Leveraging AI to combat money laundering | HCLTech
Digital Process Operations

Leveraging AI to combat money laundering

As technology evolves, AI will undoubtedly remain a pivotal ally in the ongoing fight against financial crime, safeguarding the integrity of the global financial services industry.
2 minutes 30 seconds read
Jesper Kristensen


Jesper Kristensen
Associate Vice President, Digital Process Operations
2 minutes 30 seconds read

As per the United Nations Office on Drugs and Crime (UNODC), around 2% to 5% of the global GDP, equivalent to over $800B, is laundered annually.

In fact, nearly 70% of financial institutions reported having lost at least $500,000 to fraud in 2022 alone. In the same year, five of the world’s leading banks were collectively fined for more than $2.2B for various financial regulatory infractions.

Traditional approaches to investigating money laundering hinge on manual verification and human analysis. As a result, they are cost-intensive and, despite great effort, frequently prove inadequate when confronted with the intricate and advanced fraud schemes of today. These shortcomings make it imperative to explore artificial intelligence (AI) and machine learning (ML) for revolutionizing financial fraud investigations.

Indeed, the financial industry is exploring the use of AI and ML for anti-money laundering (AML) strategies. Here are some areas of AML operations that highlight the transformative potential of AI and ML in reshaping the landscape of money laundering schemes:

Know Your Customer (KYC) and Customer Due Diligence (CDD)

KYC and CDD are leveraged to collect information, verify the gathered data and validate a customer's identity. This compliance step is highly prone to fraud, as many criminals sneak past by providing unauthentic information.

AI can improve KYC and mitigate potential fraud by enabling automated data extraction, document verification, risk assessment and continuous monitoring. It helps assess large stacks of data rather easily to detect anomalies in customer behavior and identify high-risk profiles.

Transaction monitoring and sanction screening

Transaction monitoring, though a crucial step in AML compliance, introduces an unending list of challenges. Regulatory variations, false positives, customization of rules and static rule-based transaction monitoring systems are just a few of the roadblocks. Fintechs and neo-banks report several operational glitches, making them more vulnerable to financial crimes.

AI-powered transaction monitoring systems offer a range of benefits over aging static models. They support real-time analysis of data patterns and suspicious activities. AI can also improve screening by identifying false positives more accurately.

Suspicious Activity Reporting (SAR)

AI integration can help compliance analysts open suspicious activity reports faster and more efficiently. Through automation, SAR can be relatively simplified and accurate. It can identify potential fraud and money laundering activities for banks.

Risk-based AML compliance

A risk-based approach is a bank’s best bet against money laundering. It helps financial institutions mobilize their resources across high-risk customers and transactions. By optimizing resources, AI improves the accuracy, efficiency and reliability of AML operations.

Benefits of AI integration

An AML operating model integrated with AI offers several benefits, including:

  1. Improved accuracy: The amalgamation of AML and AI amplifies accuracy, elevating the ability to identify suspicious activities and potential risks, thereby fortifying the financial ecosystem against illicit transactions.
  2. Enhanced efficiency: Through seamless integration, the AML-AI combination optimizes operational processes, streamlining data analysis, pattern recognition and decision-making. This leads to swifter and more effective responses to evolving threats.
  3. Continuous learning: The symbiotic relationship fosters a culture of perpetual learning, where AI continuously adapts to new patterns and trends, refining its predictive capabilities. This dynamic adaptation ensures a proactive stance against emerging risks.
  4. Reduced costs: By harnessing the power of AI, the AML framework achieves cost efficiencies through automated data processing, reducing manual intervention and trimming resource expenditures, ultimately contributing to enhanced operational economics.
  5. Improved customer experience: AML and AI make things easier and faster for customers. They can check transactions quickly, reduce mistakes and solve problems faster. This makes customers more confident in using financial institutions.

Google Cloud’s latest innovation – an AML tool equipped with AI and ML – is one of the best examples of modern technologies offering these advantages and making banks future-ready. Leading banks worldwide, such as HSBC, Lunar and Bano Bradesco, are exploring this and similar innovations to enhance AML compliance. And in the future, we can expect these advancements to become integral components within many financial institutions.

Synergizing AI and human insight


It's essential to balance AI deployment and human expertise, as AML professionals play a crucial role in guiding and supervising AI systems


As technology evolves, AI will undoubtedly remain a pivotal ally in the ongoing fight against financial crime, safeguarding the integrity of the global industry. However, it's essential to balance AI deployment and human expertise, as AML professionals play a crucial role in guiding and supervising AI systems.

At HCLTech, we synergize advanced AI technology and human AML expertise to fortify financial cybersecurity and the banking, financial services and insurance (BFSI) industry's defenses against crime in general. Our AI solutions swiftly identify anomalies, while AML professionals provide contextual insight and regulatory adherence. Through collaborative AI-human efforts, we empower efficient and ethical financial crime prevention.

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