David provides leadership and expertise for the advancement of knowledge graph and machine intelligence based technologies at Wells Fargo. His team develops innovations that employ various AI capabilities, including semantic technology, graph analytics, machine learning and natural language processing. David’s core mission is to use AI to develop the foundational building blocks for the future of data at Wells Fargo.
David’s interests include how to operationally leverage knowledge graphs to support data catalogs, semantic data lakes, and as a source to train machine learning algorithms.
David chairs the Financial Industry Business Ontology (FIBO) initiative, a collaborative effort of global banks, financial regulators and vendors, under the auspices of the Enterprise Data Management Council. Their goal is to semantically define a common language standard for finance using ontologies.
David is also engaged in a collaborative effort with academic researchers to use knowledge graphs to help explain the influential features contributing to machine learning predictions.
David holds an MBA in Information Systems and an MSW in Psychiatric Social Work.
Knowledge Graphs and AI: The Future of Financial Data
We are at the juncture of a major shift in how we represent and manage data in the financial industry. Conventional data management capabilities are ill equipped to effectively link, harmonize and understand increasing volumes of highly variable data, especially when it is dispersed across multiple line of business organizations or sourced from external sites containing unstructured content. Knowledge graph technology has emerged as a viable production ready capability to elevate the state of the art of data management. Knowledge graph can remediate these challenges and open up new realms of opportunities not possible before with legacy technologies.
This presentation will describe the operational capabilities and benefits of knowledge graph technology, the “future of financial data”. We will discuss how knowledge representation and reasoning capabilities using ontologies is the way forward to tame the enterprise data management monster. The Financial Industry Business Ontology (FIBO) will be described as an exemplar of a semantically modeled knowledge graph for finance. We will describe how semantic graphs can be further enriched with probabilistic associations from machine learning and data mining algorithms. We will discuss the evolution of data from strings and numbers, to first class semantic objects to distributed representations of concept embeddings in vector space. We will also describe how knowledge graph technology can provide a layer of ‘knowledge’ over legacy data structures to obtain maximum understanding of content and provide the foundational building blocks for powerful semantic data catalogs and data lakes. Knowledge graph also positions organizations to better support customer 360, risk management, regulatory compliance, asset management and many other use cases.