The Fight Against Money Laundering: Machine Learning, a Game Changer

Institutions require Anti-Money Laundering (AML) specialists, top-tier data science talent, and trustworthy data sources to fully reap the benefits of machine learning and advanced analytics in the battle against money laundering.

Money laundering and other forms of financial crime are increasing in frequency and sophistication around the world.

This has prompted a strong reaction from financial institutions, which are spending billions annually to strengthen their safeguards against financial crime.

As regulators impose harsher penalties, the associated costs of maintaining compliance continue to rise. However, compliance, monitoring, and risk organisations have struggled to stay ahead of money launderers due to their reliance on the traditional rule- and scenario-based approaches.

In recent years, machine learning (ML) advancements have enabled financial institutions to greatly improve their AML programmes, especially the transaction monitoring component.

Financial institutions can get one of the most immediate and substantial gains from anti-money laundering initiatives through transaction monitoring, particularly through the combination of machine learning with other advanced algorithms.

Many financial institutions today rely on rule and scenario-based systems or rudimentary statistical methods to keep an eye on their customers’ transactions.

Industry warning signs, standard statistical indicators, and professional judgment are the primary sources of inspiration for these cutoffs and limits.

However, the rules frequently fail to reflect the most recent trends in money-laundering behaviour. Machine learning models, on the other hand, use more detailed, behavior-indicative data to develop sophisticated algorithms.

In addition, they are more adaptable, meaning they can swiftly adjust to new trends and continue improving over time.

One of the world’s largest financial institutions was able to improve the detection of potentially fraudulent behaviour by up to forty percent and increase its productivity by up to thirty percent by switching from the rule and scenario-based tools to machine learning models.

When designing anti-money laundering transaction monitoring approaches, financial institutions must consider data, methodology, monitoring, and execution. ML projects fail due to lack of stakeholder buy-in from data, technology, line-of-business, and compliance teams.

To coordinate on vision, make architectural design decisions, and analyze trade-offs for all processes, stakeholders must be involved from the start.

This ensures that business-as-usual and regulatory actions are considered. Gathering diverse perspectives and converging on ML’s vision, design, and trade-offs enhances company transparency and reduces risks.

Efficiency follows effectiveness because the value proposition is to better capture risk and generate high-quality alerts for downstream inquiry.

New technologies are only part of the picture when it comes to transformations. To successfully adapt to the realities of today’s technological systems, financial institutions must be deliberate in their approach (for example, by concentrating on the transition strategy rather than the destination itself) and adopt a collaborative mindset that marries technological and business objectives.

All technological transformations encounter obstacles. New technology might pose risks, and workers often resist them. To enhance stakeholder confidence during the pilot phase and reduce risks, a financial institution might run existing rule- and scenario-based risk scenarios in parallel with ML-based scenarios.

The organization may choose initiatives that use existing platforms that personnel are familiar with and integrate new components one at a time to increase adoption and reduce risk. Financial institutions should begin with projects with high potential profits and little risk.

MRM teams should improve the model risk management framework, expand capabilities to work closely with the data science team in the model development and validation process, shape validation standards and policies to address the specific risks associated with ML models, such as bias detection and explainability, and define precise performance and monitoring requirements to integrate ML solutions into the transaction monitoring framework.

MRM teams should automate this performance and monitor tests. Machine learning is the future for anti-money laundering.

Historically, financial institutions have lagged criminals into the race to combat money laundering. Now is the time for financial institutions to make a difference.

Using advanced analytics methods, such as machine learning with network analytics, has the potential to greatly enhance transaction monitoring by lowering the false-negative and false-positive rates and providing higher-quality signals to anti-money-laundering investigators further down the line.

Most financial institutions will need to devote a substantial amount of time and money to the upgrade. Institutions will need to cultivate a pool of talent, develop trustworthy data sources, and capitalize on the insight of specialists in the field to reap the full advantage. A difficult task, yet one that must be done because of the consequences.

Adetoyese Adepoju is an Information Technology Management Specialist with proven work experience across tech, banking, digital payments, compliance, and risk management. He is a graduate student at the Cardiff Metropolitan University and is currently leading the IT Digital Transformation Team in International Digital Financial Services (IDFS – https://international-dfs.co.uk/ ) Ltd with a charge to re-engineer operational resilience and deliver memorable experiences across several customer channels and touch-points.