Three big questions about AI in financial services

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Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics. Early returns for companies making such investments are promising.


The Politics of Artificial Intelligence in Financial Markets

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Technology is and has always been a crucial part of finance. From the first promissory notes (banknotes) in the Netherlands and China, there was a race with counterfeiters that parasitically undermined trust. As in political communication, technology is the message, rather than merely "a tool": when it comes to money, trust is not just instrumental, it is fundamental. With cashless payments being the norm and social media platforms weαving an additional layer of involvement in our social data web – Amazon, Google, Facebook, Apple – Artificial Intelligence (AI) is already in our wallets, business, and financial affairs. In a non-western setting, one may refer to the Chinese "social rating" system, which allows the state to value and evaluate social behaviour patterns, creating a link to individual credit rating.


Gurucul Risk Analytics (GRA) - Cybersecurity Excellence Awards

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Gurucul GRA is a proven big-data security analytics solution that has been successfully deployed by government agencies and global Fortune 500 companies across the financial, healthcare, technology, retail and manufacturing sectors to detect and deter insider threats, account compromise and advanced external attacks. Customers include one of the world's largest Internet payment companies, a top 5 US health insurer, large financial services firms, and government agencies. Gurucul was the only vendor cited for meeting all five use cases outlined in the Market Guide for UEBA report by analyst firm Gartner: security management, insider threats, data exfiltration/DLP, identity access management, SaaS security, plus the extra qualifications for compliance and cyber fraud. Gurucul has received industry recognition for its innovations in security analytics, including 2016 SC Awards in the US and Europe for Best Behavior Analytics/Enterprise Threat Detection, being named SINET 16 Innovator in both 2014 and 2015, Gartner Cool Vendor 2014, winning the 2016 CDM award for Best Insider Threat Prevention Solution, and more. Product review of Gurucul GRA by SC Magazine: "This is, hands-down, the most sophisticated example of behavioral analytics we have seen to date.


Future of FinTech

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Once thought to be a disruptor, FinTech is today an innovator and enabler. Financial technology, or Fintech as it is better known, began with nimbler start-ups first disrupting banks with their innovative approaches and later evolved with the latter also building partnerships with the banks to strengthen the entire financial services ecosystem. The FinTech industry comprises a variety of financial businesses such as online Peer-to-Peer lending, SMB finance, crowd-funding platforms, wealth management & asset management platforms, cryptocurrency, trading management, mobile payments platforms and money/remittance transfer and the list goes on. The versatility of FinTech is so profound that every sector is experiencing a make-over due to FinTech Innovations on daily basis. In fact, FinTech is now on the verge of changing the core requirement of a sector itself.


Piecing together the puzzle of property prices with peer-to-peer lending data

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Data science provides a venue that exercises the inquisitive mind. The opportunity to investigate new datasets and understand their value for a theory or model never ceases to fascinate and enthrall. That inquisitive nature -- the mission to understand what drives value in the commercial real estate (CRE) market -- runs at the core of what we do here at GeoPhy. It leads to the exploration and analysis of a wide variety of data. This variety and the unprecedented volume of data now available provide two of the conventional "V's" of big data [1], and make this quest both a compelling and complicated one -- like fitting together pieces of a puzzle.