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Deep Learning Extraction for Counterparty Risk Signals from a Corpus of Millions of Documents - Moody Hadi
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.