Machine Learning


RiskLab's research into Machine Learning in 2020 includes three areas: NLP (Natural Language Processing), Financial Applications, and Reserve Engineering on Machine Learning Models.

Our Work

Natural Language Processing

RiskLab has completed successful research into creating NLP (natural language processing)algorithms for extracting ESG-related twitter data. These ESG (environmental, social, andgovernance) algorithms are in pursuit of creating an “ESG score” for a corporations. The largestcompanies that evaluate corporations on ESG are predominately performed in a manual ad-hocmanner such as checklists. Natural language processing and other machine learning algorithms areparticularly well-poised to overcome the current methodologies’ weaknesses. We’d like to continuethis endeavour with several generalizations of our first approach.

Financial Applications

Truly successful applications of machine learning in finance are far and few between. That being said, with enough financial specificity, significant improvements in classicalfinancial models are possible with machine learning. We’ve found significant success thus far inusing ML to predict future covariances via “financial neural network embedding”, construct optimalportfolios, and determine market sentiment. We’d therefore like to continue this research and findfurther applications of AI in finance.

Reverse Engineering ML Models

An economic focused investigation is currently underway for howfast one can replicate an ML model via querying its API and training a copy of said model via thequeries. This being a fairly new area of research being pioneered at RiskLab, we’re very interestingin continuing this research.


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