“Leveraging Natural Language Processing to Improve Efficiency and Outcomes”

by | Mar 20, 2019

Track 2: Artificial Intelligence | Room: 1454
Rafi Lav, Specialist Master, Deloitte

Correspondence is time-intensive. Clients face challenges with information access and inconsistent communication which traces back to a truth- it takes an exhausting amount of time to locate, read, and understand the information needed from a mountain of scattered documents and data.

Using natural language processing can assist by identifying relevant material from existing documents ranked by relevance, and pinpoint draft language for personnel to utilize in formulating responses.

We will demonstrate the ability to ask a question and receive answers pulled from a repository by breaking down any questions into components and using learned information and domain-specific terminology.

The tool we will demonstrate intelligently expedites a manual and time intensive process, reading language, while simultaneously solving issues of information access and standardization to improve consistency, accuracy and predictability and build a centralized library of critical information that is relevant to your mission.

Refael Lav is a specialist master with Mission Analytics at Deloitte. Refael has over 14 years of experience developing and leading advanced analytics solutions from approach to implementation for a wide range of clients in the private and public sector, including banking, investment management, and government agencies.

Refael has extensive experience extracting knowledge from unstructured data to identify rare events and signals. His analytics solutions were implemented in the risk and compliance systems of global financial organizations and in leadership decision-making processes where they dramatically reduced costs. His solutions are based on advanced data mining and machine learning detection models, data exploration, models ensemble, messy text and feature selections. He has served as the technical lead role for efficiency benchmark efforts involving modeling and dashboards development, affecting thousands of users with controlled access. Refael has also led statistical analysis efforts for large data sets from identification to economic model creation.

Rafi holds a Master’s of Science of Finance from Johns Hopkins University and B.S. Economics from the George Washington University. Refael is a member of the corporate Board of the Master in Analytics at the George Washington University School of Business.