How Graphs continue to revolutionise the prevention of financial crime & fraud in real-time

Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.

Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.

Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.

A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.

Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.

Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.


How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?

Is it possible to do this in real time?

What do graph query languages have to do with this?

 

We addressed those questions, and more, with some of the world’s Graph database leading experts in our Connected Data London webinar sponsored by TigerGraph. To recap, check the presentation below, or catch the webinar replay.

How Graphs Continue to Revolutionize The Prevention of Financial Crime & Fraud in Real-Time from Connected Data London

Topics include:

  • A quick introduction to graph databases
  • Why graph databases are a good match for Financial Crime prevention
  • Use cases: do it like Alibaba, OpenCorporates, and Visa
  • Going real time: the need for speed in Financial Crime prevention
  • Querying graphs: the missing link between graph analytics theory and practice
  • Some key requirements for graph query languages
  • TigerGraph GSQL, a query language built for Deep Link Analytics
  • New features in GSQL and real-world applications: pattern matching, accumulators, and Financial Crime prevention

Connected Data London is the leading conference for those who use the relationships, meaning, and the context in Data to achieve great things. If you want to be on top of things in the intersection of knowledge graphs, linked data and semantics, AI and machine learning, and graph databases, and to learn from leaders and innovators, CDL’s got your back.

Join our main event and workshops in London on October 3 & 4, come to one of our regular open community Meetups in London and Berlin, or follow us online on Twitter, LinkedIn, Facebook, Instagram, YouTube, and SoundCloud.

OpenCorporates is the largest open database of companies and company data in the world, with in excess of 100 million companies in a similarly large number of jurisdictions. Its primary goal is to make information on companies more usable and more widely available for the public benefit, particularly to tackle the use of companies for criminal or anti-social purposes, for example corruption, money laundering and organised crime.

TigerGraph is the world’s fastest graph analytics platform for the enterprise. Its Native Parallel Graph™ (NPG) design focuses on both storage and computation, supporting real-time graph updates and offering built-in parallel computation. Its SQL-like graph query language (GSQL) provides for ad-hoc exploration and interactive analysis of Big Data.

With GSQL’s expressive capabilities and NPG speed, you are able to perform Deep Link Analytics: uncovering connections that previously were too impractical to reach.

Webinar host: George Anadiotis George has been connecting data since he implemented his first Graph prototype in 2005. A practitioner and thought leader with ZDNet and his own brand, Linked Data Orchestration, George’s got Tech, Data, and Media, and is not afraid to use them.
Webinar panelist: Rebecca Lee Rebecca is the Chief Data Officer for OpenCorporates, the largest open database of companies whose primary goal is to make information on companies more usable and widely available for the public benefit, particularly to tackle their use for criminal or anti-social purposes. Prior to this, Rebecca led PwC’s Investigative Analytics team: conducting forensic investigations into alleged criminality and wrongdoing, and helping clients to build their fraud and risk capabilities..
Webinar panelist: Dr. Victor Lee Victor is Director of Product Management at TigerGraph, bringing together a strong academic background, decades of experience in the technology sector, and a strong commitment to quality and serving customer needs. Victor has a PhD on graph data mining in Kent State University, and he was a visiting professor at John Carroll University before joining TigerGraph
Webinar panelist: Dr. Alin Deutsch Alin is Chief Scientist at TigerGraph, leading TigerGraph’s development strategy, technical path and key technical direction. Alin focuses on graph query language research and development, holds a PhD in Computer Science from the University of Pennsylvania and a U.S. patent based on query optimization, and has a long-standing, award-winning career in research.