Senzing's Software for Real-Time AI for Entity Resolution to Fight Financial Crime - insideBIGDATA

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Senzing, a new artificial intelligence-based (AI) software company, announced its Senzing software product to address the $14.37 billion financial fraud market. Senzing is an IBM spinout that has reinvented entity resolution, which senses who is who in real time across multiple big data sources. Senzing is disrupting the fraud solutions market by offering the first real-time, plug-and-play, AI entity resolution software product for fraud detection, insider threats and more. Now, any company can deploy Senzing to quickly and effectively detect bad actors in their big data. Senzing uses entity-centric learning and other unique techniques to pierce through falsified identities and networks to find criminals.


Redefining banking through AI and big data - Banking.com

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Banking isn't exempt from the disruption caused by new technologies. Tech giants and start-ups get the credit of being able to create the necessary change. Artificial intelligence (AI) and data science will most likely be the fuel of the new approach to finance. Already present in retail, HR, and marketing, AI is finding its way in the world of banking. But how exactly can AI be used for banking?


8 fintech trends on our radar for 2018

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Check out the session "AI in personal finance: More than just chatbots" at the Artificial Intelligence Conference in New York, April 29-May 2, 2018. Hurry--early price ends March 16. Here's what we'll be watching in the coming year. AI is sweeping across all industry sectors, including financial services. AI touches customer interactions (voice services like Siri and dialog systems), fraud detection, trading, and risk management (machine learning), and is being used to automate many back-office tasks (robotic process automation).


Companies want explainable AI, vendors respond

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Fed up with the bribery, insider trading, embezzlement and money laundering committed by white-collar criminals? What if there was an app that could help nab these crooks by using the same machine learning tools and geospatial data increasingly relied upon by police to predict where the next burglary, drug deal or assault might go down? Sam Lavigne, co-creator of the White Collar Crime Risk Zones app, was onstage at the recent Strata Data Conference in New York, claiming to be able to do just that. "We used instances of financial malfeasance; density of nonprofit organizations, liquor stores, bars and clubs; and density of investment advisers," a straight-faced Lavigne said to an audience of data experts who immediately got the dark humor. For although the White Collar Crime Risk Zones app was indeed built -- using historical data from the Financial Industry Regulatory Authority -- its purpose is not to track white-collar crime, but to draw attention to the danger these kinds of applications, and the data they rely upon, present.


Is IBM Watson A 'Joke'?

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On the May 8th edition of Closing Bell on CNBC, venture capitalist Chamath Palihapitiya, founder and CEO of Social Capital, created quite a stir in enterprise artificial intelligence (AI) circles, when he took on Watson, Big Blue's AI platform. "Human intelligence outperforms machine-learning applications in complex decision making routinely required during the course of care, because machines do not yet possess mature capabilities for perceiving, reasoning, or explaining," explained Ernest Sohn, a chief data scientist in Booz Allen's Data Solutions and Machine Intelligence group; Joachim Roski, a principal at Booz Allen Hamilton; Steven Escaravage, vice president in Booz Allen's Strategic Innovation Group; and Kevin Maloy, MD, assistant professor of emergency medicine at Georgetown University School of Medicine. "A health care organization that relies on a single EHR [Electronic Health Record] vendor's analytic solutions, as well as its own legacy analytics infrastructure created before the era of big data, may see limited progress," they continued. "While many machine-learning solutions are not yet mature and sophisticated enough to support complex clinical decisions, machine learning can be effectively deployed today to reduce more routine, time-consuming, and resource-intensive tasks, allowing freed-up personnel to be redeployed to support higher-end work."