large-scale use of personal data raises ethical and regulatory concerns. Regulators now expect AI systems to be explainable and auditable, not “black boxes” that cannot justify their outputs (EY, 2023).

At the same time, criminals are using AI themselves. Deepfakes, synthetic identities, and AI-generated phishing emails are already complicating the fight against fraud and money laundering (BNM, 2025). This makes it even more important for institutions to use AI responsibly and ethically.

The Road Ahead

The future of financial crime prevention is not about replacing humans with machines but about combining their strengths. AI can bring speed and scale, while investigators provide judgment, ethics, and context. Surveys show that more than 70% of financial institutions in Malaysia already use AI, with generative AI adoption expected to rise sharply in the next one to two years (BNM, 2025). Globally, AI in anti-fraud programs is expected to more than double in the coming years (ACFE, 2022).

AI is no longer optional in the fight against financial crime. With the right governance and collaboration, it can transform compliance from a costly burden into a proactive defense, protecting not just institutions but society.

References

1. ACFE. (2022). Anti-Fraud Technology Benchmarking Report. Association of Certified Fraud Examiners.
2. Bank Negara Malaysia (BNM). (2025). Artificial Intelligence in the Malaysian Financial Sector: Discussion Paper. Bank Negara Malaysia.
3. EY. (2023). AI-enabled anti-money laundering discussion paper. Ernst & Young.
4. Financial Times. (2022). Danske Bank and global AML fines coverage. Financial Times.
5. International Monetary Fund (IMF). (2018). Anti-money laundering and economic stability. IMF Finance & Development.
6. Shiffman, G., Liposky, S., & Hamilton, R. (2022). Artificial Intelligence and the revolution in financial crimes compliance. RiskIntell.
7. World Economic Forum. (2022). How trade-based money laundering impacts world finances. World Economic Forum.