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Merkle Science leverages TigerGraph to help customers combat financial crimes

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Merkle Science, provider of a predictive blockchain monitoring and investigative platform, plans to use TigerGraph’s advanced graph analytics to preempt and prevent financial crime. Merkle Science, with TigerGraph, will enhance how the company helps its customers—which include government agencies and financial institutions—stay one step ahead of criminals.

This will be done via the construction of a cryptocurrency network graph using TigerGraph, giving Merkle Science the ability to analyze more than 2.5TB of data in real time to better connect relationships that they were not able to do prior to unlock deeper, wider, and operational analytics at scale.

Founded in 2018, Merkle Science is the next-generation predictive cryptocurrency risk and intelligence platform that helps crypto companies, financial institutions, and government entities detect, investigate, and prevent illegal activities involving cryptocurrencies.

“Blockchain is still considered a nascent technology that is changing at a rapid pace. We are excited to partner with TigerGraph for their ability to analyze and visualize tremendous amounts of graph data. TigerGraph’s ability to handle large quantities of data coupled with their elegant and powerful query language GSQL have enabled us to build a graph data warehouse which we use to help our users understand flows of funds and determine their risk exposure. TigerGraph has proven to be invaluable in helping our users to differentiate between good actors and bad ones,” said Nirmal Aryath Koroth, co-founder and chief technology officer at Merkle Science.

As cryptocurrency and blockchain increasingly become more mainstream, the ecosystem that surrounds and supports it will also develop rapidly, while increasing the prevalence of it being a target for financial crime. As such, risk management is key—from government institutions to financial institutions across derivatives, credit, insurance, and more. The cryptocurrency network graph that Merkle Science has constructed using TigerGraph enables their customers to identify the percentage of funds sent or received from different types of sources from a specific location or address, which is advantageous in detecting and analyzing potential criminal activity.

Merkle Science’s cryptocurrency network graph, which currently contains more than 2.5TB of data and consists of 5 billion vertices and 36 billion edges, supports a complete extract, transfer and load (ETL) each day that takes under an hour, with near-instant streaming updates.

“We reached out to TigerGraph as we’ve been trying to find an elegant way to visualize our investigative data over the last 2 years. Other incumbents in the graph database space weren’t able to process our vast amounts of data fast enough in order to generate graphs in real time. TigerGraph helped solve that issue for us—with its ETL ingestion speed, we could do both batch and streaming load at the same time. TigerGraph’s GSQL software program is also the most sophisticated query language I’ve seen so far and it’s flexibility allows us to implement complex graph algorithms which would otherwise be impossible or take far longer to implement on other incumbents,” added Aryath Koroth.

For more information, go to www.tigergraph.com.

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