The ability to analyse links and relationships between entities makes graph analytics the ideal tool for anti money laundering.
Here Oracle, whose experts will be leading a series of cyber-focused webinars with FINSIA, highlight how its use can improve the efficacy and effectiveness of a traditional AML program multi-fold. Let us see how.
1. Identify higher risk entities
Ranking algorithms such as closeness centrality, degree centrality, eigenvector centrality, etc., can be used to rank nodes in a graph. These measures capture the importance of a node to a graph along different dimensions.
For instance, degree centrality captures how connected each node is in a graph, whereas eigenvector centrality measures how connected a node is to other highly connected nodes in the graph. Such centrality measures can determine the most significant nodes in the financial graph.
Degree distribution algorithms are an easy way to analyse the structure of a graph. For example, in a typical transaction graph, entities with the highest vertex degree (number of neighbours) are usually business entities. Institutions can analyze the degree distributions of their customers and identify outliers with unusually high degrees given their customer profile. Such entities might be candidates for enhanced due diligence or ongoing due diligence as part of ongoing know your customer (KYC) process.
2. Enhanced detection and monitoring
Graph querying languages like PGQL allow users to write queries or scenarios that capture complex patterns of fund movements. Such tools allow for more tailored monitoring for specific high-risk patterns. This can be particularly useful for identifying ultimate beneficiary owners (UBO), where these UBOs are embedded in a complex chain of ownership and transactions.
Graph algorithms can be used to find the shortest path between nodes in the non-transaction graph (graph considering only non-transactional relationships). If the shortest path in the transaction graph (considering only transaction data) between the same nodes is much longer, it might indicate an attempt to layer funds.
Modern graph neural networks also allow us to learn embeddings or representations of the nodes in a graph. The embeddings capture the topology, relationships, and properties of a node. Such embeddings can also be used in downstream models such as customer risk scoring or event scoring that can greatly improve models’ performance, reducing both false positives and false negatives. Graph Neural Network explainers are also available that can address concerns around explainability of these embeddings.
3. Provide context to and accelerate investigations:
Whenever an alert is flagged, it is important for the AML analysts to determine whether this is an isolated or interconnected incident. In a traditional AML investigation, it would be difficult to identify connectedness in the scattered datasets (customers, accounts, transactions, etc.). However, constructing a graph to represent a case enables graph visualizations and analytics, helping investigators get a contextual view of the investigated entity.
Modern graph deep learning techniques also allow us to learn embeddings for the cases and then surface similar suspicious activity reports (SAR) that can provide useful guidance to investigators.
4. Financial crime knowledge graphs
A long-term goal for financial institutions can be to construct a financial crime knowledge graph. Combining modern natural language processing (NLP) and graph databases will allow institutions to create a single financial crime graph that captures all structured and non-structured, internal and external data on customers. This will enable a deeper understanding of customers which will be useful across various functions such as KYC, investigations, and even marketing.
Bringing it all together
This is just a sampling of the potential use cases graph analytics enables. Institutions can experiment with some simpler use cases before embarking on large-scale adoption.
Graph analytics can empower data scientists to identify anomalies and patterns that can improve detection, reduce costs and deliver faster time to AML compliance. It also offers powerful visualization capabilities that can markedly improve investigator productivity and help them to understand complex intricate activity patterns.
Implementing graph analytics as part of the AML toolkit would need skilled resources, investment, and commitment; however, the benefits outweigh these costs as embracing graph analytics can turbocharge AML compliance programs at banks and financial institutions.
Learn more in our upcoming webinar, Managing Cloud Risk and Maintaining Security Compliance
