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Five AI technologiesDo you know the difference between artificial intelligence and machine learning? Can you explain the basics of AI technologies like GenAI and AI agents? Read our overview to understand these and other technologies behind the AI craze.
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AI transforms insurance: See 8 examples of how it worksUsing AI in insurance can boost value for customers, insurance companies and stakeholders alike. Examples range from stronger fraud detection and improved customer service to optimized underwriting processes and competitive advantage. Learn more about how AI is transforming the industry.
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What is AI modeling?AI modeling involves creating programs that use one or a combination of algorithms to allow computers to think, learn and predict outcomes. Much like a human brain, AI models absorb input data – numbers, texts, images, video, sound – to learn how to predict outcomes or solve specific tasks without explicit instructions at every step.
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Understanding digital twin technologyLearn how digital twin technology can change industries from health care to manufacturing, and shape the future of data modeling and process improvements.
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What is synthetic data? And how can you use it to fuel AI breakthroughs?There's no shortage of data in today's world, but it can be difficult, slow and costly to access sufficient high-quality data that’s suitable for training AI models. Learn why synthetic data is so vital for data-hungry AI initiatives, how businesses can use it to unlock growth, and how it can help address ethical challenges.
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Big data in government: How data and analytics power public programsBig data in government is vital when analyzed and used to improve the outcomes of both public and private sector programs – from emergency response to workforce effectiveness. The vast volumes of data created every day are the foundation of insightful changes for government agencies across the globe.
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Unlocking a strategic approach to data and AIAI is only as good as the data that powers it – this is a fundamental truth about data and AI that defines the limits of what’s possible with artificial intelligence. It may seem surprising, but it's rarely a bad algorithm or a bad learning model that causes AI failures. It's not the math or the science. More often, it's the quality of the data being used to answer the question.
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AI anxiety: Calm in the face of changeAI anxiety is no joke. Whether you fear jobs becoming obsolete, information being distorted or simply missing out, understanding AI anxiety can help you conquer it.
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Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Discover what you’ll need to get started defending against fraud – from integrating supervised and unsupervised machine learning in operations to maintaining customer service.
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6 ways big data analytics can improve insurance claims data processingWhy make analytics a part of your insurance claims data processing? Because adding analytics to the claims life cycle can deliver a measurable ROI.
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What are AI hallucinations?Separating fact from AI-generated fiction can be hard. Learn how large language models can fail and lead to AI hallucinations – and discover how to use GenAI responsibly.
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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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Data quality management: What you need to knowData quality isn’t simply good or bad. Data quality management puts quality in context to improve fitness of the data you use for analysis and decision-making.
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Next generation anti-money laundering: robotics, semantic analysis and AIAdding AI and machine learning to your anti-money laundering program can combat rising compliance pressures, complexity and risks. Learn about the results innovative financial institutions around the globe are getting from next-generation AML.
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A data scientist’s views on data literacyData literacy is a social imperative – and understanding data and data analysis is critical for being a responsible citizen. Get a data scientist and teacher's perspective on the value of having foundational knowledge so you can more easily tell data facts from data fiction.
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How AI and advanced analytics are impacting the financial services industryTop SAS experts weigh in on topics that keep financial leaders up at night – like real-time payments and digital identity. See how advanced analytics and AI can help.
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Intelligent policing: Data visualization helps crack down on crimeLearn how data visualization can give police real-time views of locations enriched with other data to help them make intelligent, fact-based decisions.
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Shut the front door on insurance application fraud!Fraudsters love the ease of plying their trade over digital channels. Smart insurance companies are using data from those channels (device fingerprint, IP address, geolocation, etc.) coupled with analytics and machine learning to detect insurance application fraud perpetrated by agents, customers and fraud rings.
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4 strategies that will change your approach to fraud detectionAs fraudulent activity grows and fighting fraud becomes more costly, financial institutions are turning to anti-fraud technology to build better arsenals for fraud detection. Discover four ways to improve your organization's risk posture.
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Modern manufacturing's triple play: Digital twins, analytics & IoT IoT-powered digital twins revolutionize manufacturing with real-time data analysis, predictive maintenance and optimized production. Discover their transformational impact.
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Public health infrastructure desperately needs modernizationPublic health agencies must flex to longitudinal health crises and acute emergencies – from natural disasters like hurricanes to events like a pandemic. To be prepared, public health infrastructure must be modernized to support connectivity, real-time data exchanges, analytics and visualization.
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Why banks need to evolve their approach to climate and ESG riskManaging environmental, social and governance (ESG) risk is important to banks, regulators, investors and consumers – yet there are many interpretations of how to do it. To thrive, organizations must evolve their risk management practices – including those affected by ESG risk.
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Analytics leads to lifesaving cancer therapiesA long-shot treatment offers hope to 10-year-old Harrison after he learns the DNA profile of his cancer is resistant to chemo. Find out how data and analytics play a role in cancer research and cancer treatments that are saving lives.
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Know your blind spots in tax fraud preventionTax agencies sometimes miss fraud that's happening right under their noses – despite robust external fraud prevention efforts. Find out where traditional tax fraud prevention and detection efforts fall short, and how analytics can change that.
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Are you covering who you think you’re covering? Payers often don't focus enough on healthcare beneficiary fraud in public and private healthcare plans. Before paying a claim, payers need to ensure beneficiaries are eligible. Advanced analytics applied to a broad range of data can help them accurately detect and prevent beneficiary fraud.
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The importance of data quality: A sustainable approachBad data wrecks countless business ventures. Here’s a data quality plan to help you get it right.
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Containing health care costs: Analytics paves the way to payment integrityTo ensure payment integrity, health care organizations must uncover a broad range of fraud, waste and abuse in claims processing. Data-driven analytics – along with rapid evolutions in the use of computer vision, document vision and text analytics – are making it possible.
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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 these 10 questions as a guide.
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Analytics tackles the scourge of human traffickingVictims of human trafficking are all around us. From forced labor to sex work, modern-day slavery thrives in the shadows. Learn why organizations are turning to AI and big data analytics to unveil these crimes and change future trajectories.
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10 ways analytics can make your city smarter From child welfare to transportation, read 10 examples of analytics being used to solve problems or simplify tasks for government organizations.
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Risk data infrastructure: Staying afloat on the regulatory floodWhat are the challenges of a risk data infrastructure and how can they be addressed? Here's what you need to know to build an effective enterprise risk and finance reporting warehouse that will effectively address compliance requirements.
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Are you good at scoring?Credit scoring is the foundation for evaluating clients who apply for a loan (or other types of exposure for the bank). It is not unusual for it to take up to 12 months to build and deploy a new credit scoring model. Reforming the process will help minimize losses, increase earnings and reduce operational risk.
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