A consumer's credit score used to be a commonly understood number -- the time-honored FICO score -- that banks all used in their underwriting. But banks increasingly are relying on dozens of scores that reflect a variety of data sources, analytics and use of artificial intelligence technology. The use of AI offers lenders the ability to get a precise look into someone's creditworthiness and score those previously deemed unscorable. But such scoring techniques also bring uncertainty: What it will take to convince regulators that AI-based credit scores are not a black box? How do you get a system trained to look at the interactions of many variables, to produce one clear reason for declining credit?

There's a growing sentiment out there with all the wonderful things happening in artificial intelligence, machine learning, and data science that these technologies are ready to solve all the things (including how to kill all humans). The reality is there are still a bunch of significant hurdles between us and the AI dystopia/utopia. One big one that is the main impetus behind my research is the disconnect between the statistical foundations of machine learning and how real data works. Machine learning technology is built on a foundation of formal theory. Statistical ideas, computer science algorithms, and information-theoretic concepts integrate to yield practical methods that analyze large, noisy data sets to train actionable and predictive models.

AI in Production – AI is only used by banks in production in a few key use cases such as high-frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalizing models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

The focus on financial crime, and the money laundering that funds terrorist attacks and other criminal activities, has forced the industry to look for smarter approaches. In the previous posts in this mini-series, TJ Horan noted that AI is the newest hope for compliance, and Frank Holzenthal explored the benefits that AI can bring to compliance officers. Now it's my turn, and I'm going to explore the AI and machine learning technologies my team has integrated into the FICO TONBELLER Anti-Financial Crime Solutions. We have built on top of the FICO TONBELLER solutions using FICO's battle-proven and patented artificial intelligence and machine-learning algorithms, which are used in FICO Falcon Fraud Manager to protect about two-thirds of the world's payment card transactions. Industry experts have begun to realize the significance of analytics in combatting anti-money laundering.

The Defense Department is likely within 18 months of introducing autonomous cybersecurity tools that will be capable of augmenting human analysts by predicting threats, including insider activity, and dynamically isolating parts of the network that may come under attack, the department's outgoing chief information officer said Thursday. Terry Halvorsen, speaking to reporters during his last media roundtable before retiring at the end of the month, said as the department continues deployment of its 12 Joint Regional Security Stacks--a $1.6 billion effort to eliminate hundreds of disparate firewalls with centrally managed commercial security appliances and network monitoring tools--the next major step will be the deployment and testing of AI-based security applications. "Given the volume [of attacks] and where I see the threat moving it will be impossible for humans by themselves to keep pace. We can and we're very close to being able to put more autonomy into the security tools, and we will get to the point within the next 18 months where AI is becoming a key factor in augmenting the human analyst in making those decisions about what to do," Halvorsen said. Halvorsen recently hosted a special meeting of the Five Eyes alliance (Australia, Canada, New Zealand, the United Kingdom, and the United States), plus Germany, Japan, and NATO, at which AI-based cybersecurity was discussed in detail. "This is very real inside the department.

It sounds like Valve is taking advantage of a neural network to combat the spread of cheats in Counter-Strike: Global Offensive. Posting on Reddit, someone using the moderator-verified Valve Anti-Cheat account wrote that a fix is in the works for folks using spinbot hacks. A spinbot, as Rock, Paper, Shotgun describes it, helps avoid being hit by other players' weapons. Combined with aiming cheats, it makes the cheater pretty impervious to defeat. But the type of processing power needed to fuel this sort of endeavor sounds pretty intense considering everything that goes on in a given match, and the amount of matches that take place per day.

NEW DELHI: Software jobs are still pretty lucrative: Only that robotics, artificial intelligence and Big Data now rake in the money, while mature application services sink fast in the pronounced migration of value toward emerging technologies. Initially built around cost benefits and an improving telecom infrastructure that would allow Indians to follow the US workday, technology is now battling a sweeping wave of de-globalization and automation: Emerging technologies where talent is scarce will likely be the winners when valuemigration is complete, according to hiring and compensation data. At least five search firms and HR heads at technology companies told ET that the demand for professionals with expertise in robotics, machine learning, artificial learning and data science has increased by up to 50% over the last year. Hirings at the top end of the technology spectrum will replace jobs that are repetitive in structure and content, creating a recruitment market premised largely on emerging technologies. "While low-skilled jobs will drop by 30%, automation is expected to increase the middle-skilled jobs by 8% and high-skilled recruitments will rise by 56%," said Debashis Patnaik, senior director, human resources, Dell EMC.

Parascript has introduced a data location, extraction and verification software solution that deploys template-less, neural network-based document extraction. Parascript says it has'productised' it's machine learning platform to support custom-developed recognition projects with much quicker turnaround than traditional rules-based approaches. The result is significantly faster production with more reliable and refined results. "Machine learning offers a whole new set of opportunities for organisations across many industries to more precisely streamline their operations and deliver rapid, accurate data to their clients," said Greg Council, Vice President of Marketing and Product Management. Traditional recognition and capture solutions often successfully use business rules to process information.

This is a replay of IBM's Machine Learning Event which took place in New York City on February 15, 2017. To learn more about IBM Machine Learning, please visit: https://ibm.biz/machinelearning In this video: Event Kickoff -- Machine Learning launch Overview -- Rob Thomas, General Manager, IBM Analytics Top Trends in Machine Learning & Cognitive Applications -- Mike Gualtieri, VP, Principal Analyst, Forrester Build a Better World -- Tooraj Arvajeh, Chief Engineering Officer, Blocpower Cognitive Analytics and Machine Learning for the Enterprise Town Hall -- Dinesh Nirmal, VP, IBM Analytics Platform Development, Stephanie Mitchko, Chief Technology Officer, Cadent Network, and Randy Halley, Executive Vice President, America First Credit Union Live Product Demonstration on Machine Learning -- Dr. Avijit Chatterjee, STSM Analytics IBM and Daniel Hernandez, Vice President Offering Management, IBM

There are seven key megatrends driving the future of enterprise IT. You can remember them all with the helpful mnemonic acronym CAMBRIC, which stands for Cloud Computing, Artificial Intelligence, Mobility, Big Data, Robotics, Internet of Things, CyberSecurity. In this post we dive deeper into Artificial Intelligence. Artificial Intelligence is the discipline of thinking machines. The field is growing dramatically with the proliferation of high powered computers into homes and businesses and especially with the growing power of smartphones and other mobile devices.