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Forbes Fintech 50 2018: The Future Of Wall Street And Big Data

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Being at the cutting edge of technology on Wall Street can mean billions of dollars in value created, or saved. In a dawning era of disruption, Wall Street-focused financial technology startups are helping the world's biggest banks and investors manage the flow of money better, safer and at a lower cost. America's biggest lenders are using novel machine learning engines to sift through haystacks of information to uncover fraud and catch identity thieves, saving consumers money. In financial markets, new techniques of studying language are helping firms and exchanges spot market manipulation. Mega banks are using artificial intelligence to get a better, real time understanding of their market and operational risks, while new software platforms are altering how big firms collaborate internally and with the rest of the street.


Top Data Sources for Journalists in 2018 (350 Sources)

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There are many different types of sites that provide a wealth of free, freemium and paid data that can help audience developers and journalists with their reporting and storytelling efforts, The team at State of Digital Publishing would like to acknowledge these, as derived from manual searches and recognition from our existing audience. Kaggle's a site that allows users to discover machine learning while writing and sharing cloud-based code. Relying primarily on the enthusiasm of its sizable community, the site hosts dataset competitions for cash prizes and as a result it has massive amounts of data compiled into it. Whether you're looking for historical data from the New York Stock Exchange, an overview of candy production trends in the US, or cutting edge code, this site is chockful of information. It's impossible to be on the Internet for long without running into a Wikipedia article.


How AI Can Help with the Detection of Financial Crimes 7wData

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Paige Dickie develops Artificial Intelligence (AI) and digital strategy for Canada's banking sector at the Vector Institute for Artificial Intelligence in Toronto. She began her career in management consulting -- much to the disappointment of her father, an engineer -- because she had earned advanced engineering degrees in biomedical and mechanical engineering. Dickie initially worked at McKinsey, the global consulting firm, helping multinational financial institutions across a range of fields from data strategy and digital transformation to setting up innovation centers. She recently joined Vector to lead what she describes as "an exciting project with Canada's banking industry. It's an industry-wide, sector-wide, country-wide initiative where we have three different work streams -- a consortium work stream, a regulatory work stream, and a research-based work stream."


Major Roadblocks on the Path to Machine Learning

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In part one of this series last week, we discussed the emerging ecosystem of machine learning applications and what promise those portend. But of course, as with any emerging application area (although to be fair, machine learning is not new), there are bound to be some barriers. Even in analytically sophisticated organizations, machine learning often operates in "silos of expertise." For example, the financial crimes unit in a bank may use advanced techniques to catch anti-money laundering; the credit risk team uses completely different and incompatible tools to predict loan defaults and set risk-based pricing; while treasury uses still other tools to predict cash flow. Meanwhile, customer service and branch operations do not use machine learning at all because they lack the critical mass of specialists and software.