Conversational Analytics of Financial Services Data

Financial services data, including customer data, trade data, product data, investment reports, mortgage documents, policies, and more, is inherently complex and comprises a range of variables, including studies, phases, substances used in trials, site locations, and many more. The data may be stored in many different formats in multiple locations, including SQL databases, spreadsheets, unstructured repositories (like email systems), and third-party systems like core baking and mortgage systems.

Traditional methods require users to exhaustively cross reference data across many dashboards or develop complex database-driven solutions. Such solutions are often opaque and getting answers usually requires tricky, time-consuming work by an analyst skilled in the domain and knowledgeable about where the data can be sourced and how to interpret it correctly, which consumes valuable time and resources.

Knowledge graphs can integrate data from multiple underlying sources and support the implementation of intuitive natural language user interfaces that understand the questions being asked and return accurate, complete answers.