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Beyond IFRS 17 – what's next?IFRS 17 is not just another accounting standard. It represents a long-term investment that will pay off for insurers with a clear vision for future goals. Learn how IFRS 17 can provide transparency and insight to an insurance business while identifying strengths and areas for improvement.
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What is a customer data platform?Die Nachfrage an einer Customer Data Platform steigt immer weiter. Erfahren Sie hierzu alles was Sie wissen müssen und wie Sie das Maximum aus Ihrer CDP herausholen.
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AI revolutioniert die Versicherungsbranche: 8 Beispiele dafür, wie es funktioniertDer Einsatz von AI im Versicherungswesen kann den Mehrwert für Kunden, Versicherungsgesellschaften und Stakeholder gleichermaßen steigern. Beispiele hierfür sind eine verbesserte Betrugserkennung, ein optimierter Kundenservice, optimierte Zeichnungsverfahren und Wettbewerbsvorteile. Erfahren Sie mehr darüber, wie AI die Branche transformiert.
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IFRS 17 and Insurance Capital Standards: Insurance regulation meets insurance accounting standardsIFRS 17 and ICS facilitate comparability and transparency for insurers from a regulatory and accounting perspective. Explore their similarities and differences, recognizing how clear communications can help stakeholders navigate regulatory and statutory reporting changes, increase confidence, and help mitigate adverse effects on share prices and ratings.
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AI and humanity: Collaborating to solve global problemsDisplaced communities, the food crisis and natural disasters – these situations affect quality of life, and even the ability to survive. Could artificial intelligence be the unexpected boost humanity needs? Could it accelerate our ability to make a positive, lasting impact? Learn more and read examples of how AI is being used to find solutions to global problems.
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Was ist AI-Modellierung?Bei der AI-Modellierung werden Programme erstellt, die einen oder mehrere Algorithmen verwenden, damit Computer denken, lernen und Ergebnisse vorhersagen können. Ähnlich wie das menschliche Gehirn nehmen AI-Modelle verschiedene Eingabedaten – wie Zahlen, Texte, Bilder, Videos oder Ton – auf, um zu lernen, wie Ergebnisse vorhergesagt oder bestimmte Aufgaben gelöst werden können, ohne dass dabei explizite Anweisungen für jeden Schritt erforderlich sind.
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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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Big Data für Behörden: Wie Daten und Analytics staatliche Programme unterstützenBig Data ist in der Verwaltung von entscheidender Bedeutung, wenn sie analysiert und genutzt werden, um Programme im öffentlichen und privaten Sektor zu verbessern – von der Notfallhilfe bis hin zur Effektivität der Mitarbeiter:innen. Die riesigen Datenmengen, die jeden Tag erzeugt werden, bilden die Grundlage für tiefgreifende Veränderungen bei Behörden auf der ganzen Welt.
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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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Mit synthetischen Daten AI-Durchbrüche ermöglichenIn diesem Artikel beleuchten wir die entscheidende Rolle von synthetischen Daten in unseren daten-hungrigen AI-Initiativen, wie Unternehmen Wachstum mit synthetischen Daten generieren können, und welche ethischen Fragen noch nicht geklärt sind.
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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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Was sind AI-Halluzinationen?Es ist schwer, Fakten und AI-generierte Fiktion voneinander zu trennen. Erfahren Sie, welche Fehler bei Large Language Models auftreten können und wie es in der Folge zu AI-Halluzinationen kommt – und lernen Sie, GenAI verantwortungsvoll zu nutzen.
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Was sind Chatbots?Chatbots sind eine Art dialogfähige künstliche Intelligenz (AI), die dazu konzipiert ist, menschliche Interaktionen mit Computern zu vereinfachen. Erfahren Sie, wie Chatbots in Unternehmen eingesetzt werden und wie sie in Analytics-Anwendungen integriert werden können.
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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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