Deep Learning for Third Party Risk Identification and Evaluation at Dow Jones
Deep Learning for Third Party Risk Identification and Evaluation at Dow Jones


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, information extraction 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
Gain intelligence from global media and research tools, scanning and monitoring almost 2 million articles per week
Achieve near real-time risk data detection and delivery capabilities

We will explain how Dow Jones created AI-powered Risk & Compliance information extraction 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.


Yulia Zvyagelskaya is a data scientist at Dow Jones on the Professional Information Business Technology team, where she’s responsible for the applications and development of machine learning-based solutions to real-world problems. Her research has spanned the areas of machine learning, natural language processing, statistical modelling, and time series forecasting, and a range of industries — retail, advertising, public relations, education, and professional services. Yulia has implemented multiple ML-driven projects in the fields of computer vision and NLP. She holds master’s degrees in computational linguistics and artificial intelligence, and big data management and analytics. Yulia periodically teaches applied analytics and machine learning classes at Dow Jones, she is a data science mentor for Dow Jones employees and speaker at industry conferences — where she shares her knowledge and passion for AI, machine learning, and coding.

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