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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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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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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 formas en que el big data analytics puede mejorar el tratamiento de datos de la indemnización del seguro¿Por qué integrar la analítica en el tratamiento de datos de las reclamaciones de seguros? Porque añadir la analítica al ciclo de vida de las reclamaciones puede proporcionar un retorno de la inversión cuantificable.
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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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¿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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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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¡Evita de una vez por todas el fraude en las solicitudes de seguros!Los defraudadores están encantados con lo fácil que es realizar sus transacciones a través de los canales digitales. Las compañías de seguros más inteligentes utilizan datos de esos canales (huella dactilar del dispositivo, dirección IP, geolocalización, etc.) junto con análisis y aprendizaje automático para detectar fraudes en las solicitudes de seguros perpetrados por agentes, clientes y redes de fraude.
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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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Por qué los bancos deben evolucionar su enfoque del riesgo climático y ESG.Gestionar el riesgo medioambiental, social y de gobernanza (ESG) es importante para bancos, reguladores, inversores y consumidores, pero hay muchas interpretaciones sobre cómo hacerlo. Para prosperar, las organizaciones tendrán que hacer evolucionar sus prácticas de gestión de riesgos, incluidas las afectadas por los riesgos ESG.
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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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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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Viking transforms its analytics strategy using SAS® Viya® on AzureViking is going all-in on cloud-based analytics to stay competitive and meet customer needs. The retailer's digital transformation are designed to optimize processes and boost customer loyalty and revenue across channels.
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Top prepaid card fraud scamsThe margin for prepaid cards is slim, so it's particularly important to root out the scams. Here are some tips for combating and mitigating prepaid card fraud.
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Understanding capital requirements in light of Basel IVMany financial firms are already using a popular 2012 PIT-ness methodology for internal ratings-based models. This article examines eight ways the industry is successfully using the methodology – and why this approach can bring synergies for banks, value for regulators, and major competitive advantages.
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Model risk management: Vital to regulatory and business sustainabilitySloppy model risk management can lead to failure to gain regulatory approval for capital plans, financial loss, damage to a bank's reputation and loss of shareholder value. Learn how to improve model risk management by establishing controls and guidelines to measure and address model risk at every stage of the life cycle.
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Detect and prevent banking application fraudCredit fraud often starts with a falsified application. That’s why it’s important to use analytics starting at the entrance point. Learn how analytics and machine learning can detect fraud at the point of application by recognizing the biggest challenge – synthetic identities.
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Strengthen your payment fraud defenses with stronger authenticationThe rapid growth of digital wallets and payment applications ushered in many new payment fraud threats. Today, it’s more critical than ever to authenticate users. Learn four innovative to ways strengthen your authentication defenses while reducing false positives and protecting customers’ assets.
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