SAS® Insights
News and views from the analytics experts
Most recent articles
-
- Select a topic
- Analytics
- Artificial Intelligence
- Business Intelligence
- Cloud
- Customer Intelligence
- Data for Good
- Data Management
- Fraud and Security Intelligence
- Internet of Things
- Machine Learning
- Risk Management
-
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.
-
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.
-
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.
-
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.
-
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.
-
The best gift you can give to thieves this holiday season? Your identity.While the use of EMV in cards has helped to mitigate fraud perpetrated at retail stores, undeterred fraudsters have focused their efforts online. Find out how advanced analytics and machine learning help combat this threat.
-
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.
-
What big data has brought to the privacy discussionHow does big data impact your privacy? There are ways to balance privacy and security in an increasingly transparent and dangerous world.
-
Bringing data to the streamHow much do we know about fresh water systems and the dynamic nature of streams and rivers? Find out how one data scientist turned his fascination with streams and rivers into a career.
-
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.
-
Scenario stress testing: Beyond regulatory complianceScenario stress testing offers banks a way to simulate responses to a financial crisis using a wide range of conditions and levels of severity.
-
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.
-
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.
-
Analytics can help prevent substance use disorder and over-prescribingStates 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.
-
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.
-
Detect and prevent banking application fraudSince credit fraud often starts with a falsified application, it makes sense to have analytics-driven tools in place to detect fraud from the earliest point and across the life of the account.
-
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.
-
Machine learning, Michael J. Fox and finding a cure for Parkinson’sUsing machine learning, data scientists developed a model that can help doctors accurately predict Parkinson's disease progression and start treatment earlier, when it will have greater impact.
-
Medicaid and benefit fraud in 2018 and beyondTo curb the growing amount of Medicaid and benefit fraud and improper payments, agencies and their commercial counterparts need fraud and abuse detection systems with data management and analysis that can keep up and even stay one step ahead.
-
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.
-
Strengthen your payment fraud defenses with stronger authenticationThe rapid growth of digital wallets and payment applications has ushered in new payment fraud threats to consumers and organizations. Find out how the use of innovative technologies to combat payment fraud is a viable and effective solution with additional benefits.
-
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.
-
GDPR and AI: Friends, foes or something in between?The GDPR may not be best buddies with artificial intelligence – but GDPR and AI aren't enemies, either. Kalliopi Spyridaki explains the tricky relationship between the two.
-
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.
-
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.
-
Seven tips for creating a self-service BI governance strategySelf-service BI and IT governance – sometimes the two seem at odds. Can they coexist peacefully? Live happily ever after? TDWI thinks so. They offer seven tips for creating a strategy that works for both.
-
Personal data: Getting it right with GDPRTo learn more about the definition of personal data, why it’s in the news and why it’s being tightly regulated by laws like the General Data Protection Regulation (GDPR), we interviewed Jay Exum, Privacy Counsel at SAS.
-
Preventing domestic violence with wearablesAfter her sister became a victim of domestic violence, Kimberly Calhoun developed a wearable that collects data and reports on the offender's location in real-time, alerting police and protecting victims.
-
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.
-
Big data in government: How data and analytics power public programsBig data generated by government and private sources coupled with analytics has become a crucial component for a lot of public-sector work. Why? Because using analytics can improve outcomes of public programs.
-
Analytics startup focuses on health care optimizationAnalyzing vast amounts of data is key to optimizing health care outcomes, improving patient satisfaction and lowering total costs.
-
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.
Send SAS Insights straight to your inbox
