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Big Data Explosion

Cisco

Those opportunities will rely on data and analytics for real-time decision making. Some familiar examples are receiving banking fraud alerts on mobile devices, submitting photos for insurance adjustments, or using robo-advisors for investment decisions. Creating new customer value from AI.

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Giving Big Data Decision-Making A Real-Time Touch

PYMNTS

Big data, big data, big data. We all know the term, understand its significance and have seen plenty of examples across the industry of how businesses are utilizing massive amounts of data (and the applied analytics needed to make sense of it all) as a competitive edge in the market.

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Redefining banking through AI and big data

NCR

The answer comes down to understanding that AI is an umbrella name for multiple technologies built on big data and neural networks. Fraud detection. Detecting fraud is a combination of artificial intelligence and human expertise. This protects customers from fraud by authenticating calls. Defining AI for banking.

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BNY Mellon’s AI-Based Fraud Detection Initiative

PYMNTS

Countering digital fraud is a lot like playing whack-a-mole: As soon as one fraudster is taken out, two more pop up where they’re least expected. The popularity of digital banking services has created ample opportunities for bad actors, leaving banks scrambling to protect themselves against the rising tide of fraud. It helps.”.

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Sift Science Eyes Global Fraud Prevention With $53M In Funding

PYMNTS

Fraud detection startup Sift Science has raised $53 million in a series D round, bringing its total amount raised to $107 million. Founded in 2011, Sift Science plans to use this latest round of funding to grow its fraud detection and prevention product globally. Clients include Twitter, Airbnb, Twilio, Instacart, Zillow and Yelp.

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The big data boom – what it means for banking

NCR

Big data has been one of the tech industry’s most popular buzzwords for a few years now. But as the number of data sources grows and technology to process it becomes more powerful, the trend is changing from a nice-to-have addition to becoming an essential part of any company’s offering. Fighting fraud.

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Beware of data “science projects” turned fraud prevention solutions

Insights on Business

Because of this, traditional rules-based fraud detection systems have become outdated and no longer work. Today, real-time payments require real-time fraud detection. Modern payment fraud schemes require modern prevention. Data-science-project-turned-fraud-prevention-solution vs. purpose-built-fraud-prevention-solution.

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