Detecting Financial Fraud Using GraphX and ThingSpan

Detecting Financial Fraud Using GraphX and ThingSpan

Many articles and blogs, including our own, have shown how graph databases can be used to look at financial transaction data to see if particular individuals or organizations are involved in money laundering or other kinds of fraud. In this blog I will expand on this issue and explain how institutions can use Objectivity’s ThingSpan and GraphX, running on Apache Spark, to tackle detect financial fraud more quickly and efficiently.

In a typical scenario, investigators are trying to determine the money trail initiated by the perpetrator(s). This is a very simple navigational query using a graph database, along the lines of “Starting at the Person_X vertex, perform a transitive closure using Financial_Transaction edges and any kind of vertex.”

That is great if we already know that Person_X is of interest, but what if all we have is a huge graph of recent and historic financial transactions garnered from multiple sources, such as banks, exchanges, real estate transfers, etc.? The problem evolves from being a simple query to being a Big Data analytics one. We are now interested in pattern-finding, not path-following.

The Challenges of Leveraging Data Streams in the Financial Services Industry

The Challenges of Leveraging Data Streams in the Financial Services Industry

Data has long been viewed as a strategic asset by financial service institutions. Driven by a continuing need to address regulatory compliance and fraud detection, these institutions have developed deep expertise in analytics around both streaming and non-streaming data sources. Each customer transaction, act of fraud, credit default, and complaint provides organizations the experience from which to learn and create value from their data and better address their regulatory compliance requirements.

Financial fraud is on the rise. The Nilson Report shows that losses from worldwide fraud on credit cards, debit cards and prepaid cards hit $16.31 billion in 2014, a growth of 19% from 2013 with the U.S. alone accounting for 48.2% of this total. Fraudsters use very sophisticated and technology-enabled techniques. Some of the major victims of fraud are credit card companies, insurance companies, retail merchants, and telecom service providers. Fraud analysis is used to take various forms of stored data and streaming data and convert them into actionable insights to detect fraud and take necessary required actions.