The Financial Industry Regulatory Authority (FINRA) is dedicated to investor protection and market integrity through effective and efficient regulation of broker-dealers. In its mission to safeguard the investing public against fraud and bad practices, FINRA analyzes data in the petabyte scale, looking for market manipulation, compliance breeches, and other potentially illegal activities—acquiring as many as 101 billion market events per day, which are ingested, aggregated, and analyzed for the purpose or performing surveillance of the US markets. FINRA uses cloud computing and leverages big data technology to process ever-increasing volume of financial data.
One of the challenges of dealing with such an immense ecosystem of data is being to be able to navigate through all the available information or narrow it down and focus on specific datasets to gain insight and detect and visualize violations. Using rich visualization metaphors and tools, FINRA leverages the usage of machine learning and data analytics to that end.
Jaipaul Agonus and Daniel Monteiro do Carmo Rosa detail big data analytics and visualization practices and tools used by FINRA to support machine learning and other surveillance activities that the Market Regulation Department conducts in the AWS cloud.
Jaipaul Agonus is a director in the Market Regulation Technology Department at FINRA. Jaipaul is a big data engineering leader with nearly 18 years of IT industry experience, specializing in big data analytics and cloud-based solutions. He’s currently involved in building next-generation big data market analytic platforms with machine learning, advanced visualization, and contextual access across applications.
Daniel Monteiro works on the Market Regulation Technology Department at FINRA, where he’s the principal developer of the Surveillance Review and Feedback visualization tool. In his 15 years of software development experience, Daniel has developed solutions for various business areas.
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