Using unstructured data and machine learning to understand loss events


Data is big news. Big, small or in between. Organizations are building out their data capabilities accordingly. Most organizations are historically adept at working with structured data, but as there is more data is available than ever before, its this explosion in unstructured data that provides great opportunity.

Using news and event data to identify the reality around an entity of interest (company, person or event), understanding how that reality changes with new data and how this impacts that entity is where business value can be found. These insights represent opportunities or risks. Next, taking action on this insight is imperative to realize this business value.

Machine learning technology can be applied to great effect to unstructured datasets to identify these changing realities. In the financial services segment particularly within insurance and asset management this includes the ability to identify the network effect of loss events which is the focus of this presentation.


Niranjan Thomas is General Manager, Platform and Technology Partnerships, Professional
Information Business, at Dow Jones. He is responsible for the DNA data platform including product strategy, go to market, customer solutions and driving technology ecosystem partnerships.
Niranjan has over 16 years of experience in Technology leadership roles across software design and development, software project management, cybersecurity, risk and business management. He has spent significant time delivering solutions for the manufacturing, consulting, technology, telecommunications, media and financial services industries.
Prior to joining Dow Jones he served as Head of Technology at AMP, Australia’s leading specialist
wealth management and life insurance company.

Niranjan holds a Bachelor’s degree in Business Information Systems from RMIT University,
Melbourne, Australia.

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