Discovering Fraud Patterns using IBM Financial Crime Insights

Have you ever wondered how to make sense of this data and subsequent revelations thus made on money laundering, movement of money, and corruption? Gaining meaningful insights from such massive, dense, and interconnected data is not easy. It requires detecting indirect relationships, patterns, and connections, a task relational databases that rely on primary and foreign keys to link tables are ill equipped with. Graph traversals on the other hand are highly efficient which makes transaction monitoring and analysis fast, easy and intuitive.

About IBM FCI Graph Analytics

Schema of the data

In this section, we describe how we mapped the data from FinCEN files to a graph data structure. The structure consists of the following: each Suspicious Activity Report aka ‘filing’ is filed by a ‘filer’. Each filing contains information on the “originator”, “beneficiary”, and “entity” of the transaction. These actors are banks located all over the world.

Query Data Analysis

Once the data was loaded on to the system, we used the Cypher Query framework to write queries and detect fraud patterns. This framework can be accessed by opening the “Explore” tab after the user logs into the system.

Let us now look at some specific examples from the data. The following is an example of a Suspicious Activity Report (SAR), where nearly $45 million USD were transferred from JP Morgan Chase, London to Alfa Bank, Russia:

Cypher Query for top 5 banks that filed the most SARs

Cypher Query for banks where most transactions originated:

This query lists 5 banks that have more than 1,000 outgoing transactions.

Cypher query for countries where more than $40 million USD was received:

Cypher query to detect supernode:

In the context of graphs, a supernode can be described as a vertex with disproportionately large number of incoming and outgoing edges. We use this query to list 5 countries that were flagged most often in SARs.

Graph Algorithm Analysis

The top beneficiary banks in FinCEN files:

The top originator banks emerged from FinCEN files:

Top beneficiary countries revealed in FinCEN files:

Using a combination of Page Rank and Degree algorithms, we identified the most prominent countries in the network and have tagged them as Originator (O), Intermediary (I) and Tax havens (TH), as listed below:

Conclusion

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