Commercial banks hand out loans based on risk assessment at a point in time. When a company's risk degrades, banks must act quickly to limit their exposure. We built a graph that combines external news, and internal data to alert analysts when a customer's rating might degrade bc of negative news.
With thousands of commercial banking clients, it is difficult for risk analysts to know what the bank's exposure is to certain customers. Often they are unaware that an entity is a customer or has a commercial relationship with one.
The graph we built combines relational internal bank data to show how a loan is structured, unstructured documentation to identify connected entities, and external media (twitter, reuters, etc) with sentiment analysis.
Whenever we detect a news article with negative sentiment and identify a path to one of our customers we issue an alert to the risk team who can then take appropriate action. In this talk I'll highlight the key technologies we used to make this possible: NLP, Neo4j plugins, and how we built an end-to-end application using neo4j as the engine.
Wednesday April 22, 2020 1:30pm - 2:10pm EDT
Room 3
