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Operationalizing analytics: 4 ways banks are conquering the infamous ‘last mile’Here are four examples across the banking industry that show how these leading organizations followed a clearly defined path to conquer the infamous 'last mile' of analytics.
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ModelOps: How to operationalize the model life cycleModelOps is where analytical models are cycled from the data science team to the IT production team in a regular cadence of deployment and updates. In the race to realizing value from AI models, it’s a winning ingredient that only a few companies are using.
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AI in government: The path to adoption and deploymentThe government sector is lagging in AI adoption, but awareness of the importance of AI in the public sector is increasing. Our survey indicates that operational issues are requiring governments to turn their attention to AI projects as a way to address important public issues.
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Five trends that will reshape customer experienceApplying the latest research from HBR gives a new twist on this surprisingly relevant 2015 list of CX do’s and don’ts.
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Under siege: Improving customer experience in bankingBanks are ranking low in customer satisfaction, but improvement is possible says Digital Banking Report owner and publisher Jim Marous.
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From Apollo to AI: A new era of American explorationAs we celebrate the 50th anniversary of the Apollo 11 mission, what’s the next frontier for American Innovation? It’s available now, from our desks and waits for us to unlock its potential.
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Next generation anti-money laundering: robotics, semantic analysis and AIAnti-money laundering taken to it's next level is sometimes referred to as AML 2.0 or AML 3.0. What does this next wave of AML technology look like? What can it do that you can’t do with traditional AML? See the results innovative financial institutions around the globe are already getting.
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The untapped potential in unstructured textText is the largest human-generated data source. It grows every day as we post on social media, interact with chatbots and digital assistants, send emails, conduct business online, generate reports and essentially document our daily thoughts and activities using computers and mobile devices.
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AI in manufacturing: New opportunities for IT and operationsAn AI survey reveals that leaders and early adopters in AI are making important advances and are identifying and expanding on what works as they use AI in more ways and more parts of their organizations.
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The Humanity in Artificial IntelligenceCould artificial intelligence be the change agent we need to solve many problems around the globe? Read how AI could accelerate our ability to have a a positive, lasting impact.
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The state of insurance fraud technologyA 2019 Coalition Against Insurance Fraud study surveyed 84 companies on their use of anti-fraud technologies and compared results to 2014 and 2016. Get the highlights here.
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Edge computingWith traditional methods, data is captured, stored and analyzed later – limiting how quickly businesses can act on insights from the data. With edge computing, IoT data is processed at the edge of a network – right where it’s created or collected – avoiding delays and enabling real-time processing and action.
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How AI and advanced analytics are impacting the financial services industryTop SAS experts weigh in on the topics that are keeping institutions up at night and fraudsters in a job.
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AI in banking: Survey reveals factors for successWhat do banking executives report about their experiences with AI? Where are they focusing today? What’s working? What are their plans for the future?
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What are chatbots?Chatbots are a form of conversational AI designed to simplify human interaction with computers. Learn how chatbots are used in business and how they can be incorporated into analytics applications.
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IoT: The customer experience accelerator you can't afford to ignoreIoT represents a powerful source of data that, when combined with analytics, can yield insights on everything from behavior to emotions to health. And that's why it's key to improving customer experience.
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Data lineage: Making artificial intelligence smarterFor AI to reach its full potential, the data feeding its algorithms and models needs to be well-understood. Data lineage plays a vital role in understanding data, making it a foundational principle of AI.
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How to drill a better hole with analyticsFrom drilling holes to preventing health care fraud, learn about some of the new technologies SAS has patented with IoT and machine learning technologies.
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IFRS 17: Waiting is not an optionIFRS 17 is a principles-based accounting standard for the future-oriented valuation of insurance contracts. Designed to increase financial transparency, IFRS 17 requires insurers to report in more detail on how insurance and reinsurance contracts affect their finances and risk.
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IT/OT convergence: The dilemma of the IoT perception gapThe IoT phenomenon demands intimate collaboration between information technology (IT) and operational technology (OT). But IT and OT have distinct gaps in perception about the goals and outcomes of IoT initiatives. Tom Bradicich explains why IT/OT convergence is essential for successful IoT projects.
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Five AI TechnologiesDo you know the difference between artificial intelligence and machine learning? And can you explain why computer vision is an AI technology? Find out in this short explainer.
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IoT in healthcare: Unlocking true, value-based careGiven the potential of IoT – and the challenges of already overburdened healthcare systems around the world – we can’t afford not to integrate IoT in healthcare.
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Key questions to kick off your data analytics projectsThere’s no single blueprint for starting a data analytics project. Technology expert Phil Simon suggests considering these ten questions as a preliminary guide.
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5 ways to become data-drivenSuccessful data-driven businesses foster collaborative, goal-oriented cultures, have leaders who believe in data and are governance-oriented. Read more in this summary of TDWI research that uncovers best practices for becoming data-driven.
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Analytics: A must-have tool for leading the fight on prescription and illicit drug addictionStates and MFCUs now have the analytics tools they need to change the trajectory of the opioid crisis by analyzing data and predicting trouble spots – whether in patients, prescribers, distributors or manufacturers. The OIG Toolkit with free SAS® programming code makes that possible.
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A guide to machine learning algorithms and their applicationsDo you know the difference between supervised and unsupervised learning? How about the difference between decision trees and forests? Or when to use a support vector algorithm? Get all the answers here.
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Modern manufacturing's triple play: Digital twins, analytics, IoTUsing exact duplicates to manage complex systems dates to NASA’s early moon missions. Today, IoT is key to implementing digital twin technology in manufacturing.
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Game-changing technologies turn IIoT data into goldThe real value of IoT lies in its data. Maciej Kranz says technologies like edge and fog computing, machine learning and AI can unlock the hidden value in data from the IIoT.
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Anti-money laundering and counter-terrorist financingTo monitor for financial crimes, an essential element for both anti-money laundering and counter-terrorist financing is using data analysis to detect unusual activity during processing by monitoring transactions, customers and the network of behaviors.
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The analytical CRO and the risk aware CFOTo create a more risk-aware organization, the most important collaborative relationship for the CRO is with the CFO and the finance team. The CFO and CRO – as the executives responsible for budgeting and supervision – tend to get caught in the middle of competing objectives.
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Big data in educationA recent MIT Sloan Management report highlights how businesses are using analytics as a source of innovation -- so are universities, says SAS' Georgia Mariani.
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Effective real-time marketingEffective real-time marketing is about engaging with customers whenever and wherever they are in their buying journey. Organizations should also be focused on knowing what the customer wants (product, pricing, experience, etc.) based on real-time analytics and predictive modeling.
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