SAS features innovations in automated machine learning, computer vision and NLP to improve decision making

Latest SAS® Platform release helps businesses and data scientists put analytics in action by automating complex tasks

SAS, the leader in advanced analytics, is enhancing its automated, simple, and powerful analytics platform to help digital disruptors and emerging leaders blaze a trail forward. The updated SAS^® Platform delivers additional innovation in artificial intelligence (AI), specifically in the areas of machine learning, computer vision, natural language processing (NLP) and other technologies that underpin AI.

Building on its recently announced $1 billion investment in AI, SAS is also refining computer-vision software to help organizations use visual data to improve business outcomes.

“Our continuous innovation, clearly exhibited in the SAS Platform and in SAS AI technologies, propels front-line business, executives and data scientists to change the trajectory of their organizations with advanced analytics,” said SAS CEO Jim Goodnight.

With the latest release of the SAS Platform, SAS automates the complex tasks required to build world-class analytical models. Data cleansing, data transformations, selecting best variables, model building and comparing models, model deployment and retraining tasks are automated while using established best practices.

Operationalizing analytics through interpretability and explainability

The SAS Platform automatically compares thousands of analytical models to help choose the best for a given business problem. Using natural language generation (NLG), analytical results are displayed in plain language, so users of all backgrounds can easily interpret them and make informed business decisions faster.

This helps democratize analytics, as business users and executives can use AI technology along with data scientists and analytics experts, and understand how the analytics arrived at its results.

“Too many companies are caught in AI science-project mode and don’t have the know-how to make the leap to a machine learning model that meaningfully affects business,” said Oliver Schabenberger, SAS Executive Vice President, Chief Operating Officer and Chief Technology Officer. “Thousands of SAS PhDs and data scientists are helping customers with strategies to transform data into intelligence, and our extensive training is helping build skills in organizations. We’re simplifying our technology to help users of all skill levels use powerful AI and ML analysis to innovate. We’re making AI real.”

SAS has simplified computer vision for a wide array of applications. New capabilities like automatic segmentation can, for example, help doctors quickly identify changes in the shape and size of tumors and note their color to better fight disease.

Analytics and AI for everyone

The enhancements to the SAS Platform that make AI more accessible to users of all skill levels include:

• A new project insights area that provides a high-level narrative summary to explain, for non-data scientists, what and how an analysis was performed. SAS is making it easier for business users, data scientists, and IT experts to discuss their models and algorithms. This improved collaboration builds more trust in AI, which leads to greater adoption and more business benefits.
• Enhanced interpretability and explainability of AI models – With NLG capabilities in the SAS platform, users can automatically generate explanations of analytics results in layman’s terms, such as why a transaction was flagged as potentially fraudulent or why specific customers are the best targets for a marketing campaign. These explanations help business analysts and other business users easily understand analytic results, and encourage the real-world use of AI and advanced analytics by a wide variety of users (i.e., not just data scientists).
• Improve decision-making – The SAS Platform works seamlessly with SAS solutions like SAS Intelligent Decisioning to automate and manage decisions across the enterprise.
• Open Application Program Interfaces (APIs) that developers can use to access data and create custom web applications to help business and technical users leverage machine learning, natural language processing and other SAS AI capabilities in an automated way, without users having to understand how to code or use statistics.
• More valuable content embedded in the SAS portfolio – Software like SAS Visual Investigator and the new SAS Mobile Investigator bring the operational and investigative power of SAS^® Viya^®, its machine learning capabilities and other AI technologies to users in the field and on the go. Users can access information from their mobile device and input data from text, documents or photos and the system is updated immediately. This real-time data can also be used to update analytical models and run risk assessments based on new information.

Automating decisions at scale

In today’s fast-paced digital world, businesses must deliver immediate, informed and personalized decisions. SAS Intelligent Decisioning combines business rules management, decision processing, real-time event detection, decision governance and advanced analytics to automate and manage decisions across the enterprise.

SAS Intelligent Decisioning supports customer-facing activities such as personalized marketing and next-best action, as well as decisions affecting customers, including credit services and fraud prevention.

With SAS managing, analyzing and operationalizing high volumes of data to automate countless daily decisions and applying sophisticated analytics to real-time customer interactions, companies can be confident that enterprise decisions are smarter, effective, personalized and timely.

Analytics in action

All these developments are part of SAS’ recently announced $1 billion investment in AI to drive software innovation and AI adoption globally. The investment, over the next three years, will build on SAS’ strong foundation in AI, machine learning, NLP and computer vision to create more powerful and advanced analytics software. SAS is also investing in education programs to equip business leaders and data scientists with the technology and skills they need to boldly embrace AI and AI-driven resources and talent.

In banking, health care and other industries, visionary companies of all sizes have embraced advanced analytics and AI-driven capabilities from SAS to help solve big challenges. Two Dutch hospitals, Amsterdam UMC and University Medical Center (UMC) Utrecht, are improving patient care through computer vision and advanced analytics. And companies like Seacoast Bank are looking to scale their businesses and compete globally.

Detecting tumors faster and more accurately than humans

SAS is partnering with Amsterdam UMC on an AI medical-imaging project to help identify patients with colorectal liver cancer who are candidates for life saving surgery.

Colorectal cancer is the third most common worldwide, and in about half of patients it spreads to the liver. Using SAS AI-trained models Amsterdam UMC physicians will be able to identify with greater accuracy patients who respond well to chemotherapy and become candidates for surgery.

Until now, manual examinations of tumors and lesions were time-consuming and subjective for radiologists. A medical-image application on the SAS Platform, using computer vision and predictive analytics, provides evaluation criteria that are more objective, accurate and automated than the current manual ones. This will provide improved accuracy that can potentially save many more lives, save radiologists time, and provide radiologists with an objective response assessment metric that will help treat patients consistently.

“We are currently using this technology with colorectal liver cancer patients, however in the future it has the potential to be used in the assessment of many solid tumor types, including breast and lung cancer,” said Geert Kazemier, Professor of Hepatobiliary Surgery and Transplantation and Clinical Director of Cancer Center Amsterdam at Amsterdam University Medical Centers, the Netherlands. “In the future, we may be able to predict the outcome of surgery and overall patient survival, and by combining these new capabilities with other data collected from patients, such as genomic data, we can potentially compute a more comprehensive response or survival score.”

Bank enhances CX and grows revenue per customer 30% with SAS^® Machine Learning

Like many financial institutions, Seacoast Bank – one of the largest community banks in Florida with $6.7 billion in total assets - has volumes of customer data. It is challenged to use it wisely to understand customer value and find new ways to better serve, retain and acquire customers.

Since investing in SAS and an AI-powered, machine learning customer analytics platform, Seacoast’s risk-adjusted revenue per customer has grown by 30%, while ROI for automated marketing campaigns is in the high triple-digits. Seacoast added other predictive models and applied machine learning to solve specific business problems, such as personalization at scale, and can now market to individual customers based on their preferences and transaction history.  With analytics insights flowing throughout the bank, Seacoast marketers can automate campaigns, develop deeper relationships with its most valuable customers, and better manage commercial customer portfolios and track performance using interactive dashboards.

“Because we’re now deeply aware of customer value, we can fine-tune our customer strategies and acquisition efforts to generate very high returns and build customer loyalty,” said Jeff Lee, Chief Marketing Officer for Seacoast. “Machine learning and advanced analytics from SAS provides us with a real roadmap for the future, allowing us to access and visualize data, create insights, foster collaboration, deliver personalization at scale, and improve the customer experience.”

From small to scale: Tiny babies and the big impact of AI

Around 10% of all infants are born prematurely, and these tiny children are very vulnerable to infections. To monitor their health, these babies are connected to many devices.

With 10 years of anonymized Neonatology Intensive Care Unit (NICU) patient data, University Medical Center Utrecht in the Netherlands started a Big Data for Small Babies project with a goal to use advanced analytics, machine learning and AI techniques to proactively treat or even prevent infections in premature babies.

Using AI and advanced analytics from SAS, the UMC Utrecht team analyzed the historical data from NICU devices and developed a smart analytical model to predict infections in premature babies. This included sepsis, a severe and often life-threatening blood infection. The model powered by SAS is 90% accurate in forecasting the presence of bacterial infections that can cause sepsis. This is significantly higher accuracy than doctors’ predictions based on a patient’s exams and current symptoms.

“With the power of SAS AI and advanced analytics, UMC Utrecht doctors and nurses can make the best possible decisions for their tiny patients, delivering life-saving treatment to some, while avoiding unnecessary antibiotics for others,” said Daniel Vijlbrief, MD, Neonatologist at UMC Utrecht.

These capabilities and more were demonstrated at this year’s SAS Global Forum, the world's largest analytics conference, with more than 30,000 business and IT users of SAS software participating on-site and online.