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AI to Enhance Due Diligence

How Financial Institutions Can Use AI to Enhance Due Diligence

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.

Adding artificial intelligence (AI) techniques to Know Your Customer (KYC) and AML compliance improves automation workflows and better analyzes large volumes of data. Here are some of the benefits of using AI in KYC and AML:

Link Analysis

Human investigators have difficulty comprehending massive data sets, with large numbers of objects of different types. Enter AI-based link analysis, which models complex webs of linkage data evidence into nodes and links, enabling better insight and decision-making. When modeled as a graph, it makes dubious relationships, accounts or transactions more apparent. 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.

Unstructured Data Analysis

With the vast and increasing number of regulatory updates, guidance and reports, information overload is a significant issue. Using natural language processing (NLP) and supervised machine-learning techniques, AI can analyze unstructured content, extract metadata, identify entities and automatically interpret documents.

Pattern Recognition

Often, money launderers use numerous steps to obfuscate the true source of funds. AI-based systems, as opposed to rule-based software, are more able to uncover these complex patterns. One innovation is the use of Bayesian learning techniques to better understand legitimate behavior, enabling more robust and intelligent AML systems to initiate investigations and possible suspicious activity reports (SARs).

Workflow Automation

Generating documents, reports, notifications and audit trails is time consuming. AI can automate generic content creation, including risk profiles, due diligence information and links to data sources, in mere minutes, allowing compliance to focus on other, more in-depth tasks.

Enhancing and Advancing with AI

AI for Financial Institutions

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.

Disclaimer: Trulioo provides no warranty that the information contained in this document is accurate, up to date or complete and in no circumstance does such information constitute legal advice. Any person who intends to rely upon or use the information contained herein in any way is solely responsible for independently verifying the information and obtaining independent expert advice if required.

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