A Gartner research report found that financial criminals use sophisticated AI tools, making them more effective at disguising illicit activities to bypass traditional systems. With tactics that range from using fraudulent identities to creating deepfake videos, these criminals pose significant challenges to global financial institutions.
As a result, Anti Money Laundering (AML) regulations are constantly evolving and becoming more complex, posing compliance challenges for financial institutions. Non-compliance has grave consequences, with hefty AML fines of billions of dollars and reputational loss. The Nasdaq Verafin 2024 Global Financial Crime Report states that $3.1 trillion in illicit funds flowed through the global financial systems in 2023, totaling $485.6 billion in projected losses. These figures underscore the scale of the impact of these crimes, as these funds financed destructive crimes such as human and drug trafficking, and terrorist financing.
In this scenario, the Financial Action Task Force (FATF) recommendations help set global standards to combat AML. One of its key recommendations is for countries to assess risks and apply a risk-based approach (RBA) to money laundering and illicit financing. As countries devise policies based on this approach, it becomes imperative for financial institutions to comply. Advanced technologies like AI and big data analytics help these institutions overcome the limitations of traditional systems and stay ahead of criminals.
The Limitations of Traditional AML Systems
This industry generates massive amounts of data, posing a huge analysis challenge for traditional AML systems. Conventional systems work on a rule-based mechanism, often generating a high volume of false positives that overwhelm limited resources. In such scenarios, there is always the danger of threats slipping through. Also, these systems are not adaptable or scalable to cope with the increasing volume of transactions.
Tools such as big data analysis help overcome these challenges effectively, especially when combined with Artificial Intelligence (AI). More importantly, it facilitates accurate and timely insights, streamlines and automates compliance processes, and aids global collaboration among financial institutions and regulators to revolutionize Financial Crime Compliance (FCC) measures. As a result, we have smart, dynamic, more intelligent, and proactive systems.
Key Applications of Big Data in AML Programs
Financial institutions are increasingly using big data analytics not just to detect financial crimes, but to predict, prevent, and respond to it more effectively. Here are the three key areas where big data is transforming AML efforts:
- Enhanced detection
Real-time transaction monitoring is possible with AI and big data analysis to flag anomalies and ensure swift measures against potential threats. Data mining techniques uncover hidden links, while data visualization helps detect suspicious activity within complex transaction networks more intuitively. Together, they greatly enhance the analysis of Trade-Based Money Laundering (TBML), making it easier to identify intricate patterns and hidden relationships that extend across international borders. The net result is improved accuracy and efficiency with fewer false positives in detecting financial crimes, along with the added benefits of reduced investigation time and resources, and increased savings.
- Proactive risk management
Predictive risk analytics, a subset of big data analytics, helps financial institutions preempt and mitigate risks by analyzing a combination of historical and real-time data. The predictions help financial institutions plan for emerging threats and strategically allocate more resources to high-risk areas to combat them before those threats become real. Text mining and Natural Language Processing (NLP) help organizations analyze even unstructured data and uncover emerging threats with greater precision. Network analysis highlights the key activity hubs, entities, and activities, aiding this process.
This data analysis capability extends to Customer Due Diligence (CDD) activities, enhancing risk management significantly. Unstructured customer data from diverse sources is analyzed to verify customers and understand their behaviors. In this process, supervised and unsupervised Machine Learning (ML) models classify customers and group them accurately for robust risk profile assessments. This RBA approach also enhances customer experiences by reducing unwarranted disruptions to low-risk profiles.
- Streamlined compliance
Streamlined AML processes based on RBA, as recommended by FATF, enhance transparency and auditability for financial institutions. Also, it paves the way for robust evidence of AML compliance to regulatory authorities. Automated reporting and compliance help these institutions respond rapidly to potential threats, aiding customer retention, a key factor in this highly competitive sector. Advanced data analysis tools facilitate generating highly accurate Suspicious Activity Reports (SARs), generating alerts for high-risk transactions, and enabling compliance teams to investigate rapidly with the help of visualization tools. Due to their ability to scale effortlessly with high data volumes and quickly adapt to evolving AML threats and trends, these ML model-based tools bolster compliance.
Shaping Tomorrow’s AML Strategies with Big Data and AI
By harnessing the power of big data analysis and integrating it with AI technologies, financial institutions can transform their AML capabilities. Various tools powered by these technologies can enhance anomaly detection, risk management and compliance, helping institutions safeguard the integrity of the global financial system.
How Can Infosys BPM Help?
Infosys BPM’s Financial Crime Compliance offerings come with a holistic approach with solutions and services spanning the complete FCC value chain. We help our customers achieve measurable cost savings, operational efficiencies, and regulatory compliance.
