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Deep Learning for third party risk identification and evaluation at Dow Jones

Yulia Zvyagelskaya (Dow Jones)
1:00pm1:40pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South

Who is this presentation for?

Those interested in the best practices of applying deep learning models to real-world business problems or those involved in data science, machine learning and natural language processing.

Level

Intermediate

Prerequisite knowledge

The attendees should have general understanding of machine learning models building and leverage and an interest to applying deep learning approaches to practical business problems.

What you'll learn

The attendees will learn how Dow Jones Risk & Compliance leverage latest deep learning and natural language processing techniques to develop and deploy internal risk management and regulatory compliance research tools.

Description

Over 16 years Dow Jones is supplying Risk & Compliance data to banking and financial Institutions, corporate and governments, covering the world with defined, structured content sets of people and entities used to manage third-party risk: anti-money laundering, anti-bribery and corruption, sanctions or reputational risk. In order to achieve a comprehensive coverage guided by international regulation and guidance since 2002, we follow very high editorial standards and research methodologies, combined with state-of the-art machine learning techniques, to manage 30 risk categories 24 hours per day in over 70 languages.

In this presentation, we will focus on the natural language processing and deep learning techniques, which Dow Jones leverages for Risk & Compliance data capturing and workflow efficiency improvement.

At Dow Jones, we wanted to apply a new approach to the existing content delivery pipeline with the objectives to:

Eliminate low-level, repeatable, manual processes, enabling researchers to focus on strategic tasks
To gain intelligence from global media and research tools, scanning and monitoring almost 2 million articles per week
To achieve near real-time risk data detection and delivery capabilities

We will explain how Dow Jones created AI-powered Risk & Compliance data research solution, that uses Natural Language Processing for risk profiles creation and management. The presentation will also highlight the unstructured data preprocessing stage, model selection criteria and neural networks parameter tuning processes to provide scalability and performance in order to achieve mentioned key objectives.

Photo of Yulia Zvyagelskaya

Yulia Zvyagelskaya

Dow Jones

Yulia Zvyagelskaya is a data scientist at Dow Jones, where she is responsible for development and implementation of machine learning applications. Yulia developed several AI-driven projects in the fields of Computer Vision and Natural Language Processing. She holds Master’s degrees in NLP (Computational Linguistics, Artificial Intelligence), as well as in Big Data Management and Analytics. Yulia has won several international Artificial Intelligence and Big Data competitions.

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