The Evolution of Anti-Money Laundering (AML) in the AI Era

   

The Stakes of Financial Crime Prevention

The battle against financial crimes is critical, supporting efforts to combat organized fraud, human trafficking, and drug trade. This fight demands that investigators be increasingly agile, efficient, and thorough in their approach. The consequences are significant, often a matter of life and death.

Emerging Skills in AML

The AML field is evolving, with key skills now including:

• Proficiency in Generative AI (GenAI)
• Understanding of Large Language Models (LLMs)
• Application of Machine Learning (ML) in fraud detection and transaction monitoring

These technological advancements require:

• Human oversight
• Model risk management
• Strong ethical foundations

The integration of these skills is creating new roles that leverage human expertise in:

• Enhanced due diligence investigations
• AML policy development
• Cybersecurity threat hunting

Current Limitations and Proper Use of AI in AML

At present, AI should not be relied upon for tasks requiring judgment or decision-making. Instead, it serves best as:

• A rewording and summarization tool
• A technology assistant to enhance work output

It's crucial to understand that AI does not replace AML professionals. Rather, it should be viewed as a resource to augment human capabilities, providing summarizations and approximate information.

Challenges with AI: Hallucinations and Generalization

AI hallucinations remain a significant concern. Key points to remember:

• AI may combine factual and non-factual information
• It aims to provide desired responses, not necessarily accurate ones
• AI doesn't distinguish between truth and falsehood
• GenAI generalizes information, potentially leading to inaccuracies with highly specific queries

Essential Skills for the AI-Augmented AML Professional

1. Reviewing and validating AI responses and conclusions
2. Identifying mistakes, especially in compliance, investigations, and risk management
3. Understanding AI's strengths, weaknesses, and information processing methods
4. Recognizing and mitigating inherent biases in AI training
5. Adhering to a "verify before trust" approach

Getting Involved in AI within AML

To engage with the growing role of AI in AML:

1. Pursue introductory coding classes or self-paced certificate courses (including free resources)
2. Utilize reputable AI tools like ChatGPT, Copilot, and Dolly
   • Remember: While these tools are legitimate, they are still AI and require verification
   • GenAI aims to provide preferred answers, not necessarily correct ones

By embracing these technologies responsibly, AML professionals can enhance their capabilities and adapt to the evolving landscape of financial crime prevention.

How do you think GenAI will affect future AML jobs? Big, small or insignificant? Why? 

Where do you think GenAI could help in the AML space?

3 ways generative AI can help with financial crime investigations:

 

1. Pattern recognition and anomaly detection: Generative AI models can analyze vast amounts of financial transaction data to identify unusual patterns or anomalies that may indicate fraudulent activity. By learning normal transaction behaviors, the AI can flag deviations for further investigation.

2. Natural language processing of documents: AI can rapidly process and extract key information from large volumes of unstructured text like emails, chat logs, and financial documents. This can help investigators quickly sift through data to find relevant evidence and connections.

3. Predictive modeling of criminal networks: By analyzing historical data on known financial crimes, generative AI can create models to predict potential criminal networks or forecast likely future criminal activities. This allows investigators to take a more proactive approach.

(A Couple) Technology Mistakes in FinCrime and how to avoid them

Over the past decade, technology in financial crime prevention has evolved significantly. When properly utilized, it serves as a powerful ally for financial institutions in their battle against fraudsters and in safeguarding customer assets. However, if misused, this same technology can become a destructive force, enabling theft, embezzlement, and devastating personal lives.

Modern innovations like sophisticated behavioral analysis, AI, machine learning, and robotics offer valuable tools for this purpose. When implemented effectively, these technologies can enhance the detection of suspicious activities, minimize false alarms, and accelerate response times.

The trick is understanding how to use this technology, when and where. It's onerous, it's expensive and as transactional volumes increase, so do the potentials for mistakes.

You Can't See Me: Illicit actors establish numerous fraudulent accounts through shell companies to facilitate the laundering of their unlawfully acquired funds. The proliferation of digital banking has intensified this issue, allowing for the rapid global transfer of immense amounts of money in near real-time.

Developing a secure technological infrastructure for the transparent and trackable sharing of customer data, disseminated via digital pathways under the control of sanctioned users, offers a solution to this challenge. This system, akin to a digital passport, can foster trust among participants.

You can have your privacy, but security too? How do global governments and regulatory bodies balance preserving their citizens' privacy with implementing sufficient oversight and transparency to safeguard against criminal activities? What's the optimal approach to simultaneously uphold citizens' rights and ensure their protection? There needs to be an innovative structure of working to combat these kinds of illegal activities via a collective, collaborative effort by both the public and private sector.

Enhancing protocols for financial institutions to verify suspicious activities would be a crucial first step. Ultimately, this issue revolves around trust: individuals must have faith in their governments and banks regarding the appropriate use of their information, while banks and regulators need increased certainty about account holders' identities.

Missing evidence is not the same as missed evidence or evidence that is lost.

  

Missing evidence is not the same as missed evidence or evidence that is lost. When I speak of missed evidence it relates to evidence that could have been found had the right avenues been followed down.

One notable case is the O.J. Simpson murder trial from 1995. There was delayed collection of evidence wherein police waited hours before thoroughly searching the crime scene, potentially allowing evidence to be contaminated or lost. Among other errors there was overlooked evidence: A bloody fingerprint on a gate near the crime scene was not properly documented or analyzed. These investigative errors were exploited by Simpson's defense team to cast doubt on the prosecution's case, potentially contributing to his acquittal in the criminal trial.

This could have been avoided by:

• Following proper evidence collection procedures and handling,
• Following proper documentation procedures in criminal investigations

Instead, the playing field was leveled through time and age.

Thankfully, lessons were learned and have significantly influenced modern forensic science and criminal investigation procedures, leading to more rigorous standards and practices in evidence handling across the legal system.

Blueprint to avoid the 5 biggest mistakes in FinCrimes investigations

I love financial crimes and digital investigations. My aim is to guide other FinCrime Investigators to be the best they can be. I hold certifications in AML, Financial Crimes & Digital Investigations. I'm introducing a new and completely FREE min-course on improving your Financial Crimes Investigations.  You can see more here:  Better Financial Crimes Investigations