How Financial Institutions Can Use AI to Enhance Due Diligence
Artificial Intelligence is taking the world by storm, but how is it helping financial institutions meet their compliance challenges? One in particular revolves around the financial sector’s requirement to operate within Anti-Money Laundering (AML) regulations. These regulations are based on the Financial Action Task Force (FATF) International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation (often referred to as the FATF 40 Recommendations) as interpreted by the EU Anti-Money Laundering Directive (3rd and 4th Anti-Money Laundering Directives or 4AMLD).
4AMLD has set standards for identifying and verifying business clients and beneficial owners, applying customer due diligence and enhanced due diligence when higher risk situations have been assessed. FATF guidance encourages firms to take a risk-based approach to countering money laundering and terrorist financing.
Increasingly, banks are being challenged by customers, competitors and the regulators to detect and properly manage risk. At the same time, the financial and reputational penalties for getting controls wrong are also rising to record levels.
Artificial Intelligence (AI) takes KYC (Know Your Customer) and AML compliance to the next level. AI isn’t just a technology — it is a collection of related technologies offering the potential to automate workflows and quickly analyze large volumes and different types of data. Some of the implied benefits of using Artificial Intelligence in KYC and AML are examined below.
AI-based link analysis is a set of techniques for exploring associations among large numbers of objects of different types. These methods are crucial in assisting AML investigators in comprehending complex webs of evidence and drawing conclusions that are not apparent from any single piece of information. Link analysis is employed to augment traditional KYC and KYCC (Know Your Customer’s Customers) processes, creating multilayer and hierarchical networks for relationships between customers, their organizations, suppliers and business partners.
These methods are equally useful for creating variables that can be combined with structured data sources to improve automated decision-making processes. Typically, linkage data is modelled as a graph, with nodes representing entities of interest and links representing relationships or transactions along with dubious jurisdictions, companies and UBOs.
Unstructured Data Analysis
AI in KYC relies more on Natural Language Processing (NLP) and supervised machine learning (ML) techniques. Each of these technologies has specific uses and NLP, in particular, is starting to come into widespread use in helping to analyze unstructured content such as adverse media. Together with machine learning, NLP-based AI can “read” content and perform a range of tasks including extracting metadata, identifying entities that are referred to, and “understanding” the intent or purpose of specific parts of the document.
In most cases, money launderers hide their actions through a series of steps that make it look like funds that came from illegal or unethical sources are earned legitimately. Most of the major banks around the globe are shifting from rule-based software systems to artificial intelligence-based systems, which are more robust and adept to money laundering patterns. Recently, RegTech players have developed Anti-Financial Crime Solutions using unsupervised Bayesian learning techniques to understand customer behavior and furthering investigations and possible SAR (Suspicious Activity Report) filings.
AI can also be used in generating documents, reports, audit trails and notifications. For instance, reports can generate risk profiles on both companies and individuals in just minutes, providing comprehensive and in-depth global due diligence information. The reports also provide links to the data sources and are fully auditable. This capability is even more critical as recent and upcoming changes to global KYC regulations require the identification of and due diligence on beneficial owners.
Enhancing and Advancing with AI
FIs, especially the large ones, can gain immensely by leveraging artificial intelligence and machine learning (AI/ML) in their KYC and AML operations. An AI/ML enabled system can massively enhance the overall efficiency and effectiveness of compliance processes, driving huge cost savings for FIs. Additionally, it can significantly improve customer experience, delivering a positive impact on an FI’s top line.
The good news for FIs is that AI/ML enabled solutions complement any existing rules-based KYC/AML systems, meaning these new-age solutions can readily be implemented on top of an FI’s existing system. Hence, an end-to-end replacement of existing systems is not required.
In the initial period, FIs should focus on adopting AI/ML enabled solutions in KYC/AML processes for maximum impact with minimal disruption. Link analysis, unstructured data analysis, pattern recognition and workflow automation are all good candidates for early-stage adoption.
Deepak Amirtha Raj is a Strategy & Research Analyst in the Risk and Compliance sector. He focuses on Business Strategy Research, Emerging Technologies and Advanced Analytics. His deep understanding of the industry combined with his thoughtful and strategic approach has helped many RegTech players and Financial Institutions. He is a motivator and coach combining business acumen with analytical depth to align operational efficiencies with corporate goals. Deepak had previously worked with Royal Bank of Scotland.