Webinar: Risk Analytics using Knowledge Graphs/FIBO with Deep Learning

Gartner reported that close to 50% of AI and machine learning projects fail in part due to challenges with “data complexity and systems integration”. In the context of risk analytics, combining macroeconomic data with internal risk data, tracking data provenance, and staging analytics-ready data for constantly changing business requirements illustrate several of those data challenges.
Using publicly available data for 40 million mortgage loans over 20 years, we built a knowledge graph leveraging the FIBO ontology to demonstrate a next-generation analytics platform that:
– Reduces time spent on data engineering tasks by over 50%
– Natively captures detailed data lineage for each reported output metric
– Rapidly and flexibly generates input data for deep learning models
– Increases speed to delivery of sensitivity analysis and stress testing by generating model predictions directly in the graph database
– Enables a more holistic capture of model risk management data
The webinar will explore:
– Challenges common to a risk analytics pipeline
– Comparison of relational data model and graph approaches
– Application of graph analytics to mortgage loan data
– Deep neural networks and graph convolutional networks
– Scalable graph computation with TensorFlow
– Use cases in adjacent areas including customer service, collections, fraud and AML
The webinar will be led by industry experts including:
Greg Steck, Director of Data Engineering, FI Consulting
Thomas Cook, Director of Sales, Cambridge Semantics
Mike Meriton, Moderator, Co-Founder & COO, EDM Council
Register for the Webinar