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Government procurement fraud: Detecting risk, protecting fundsProcurement fraud, waste and abuse cause an average loss of $1.4 million annually per organization, making it rank as one of the top risk concerns among government leaders. A proven way to stay ahead of emerging risks is with continuous monitoring built on automated analytics. Learn what drives procurement fraud, how to spot the warning signs, who commits this type of fraud, and how to close gaps in your procurement risks.
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Insurance customer retention in the AI ageInsurance customer retention has shifted with AI. To enable growth, insurance companies must reduce churn, keep policyholders renewing coverage over time, and protect policies in force. Retaining loyalty now requires insurers to lean into new technological capabilities. See what the "96% rule" means, how our customers are breaking free of the customer retention hole, and the role analytics and AI play.
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Quantum AI examplesQuantum AI has seemed theoretical for years. But it’s gaining momentum in real-world applications. Explore quantum AI examples across financial services, insurance, health care and manufacturing to see how problems beyond classical AI’s reach are solved. And learn about hybrid quantum-classical approaches, where quantum systems and classical computing work together, each handling the parts of a problem they solve best.
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4 strategies to change your approach to fraud detectionFraud detection methods must evolve as fraud threats continue to evolve rapidly. See how financial institutions are using analytics and AI to refine their fraud strategies – and discover four ways to improve your organization's risk posture.
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Stemming the tide of VAT fraudValue-added tax (VAT) fraud is a constant pressure on global tax compliance and revenue collection. Even with major strides in digital tax administration, the complexity of VAT systems still creates space for sophisticated schemes to take hold. Governments and tax authorities are stepping up by using advanced analytics, cross-border collaboration and real-time data integration to spot fraud earlier, respond faster and close VAT gaps.
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Trustworthy AI: How to move from ambition to ROITrustworthy AI is artificial intelligence technology that is designed, developed and deployed with human-centricity and other core principles in mind. Learn what it means to trust AI in the face of constant, real-world business pressures. And see why treating trustworthy AI as a repeatable operating model provides a long-term business advantage – and financial returns.
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Containing health care costs To improve payment integrity processes, healthcare organizations rely on trusted analytics to contain costs, reduce administrative burdens, detect fraud in real time, and improve access and quality of care. In addition to data-driven analytics, rapid evolution in computer vision, document vision and text analytics have made it possible.
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Is your customer engagement platform really engaging? As decisioning and orchestration become the convergence point within the MarTech stack, the middle layer is where it all comes together. It brings together data, decisioning and activation to enable real-time, personalized customer engagement across channels. Uncover the essential features of a modern customer engagement platform and why prioritizing the middle layer is vital for marketers.
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Benefits of cloud services for analytics and AIThe benefits of cloud services include flexibility, efficiency and strategic value. Learn how cloud services and managed cloud work – including how they help organizations move faster, control costs and modernize legacy systems. Dig into their advantages, including core infrastructure components, delivery models and the strategic value of scalability, security and cloud sovereignty.
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Accelerating innovation with smarter AI hardwareAI is now woven into strategic objectives, and the energy efficiency of AI hardware directly affects operational expenses, geographic decisions and ESG commitments. But AI hardware systems in their current form and at their current growth rate are not sustainable. Leaders must begin to recognize hardware choices not as a technical detail but as a key part of their strategy.
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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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What is a risk model?Banks use multiple models to meet a variety of regulations (such as IFRS 9, CECL and Basel). With increased scrutiny on model risk, bankers must establish a model risk management program for regulatory compliance and business benefits. Begin the planning by clearly defining what a risk model is.
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AI in government: The path to adoption and deploymentGovernments hold rich data and rising public expectations. Now’s the time to harness artificial intelligence (AI) and shape the future of AI in Government.
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Intelligent policing: Are data analytics systems the key to public safety?Police agencies are navigating rapidly changing landscapes, rising data volumes, evolving threats, and growing expectations for transparency and coordination. They can't keep pace if they're dependent on siloed data, aging platforms and legacy practices. Intelligent policing can help. This evolution from reactive to problem-oriented policing relies on shared data, visualization and a scalable analytics ecosystem.
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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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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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La IA y la humanidad: una colaboración para resolver los grandes retos del mundoLas comunidades desplazadas, la crisis alimentaria mundial y los desastres naturales no solo afectan a la calidad de vida, sino también a la capacidad de supervivencia. ¿Y si la inteligencia artificial fuera el impulso inesperado que la humanidad necesita? ¿Y si pudiera ayudarnos a lograr un cambio positivo y duradero? Descubra la forma en que la IA está contribuyendo a encontrar soluciones a los grandes retos del mundo.
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Cinco tecnologías de IA¿Cuál es la diferencia entre la inteligencia artificial y el aprendizaje automático? ¿Podría explicar los conceptos básicos de tecnologías como la IA generativa (GenAI) y los agentes de IA? Lea nuestra información general para entender estas y otras tecnologías que están detrás del auge de la inteligencia artificial.
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La IA transforma los seguros: ocho ejemplos de su funcionamientoEl uso de IA en seguros puede aumentar el valor para los clientes, las compañías de seguros y las partes interesadas por igual. Los ejemplos van desde una detección de fraude más fuerte y un servicio al cliente mejorado hasta procesos de suscripción optimizados y ventajas competitivas. Descubre más información sobre cómo la IA está transformando la industria.
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¿Qué son los modelos de IA?La modelización de IA implica la creación de programas que utilizan un algoritmo o una combinación de algoritmos para permitir a los ordenadores pensar, aprender y predecir resultados. Al igual que el cerebro humano, los modelos de IA absorben datos de entrada —números, textos, imágenes, vídeo, sonido— para aprender a predecir resultados o resolver tareas específicas sin instrucciones explícitas en cada paso.
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Así funciona la tecnología gemelos digitalesDescubra cómo la tecnología gemelos digitales está transformando sectores como la salud y la fabricación, y cómo puede marcar el futuro del modelado de datos y la optimización de procesos.
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Big data en el sector público: cómo los datos y el análisis impulsan los programas públicosEl big data en las administraciones públicas es vital cuando se analiza y utiliza para mejorar los resultados de los programas de los sectores público y privado, desde la respuesta a emergencias hasta la eficiencia del personal. Los enormes volúmenes de datos que se crean a diario son la base de cambios importantes para las agencias gubernamentales de todo el mundo.
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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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Ansiedad frente a la IA: mantener la calma ante el cambioLa ansiedad frente a la IA no es ninguna broma.— Tanto si temes que tu trabajo se quede obsoleto, que la información se distorsione o simplemente perderte algo, comprender la ansiedad que provoca la IA puede ayudarte a vencerla.
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Datos sintéticos para impulsar avances de IADescubre por qué los datos sintéticos son vitales para las iniciativas de IA, cómo los utilizan las empresas para desbloquear el crecimiento y cómo pueden ayudar a abordar los retos éticos.
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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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Edge computingTraditional methods capture, store and analyze data later, slowing response times. With edge computing, IoT data is processed at the edge of a network where it’s created or collected – avoiding delays and enabling real-time processing and action.
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¿Qué son las alucinaciones de IA?Separar la realidad de la ficción generada por la IA puede ser difícil. Aprende cómo los grandes modelos lingüísticos pueden fallar y conducir a alucinaciones de IA y descubre cómo utilizar la GenAI de forma responsable.
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Data lake and data warehouse – know the differenceIs a data lake just marketing hype, or a new name for a data warehouse? Find out what a data lake is, how it works and when you might need one.
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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, insurers, regulators, investors and consumers – yet there are many interpretations of how to do it. To thrive, organizations must learn how to evolve their risk management practices.
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¿Qué son los chatbots?Los chatbots son una forma de IA conversacional diseñada para simplificar la interacción humana con las computadoras. Aprende cómo se utilizan los chatbots en las empresas, cómo pueden incorporarse a las aplicaciones analíticas
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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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