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The Future is Fintech: 4 Drivers of Change in Financial Services

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The world of financial services has reached a point of no return along the road of digital transformation. A combination of several factors is driving this shift: the rise of big data, consumers' demands for convenient and affordable financial services, and the proliferation of mobile technology are all responsible. Financial services companies must adopt automated, data-drive solutions to successfully compete against new, technology-based entrants. Although the rules surrounding developing fintech are still under construction, its potential to improve operational efficiency, safeguard investments, and fortify cybersecurity, is uncapped. At this point in the game, technology adoption is mandatory for financial institutions.


Accessible Data and Artificial Intelligence: Is this the Future of Fintech?

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Time, finance, and intelligence – all the reasons why AI and accessible data are the future of the FinTech industry. According to the International Data Corporation (IDC), research claims that the world will create approximately 163 zettabytes of data by 2025. To better understand the future of FinTech, you will have to look at the beginning of where it all began: Artificial Intelligence (AI). Today, AI is used on multiple platforms, such as Virtual mobile assistance, chatbots, accessible data, and more. These technologies are already integrated into smartphone mobile devices that can fulfill common tasks as well as solve financial issues.


Cognitive Computing with @JHurwitz at @CloudEXPO #AI #IoT #Cognitive

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What Is the Business Imperative for Cognitive Computing? Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & Associates, Inc., put cognitive computing into perspective with its value to the business.


Why AI will determine the future of fintech

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More investors are setting their sights on the financial technology (Fintech) arena. According to consulting firm Accenture, investment in Fintech firms rose by 10 percent worldwide to the tune of $23.2 billion in 2016. China is leading the charge after securing $10 billion in investments in 55 deals which account for 90 percent of investments in Asia-Pacific. The US came second taking in $6.2 billion in funding. Europe, also saw an 11 percent increase in deals despite Britain seeing a decrease in funding due to the uncertainty from the Brexit vote.


How financial institutions can get started with AI today

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If 2016 was the year artificial intelligence (AI) became a buzzword for the financial services industry (among many others), then 2017 has been more a year of introspection. AI is not yet a magic switch, particularly for financial institutions (FIs) where data access and security play a critical role. We're only at the beginning of this new age of computing which holds the potential to transform the entire working of a bank. In this context, what can banks and FIs do today to get started with AI? The applications or use cases of AI within a FI are numerous, ranging from front and middle to back office, and can vary in value.


Robert Cohen Joins @CloudEXPO NY Faculty @ExpoDX #IoT #IIoT #FinTech #DevOps #SmartCities

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Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups. As a result, many firms employ new business models that place enormous importance on software-based innovations. They require not only skilled occupations, such as data analysts and DevOps professionals, with more technical skills, but also middle-level employees with more software and computing acumen.


Lessons from Game of Thrones: Stopping the White Walkers of Data Monetization @ThingsExpo #IoT #M2M #BigData

@machinelearnbot

As we end 2017, I'm tired of writing "lecturing" blogs about what organizations should be doing to master data monetization in order to power their business models and achieve digital transformation. While the objective of every organization should be to master big data and data science (artificial intelligence, machine learning, deep learning) to drive "data monetization," let's take a breath and have some fun. My recent ankle surgery afforded me the opportunity to binge watch "Game of Thrones." As I watched the impending battle between the White Walkers and humanity, I couldn't help but identify a number of lessons that we can learn from Jon Snow's battle with the leader of the White Walkers…and the power of Valyrian steel! Game of Thrones and data, not exactly two things you think are in harmony, but this is where I find myself.


Machine Learning in Fintech - Demystified

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– Big data helps to make strategy for future and understand user behaviors. In 1959, Arther Samuel gave very simple definition of Machine Learning as "a Field of study that gives computer the ability to learn without being explicitly programmed". Now almost after 58 years from then we still have not progressed much beyond this definition if we compare the progress we made in other areas from same time. The idea of FinTech adopting some best practices from the Big Data and AI (Artificial Intelligence, Machine Learning and Deep Learning) is not so new, have you heard of accepting selfie as authentication for your shopping bill payment, Siri on your iPhone etc. A Decentralized Autonomous Organization (DAO) is a process that manifests these characteristics. It's code that can own stuff. Self-driving car is an excellent example for this. What if you use blockchain to store the state of machine. The key move for blockchain-enabled thinking is that instead of having just one instance of a memory, there could be arbitrarily many copies of a memory, just as there can be many copies of any digital file.


Five Hospitality Predictions for 2018 @CloudExpo #AI #ML #Cloud

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The hotel and hospitality industry, enabled with advanced technology and more collaboration with associated businesses, will see some important trends in 2018 as hotel brands reinvent themselves to cater to a new type of clientele. Millennial guests will dominate the landscape, and reshape the industry with demands for more automated options and conveniences and the ability to do everything from a smartphone, and hotels - eager to deliver more conveniences to this younger audience - will forge closer alliances with retailers and community destinations. Hotel guests will place increasing priority on booking rooms that are totally connected, fully automated, and with an ability to seamlessly check in, check out, and access services, all through a smartphone app and without the need for human interaction. Human travel agents have long since gone out of style, and online booking platforms provide a more convenient and often less expensive option. "In 2018, we will continue to see hotel booking platforms continue to advance," said Chris Rivett, travel expert at HotelsCombined.com.


Robotic Process Automation and Workload Automation @CloudExpo #AI #ML #DX

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Robotic process automation (RPA), a concept that has emerged over recent years, is still in a state of rapid evolution, existing without a clearly defined end-state or direction. As such, vendors are experimenting and pushing their products into uncharted waters - successfully or otherwise. Nonetheless, we can be sure that artificial intelligence and machine learning will continue to develop and impact on automation solutions as whole, even if at the moment these capabilities do not frequently exist within the RPA space. RPA is in fact most commonly used to emulate keystrokes; it runs through the application interface and its processes are defined using demonstrable steps - one of its selling points is that these rules do not require code and can be taught by a non-technical end-user. To put it simply, RPA manipulates existing software applications by imitating human behavior through rule-based tasks.