Money Laundering

Role of AI and Machine Learning in Combating Money Laundering

AI and ML transformed AML by detecting suspicious patterns, automating compliance, and effectively combating financial crimes like fraud and money laundering.

Dvij Sutariya

— 5 min read
Role of AI and Machine Learning in Combating Money Laundering

Money laundering is a chronic concern that allows the commission of crimes such as drug trafficking, terrorism, and even corruption. As money launderers develop newer and more sophisticated methods, they tend to outsmart the most traditional techniques used to track the movements of laundered money.

Therefore, the role of Artificial Intelligence (AI) and Machine Learning (ML) has grown even more vital in combating money laundering. These technologies are applying new levels of efficiency, effectiveness, and adaptability to Anti-Money Laundering (AML) measures while revolutionizing the overall game against financial crimes.

The Evolution of AML: From Manual to Intelligent Systems

Old-fashioned AML measures relied on manual processes and rule-based systems in monitoring transactions and detecting suspicious activity. These measures were somehow effective but not adaptable to new money laundering methods. The main challenges included:

  • Many False Positives: Most manual systems raised false positives and wasted time and resources.
  • Not Scalable: Traditional monitoring systems get overwhelmed by the exponents of increasing digital transactions.
  • Poor Adaptability: Static rules missed many emerging money laundering patterns.

These AML compliance shortcomings are being addressed using artificial intelligence and machine learning, with processes automated, learned from data, and changed by incorporating new threats.

How AI and ML Work in AML

Different features contribute to improved AML processes through AI and its sibling, namely ML.

1. Continuous Transaction Surveillance

These technologies are competent enough to follow high-volume real-time transactions and pinpoint irregularities in money laundering activities.

A case example is where the AI alerts if an individual keeps making international wire transfers just below the regulatory threshold, a practice known as "structuring."

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Real-Life Example: JPMorgan Chase uses AI systems to monitor well above $5 trillion in daily transactions, thus vastly reducing human observation requirements.

2. Recognition of Pattern

Machine Learning algorithms best require pattern recognition in massive datasets, enabling someone to detect some suspicious activities that other conventional systems might miss.

For instance, routing multiple transactions through different jurisdictions to camouflage the origins of the money could be detected through layering techniques.

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Real-life Example: HSBC uses ML models to detect anomalies in customer behavior, thus ensuring the advanced detection of possible laundering schemes.

3. Augmented CDD - Customer Due Diligence

AI automates and improves the KYC process, resulting in shorter onboarding time and higher visibility of high-risk clients. For instance:

For example, AI cross-checks a client's identity against public and private database records to identify red flags, like adverse media reports or sanctions lists.
Real Live Case Scenario:

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Real-life Example: Standard Chartered had developed and integrated artificial intelligence in their systems, reducing their CDD review duration by up to 40%.

4. Predictive Analytics

Predictive analytics has been made possible through Machine Learning, which purports to predict likely laundering activities based on past data.

For example, it may predict strange account behavior by having a trend analysis of behavior, including a spike in cash deposits.

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Real-life Example: ING Group uses predictive models to evaluate the absence of its money laundering business, thus allowing intervention resulting from this assessment to be proactive.

Applications of AI and ML in AML

1. Automation of Suspicious Activity Reports (SARs)

Filing SARs is compulsory under regulations. In actual practice, it consumes time and is subject to human error. AI can automate the following activities involved in SAR filing:

  • Identification of suspicious transactions
  • Categorization of risk levels
  • Creation of detailed, accurate reports.
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Example: AI cuts the workload of compliance officers by 50%, allowing them to concentrate on critical cases.

2. Resolution of Entities

Entity resolution is matching different disparate data points to represent a single profile. Criminal users generally adopt false identities or maintain several accounts to evade detection. AI has made it possible through:

  • Analyzing data from its multiple sources
  • Matching mismatch of names, addresses, and account details.
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Example: One of the Top US Banks used AI to link 20 accounts, attributing them to a single entity laundering cartel money.

3. Fraud detection in Cryptocurrencies

Cryptocurrencies present their own set of specific anti-money laundering challenges because of their pseudonymous nature.

  • AI and ML help track transactions across blockchain networks and identify wallet addresses linked to illicit activities.
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Example: AI flags as suspicious lots of funds going through numerous wallets quickly (a laundering trick called "peeling").

4. Trade-In Money Laundering (TBML).

TBML is when an individual using trade hides the movement of illicit funds. AI assists in TBML in:

  • Scrutiny and examination of trade invoices and shipping records identify differences in price, quantity, or documents involved.
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Example: AI highlighted suspicious over-invoicing in a series of transactions associated with a shell company in a TBML plot worth $100 million.

Real-World Examples of AI and ML in AML

Case of the Danske Bank: It was found that more than $230 billion in dubious transactions went through the Estonian branch of Danske Bank. To prevent problems like that, the bank has introduced AI systems to analyze transaction patterns, thus adding armor in the form of AML compliance.

Fraud Detection Brought by PayPal: PayPal employs advanced ML models to detect fraudulent transactions and chargeback cases and comply with international AML regulations.

AI System of FINRA: The Financial Industry Regulatory Authority (FINRA) now uses AI-based systems for trade monitoring and insider trading detection to protect the integrity of financial markets.

Benefits of AI and ML in AML

  1. Efficacy & Speed: Process automation includes monitoring transactions, filing SARs, and conducting CDD due to faster detection and response times.
  2. Scale: AI climbs with increasing transaction numbers and millions of data points without losing accuracy.
  3. Flexibility: Machine Learning algorithms change as criminals change their laundering schemes; the systems are, therefore, always one step ahead.
  4. Cost-Effectiveness: Cost reduction for compliance procedures where labor has been taken automated.

Challenges in Implementing AI and ML for AML

1. Data Quality for Integration: AI requires clean, structured data; however, disaggregation and incompleteness lead to inaccuracies.

  • Solution: financial institutions must invest in good-quality data management and integration tools.

2. High Up-front Costs: The initial cost of AI system development through technology and training may be heavy.

  • Solution: Initial cost can be understood as being compensated by savings for the longer term, along with improved efficacy.

3. Privacy Concerns: Many sensitive personal customer data processed in AML systems raise privacy issues.

  • Solution: Institutions must guarantee that all operations comply with adequate data protection legislation like GDPR.

4. Regulatory Resistance: Some regulators are apprehensive regarding AI, given the insufficient transparency of algorithms' operations.

  • Solution: Enlightening AI (XAI) that provides clear instructions behind decisions would answer this.

Future of AI and ML in AML

Trends that will continue to fuel the growth of AI and ML in AML include the leverage of advancing technology and increasing sophistication in financial crime. Some of the key trends are:

  1. Integration with Blockchain: Instead, AI is expected to enhance blockchain analytics and allow significantly improved tracking of transactions across decentralized networks.
  2. Federated Learning: This method will empower financial institutions to work together on AI models without compromising their proprietary, sensitive data, thereby adding value to the mutual advantage in AML efficacy.
  3. Explainable AI (XAI): As demands from regulators increase for transparent operations, XAI will take the lead by ensuring AI decisions are understandable and auditable.
  4. Collaboration Across Borders: AI-powered systems will facilitate cross-border information sharing to enhance the growing efforts made by jurisdictions worldwide to combat these crimes.

For financial institutions, AI and Machine Learning defined the entire scale of work that would otherwise be impossible: keeping pace with ever-more-sophisticated money laundering schemes.

By automating processes, enhancing detection, and adapting to emerging threats, they are a systematic approach to giving a solid response to financial crime. Their success, however, depends on resolving challenges related to data quality, regulatory acceptance, and cost barriers.

As AI evolves, the contours of its application to AML will broaden, helping usher in a much safer and transparent financial environment around the world. Combining human expertise and AI innovation is the key to winning the fight against money laundering.

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