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.
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.
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