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How Graph Analytics Help Your Financial Institution’s AML Efforts

Today’s highly connected digital landscape has transformed the way we do banking. However, this increase in convenience comes with various security risks that render financial institutions (FIs) vulnerable. As FIs handle larger and larger volumes of data every day, it can be difficult to detect suspicious activities, such as those that may be indicative of money laundering and financial fraud. Without the right tools, FIs cannot stay compliant with government and transnational regulations, and they won’t be able to enact measures such as anti-money laundering (AML) initiatives to protect their clients, systems, and overall business posture. One such tool that banks and other financial entities can use for AML compliance is graph analytics. Simply put, graph analytics entails using advanced algorithms to automatically establish relationships between nodes or entities in a graph, which may represent a number of things, including financial institutions, customers, and transactions. With its emphasis on establishing connections, this diagram-based visualization technique has been proven effective in helping law enforcement professionals and intelligence analysts detect cybercrimes and identify leaders of criminal organizations.

As financial crimes like money laundering become more sophisticated, your institution needs to be more proactive in protecting your customers and in fortifying the security of your front-and back-end operations. In this article, we’ll talk about how graph analytics changes the game by helping you quickly nip criminal activities like money laundering in the bud.

Graph Analytics Can Level Up Detection and Monitoring Efforts

To detect potential cases of money laundering, graph algorithms identify the shortest path between entities. The longer the path, the more likely it will indicate a case of money laundering. Unlike rules-based detection processes, graph analytics can establish these distant relationships and complex pathways to uncover previously undetected connectivity patterns between malicious and non-suspicious actors.

Intuitive programming languages like the Property Graph Query Language (PGQL) help in this regard by detecting intricate patterns in fund transfers, for example. Financial platforms that use PGQL-based graph analytics techniques allow users to create queries to identify potential scenarios and establish a chain of transactions. This is particularly helpful in identifying ultimate beneficiary owners (UBOs) who are often the masterminds behind money laundering schemes.

It Can Help Narrow Down High-Risk Entities


Graph analytics uses algorithms to rank entities based on importance. One such algorithm is degree centrality, which can be used to determine how connected an entity is to other nodes in the graph. Together with artificial intelligence (AI), graph analytics tools can help uncover high-risk entities and prevent investigators from wasting time on false positives.

FIs can use AML tools with graph analytics algorithms to determine individuals or businesses with unusually high levels of connectedness. Additionally, these tools can help FIs do their due diligence and narrow down customers who may need to undergo further know your customer (KYC) processes.

Oracle’s AML software, for example, uses risk scoring models to support KYC procedures. This allows FIs to have a comprehensive view of customers’ risk posture in relation to countries, jurisdictions,  businesses, and individuals under a certain industry, ultimately helping these institutions double down on their AML efforts.

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Graph Analytics Can Aid in Establishing an Overarching Financial Crime Graph

Combined with natural language processing (NLP), graph analytics can help institutions create a broad, interconnected graph that instantly highlights suspicious activities and potentially malicious actors. A comprehensive graph that makes sense of structured and unstructured customer data provides actionable insights that can aid in AML investigations as well as KYC efforts.

Moreover, making use of graph analysis capabilities allows FIs to have a better overview of financial activities and gain an upper hand in combating money laundering schemes and other financial crimes.


It Can Speed Up Investigations By Providing Context

Graphs are vital tools for AML investigations because they help analysts quickly determine if an alert is an isolated case or a warning sign for interconnected criminal activity. AML technologies with graph analytics features also aid in the creation of suspicious activity reports (SAR) and suspicious transaction reports (STRs), essentially boosting investigator productivity by immediately singling out causes for concern. Oracle’s AML solution, for instance, rapidly conducts risk analysis and compares customer data with global watchlists, sanctions, and politically exposed persons (PEPs) to contextualize its report generation functions.

In contrast, AML investigations that do not utilize graph analysis are limited by disparate datasets such as transactions, account data, and personal information that are relatively difficult to put into context.

 Experience a Different Way of Battling Financial Crime

Using graph analytics for AML efforts has evident benefits such as stronger customer security, speedier investigations, and clearer data visualization. But before you start implementing advanced AML solutions, you need to understand that managing these capabilities requires commitment and technical expertise. It would be wise not to charge head-first when implementing next-level AML solutions and make sure to conduct thorough planning and consultation beforehand.

That said, you should strongly consider adopting graph analysis to improve your institution’s regulatory compliance and to fortify your security posture. Beyond these core benefits, embracing new AML methods is a necessary step that you must make to promote financial security in a way that transcends borders, technologies, and industries. 

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