Deep Learning Extraction for Counterparty Risk Signals from a Corpus of Millions of Documents

China has been experiencing rapid growth over the last decade due to economically friendly reforms and a growing skilled and young population.

rstudio::conf 2020

Deep Learning Extraction for Counterparty Risk Signals from a Corpus of Millions of Documents

January 30, 2020

China has been experiencing rapid growth over the last decade due to economically friendly reforms and a growing skilled and young population. With this increasing growth, China’s interconnectedness with the global economy has increased significantly. In parallel to this economic evolution, technology has experienced rapid acceleration, which has enabled firms and governments to track and record vast amounts of data. The side effect of this unstructured big data growth is that datasets may be polluted, meaning information can be conflicting, missing, and/or unreliable. This creates a gap in the ability to provide transparency to the exposed firms importing from China: both timely early warning signals and wide coverage of small- and medium-sized enterprises (SMEs). We have been able to address this problem for our end-users by using deep learning to extract information value and opinion from a public corpus to create the needed transparency. Our data science & machine learning stack uses connect, shiny, reticulate, tensorflow and scikit-learn to build the interactive solution to our clients and deploy it using spark and airflow.

About the speaker

Moody Hadi

Moody is a Senior Director of Financial Engineering at S&P Global – Market Intelligence. As a Group Manager in New Product Development within Market Intelligence, he leads a team focusing on applying modeling techniques, such as machine learning and data sciences to extract information value for risk management. Previously, he was Co-Head of Research and Development at Credit Market Analysis (CMA), where he led the model development and research on Credit Default Swaps pricing and risk management. Prior to CMA, Moody was at the Chicago Mercantile Exchange (CME) Group and before that had several senior roles in analytical & technical practices, spanning diverse areas from Asset-Liability Management (ALM) to Business Intelligence (BI). Moody holds a Bachelors of Science in Computer Science from Georgia Institute of Technology, Masters of Science in Operations Research from Columbia University and MBA from the University of Chicago – Booth School of Business.