Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Why knowledge graphs are important to finance

haikal haikal (GRAKN.AI)
14:0514:45 Thursday, 24 May 2018
Data engineering and architecture
Location: S11A Level: Intermediate
Average rating: ***..
(3.50, 2 ratings)

Who is this presentation for?

  • Chief data architects, chief technology officers, chief data scientists, data scientists, and data architects

Prerequisite knowledge

  • A working knowledge of SQL databases
  • A basic understanding of risk analysis, the GDPR, and fraud detection technologies

What you'll learn

  • Understand why you should use a knowledge graph for financial use cases, the different uses of knowledge graphs in finance, and how automated reasoning works in a knowledge graph and its value to the developer


Artificial intelligence systems process knowledge that is far too complex for current databases. They require more expressive data schemas and more intelligent query languages so as to provide a strong abstraction over complex data and their underlying relationships.

Haikal Pribadi explains why knowledge graphs (KGs) are important for AI systems in the finance sector and details how they are being used to detect and uncover new knowledge, specifically for risk analysis, fraud detection, and GDPR use cases. Haikal then demonstrates how to use GRAKN.AI as a distributed knowledge graph for unifying data representation and gaining insight within financial service organizations.

Photo of haikal haikal

haikal haikal


Haikal Pribadi is the founder and CEO of GRAKN.AI, the database for AI, which uses machine reasoning to handle and interpret complex data. Haikal and his team work on building Grakn, a knowledge graph data platform, and Graql, a knowledge-oriented graph query language that performs machine reasoning to simplify complex data processing for AI applications. GRAKN.AI was recently awarded product of the year for 2017 by the University of Cambridge Computer Lab. Haikal’s interest in the field began at the Monash Intelligent Systems Lab, where he built an open source driver for the Parallax Eddie Robot, which was then adopted by NASA. Haikal was also the youngest algorithm expert behind Quintiq’s optimization technology, which supports some of the world’s largest supply chain systems in transportation, retail, and logistics. He holds a master’s degree in AI from the University of Cambridge.