SAS, the leader in analytics, is enhancing its easy-to-use artificial intelligence (AI) solutions to help organisations improve efficiency and quickly realise value with automation. The updated SAS^® Platform delivers new functionality including automated data management, automated machine learning and cutting-edge interpretability features, underscoring SAS’ commitment to making AI more transparent and accessible for all.

Available in the fourth quarter of 2019, the newest release of SAS^® Viya^® on the SAS Platform offers the latest AI and advanced analytics techniques, accessible to both data scientists and business users. The enhancements provide an intelligent process to automate many of the manual and complex steps required for data transformations and to build machine learning models. SAS automates the analytics life cycle – from data wrangling to feature engineering and algorithm selection – in a single click.

To add a layer of transparency, the software will dynamically produce a visual pipeline to eliminate the black box that can accompany automation. And through natural language generation, results are presented in easily understood business terms. Once a model is finalised, it can be deployed with a single click.

To further democratise AI solutions, the automated modeling process uses a REST API. This helps developers customise business applications while using SAS Analytics. Additionally, users can easily embed open source code and augment their analysis with SAS, providing a truly open experience. Machine learning is also used to recommend data transformations to reduce data preparation time.

UK Insurance provider Admiral fights fraud with AI

Fraudulent claims and applications are a major problem for the UK insurance industry, with both insurers and genuine customers paying the price through increased premiums. According to Cifas, the UK’s leading fraud-prevention service, false insurance claims rose by 27% in 2018 from the previous year, with auto/motor seeing a 45% increase.

Admiral, a UK-based insurance company, has developed fraud portals that eliminate the need for manual referrals, saving significant time and resources while detecting more fraud than ever before. Fraud investigators and analysts now work from a single centralised hub, sharing data across the organisation and applying sophisticated analytics to detect and prevent fraud. This approach has delivered more than £31 million of benefit in the last 12 months alone, including £6 million in savings for claims fraud.

“We used an insurance fraud analytical engine from SAS to apply multiple techniques – including automated business rules, machine learning, artificial intelligence, text mining, database searches, anomaly detection and network-link analysis – to automatically score claims, associated entities and any corresponding social networks,” explained Sarah Lang, Head of Business Analytics at Admiral. “This process has allowed us to build a strong relationship between analytics and claims fraud. A continuous feedback loop ensures we continue to update the process, identifying fraud in more cases in a faster way, whilst improving our customers’ experience.”

Industry disruption powered by AI

Italian startup Yolo is disrupting the insurance industry with help from SAS AI solutions. The company’s digital insurance platform provides on-demand access to policies for travel, products, health and pets. Fueled by SAS machine learning, Yolo’s Platform can customise a temporary insurance policy offer to a customer without lag time.

“As a digitally native company, our customer’s experience is paramount,” said Gianluca De Cobelli, co-founder and CEO of Yolo Group. “With SAS, our Platform is able to process a customer’s request to insure their vacation or their smartphone using all available data in real time, allowing our financial institution and corporate partners to provide their clients with a customised and dynamic mobile experience.” 

A continued partnership with IBM

For the first time, the November release of SAS Viya will run on the IBM POWER9 chip architecture. This will support all SAS Viya functionalities including GPU acceleration for machine learning, deep learning, and AI training and inferencing. In addition, this expands the flexibility to run SAS workloads in any cloud platform.

“SAS and IBM have collaborated for more than 40 years. We’ve solved some of the most complex algorithmic challenges together. IBM Power Systems offer value to mutual customers whose business challenges demand not only SAS analytics, but also high data throughput,” said Ken Gahagan, Senior Director of Compute Services for Research & Development at SAS.

The future in AI: Growth and opportunity

“SAS continues to innovate in AI, building on the $1 billion investment in AI we announced earlier this year,” said Saurabh Gupta, Director of Advanced Analytics & Artificial Intelligence at SAS. “SAS delivers AI that helps users manage, understand and analyse the data they have and make better, faster decisions with it. The latest enhancements to our AI offerings focus on automating the many manual and complex steps required to build machine learning models.”

The investment will focus on current and future R&D and expert services to build on successful AI efforts like that of Admiral and Yolo over three years. It will also include SAS education initiatives that address customer and partner needs to better understand and benefit from AI.

In 2018, SAS experienced 105% growth in AI revenue – a rate three times faster than the overall market – according to a recent IDC report on AI software platform market share.

Today’s announcement was made at the Analytics Experience conference in Milan, Italy, a business technology conference presented by SAS that brings together thousands of attendees on-site and online to share ideas on critical business issues.

SAS is No. 1 in advanced and predictive analytics market share, says analyst report

ANALYTICS EXPERIENCE, MILAN (Oct 22, 2019) – SAS again ranks number one for market share, according to the IDC report, Worldwide Big Data and Analytics Software 2018 Market Shares: Demand Across All Use-Case Patterns.^[1]

SAS led with a 27.7% market share in the advanced and predictive analytics category in 2018, more than twice that of the next competitor. SAS has led in the predictive and advanced analytics category since IDC started tracking the market in 1997 and has shown continued revenue growth in the category each year.

“As the volume and complexity of data grows, and as organisations seek to make better, faster business decisions, the need for advanced and predictive analytics like those from SAS is expanding,” said Chandana Gopal, Research Manager for Business Analytics at IDC. “SAS is the leader in advanced and predictive analytics market share and has experienced continued growth year over year.

“SAS continues to innovate in AI and the technologies that underpin it like machine learning, natural language generation and computer vision. The analytics leader remains laser-focused on meeting the market’s evolving need for powerful analytics to transform data into value.”

SAS reinvests into R&D more than twice the average for major technology firms. In 2018, SAS devoted 26% of its overall revenue into developing artificial intelligence (AI), IoT, data management and analytics software. To continue fostering innovation and progress in analytics, SAS has committed to invest $1 billion in AI over the next three years.

“Our continuous innovation, clearly exhibited in the SAS Platform and in SAS AI technologies, propels front-line managers, executives and data scientists to change the trajectory of their organisations with advanced analytics,” said Jim Goodnight, CEO of SAS. “Whether it’s providing teams of data scientists with advanced machine learning capabilities or delivering analytics that give decision makers real-time answers, SAS is committed to helping put data and analytics to work, making them accessible to all types of users and driving value to an organisation’s bottom line.”

Additionally, SAS ranks second in AI software market share according to the IDC report, Worldwide Analytic Artificial Intelligence Software Platform Market Shares, 2018: Steady Growth – Moving Toward Production (Doc #US45262419, June 2019). The report notes that while the overall AI market saw steady growth last year, SAS experienced growth at a rate more than three times faster than the overall market at 104.6%.

The SAS® Platform provides a strong analytics foundation and a single place to manage and support every phase of an organisation’s analytics journey, from initial gathering and preparing of data, through development, management and enterprise deployment of powerful analytical models. An integrated part of the SAS Platform, SAS® Viya® is a cloud-enabled, in-memory analytics engine that provides quick, accurate and reliable analytical insights.

Read about how SAS customers succeed with advanced analytics.

Today’s announcement was made at the Analytics Experience conference in Milan, Italy, a business technology conference presented by SAS that brings together thousands of attendees on-site and online to share ideas on critical business issues.

SAS moves organisations’ open source models beyond the lab to enable smarter, faster decisions

New solution streamlines analytical model management to address ‘last mile’ business challenge 

ANALYTICS EXPERIENCE, MILAN (Oct. 22, 2019) – The accelerated adoption of AI and machine learning, paired with the accessibility of open source software has data scientists churning out more analytical models than ever. However, there hasn’t been a corresponding increase in business value since few models make it out of the lab and into production. SAS, the leader in analytics, wants to change this. With the release of SAS^® Open Model Manager, SAS is helping organisations operationalise open source models and put their data to work for smarter, faster business decisions.

Many organisations struggle to complete the last mile of analytics, in part because of cumbersome manual processes and inconsistent collaboration between IT and business users. The burden of moving models from development to deployment is significantly eased by improving model development, production and automation.

An IDC survey noted that less than half of organisations can claim that their analytical models are sufficiently put to work, and only 14% say that the output of data scientists is fully operationalised.* SAS Open Model Manager helps organisations streamline the process for analytical models to go quickly from the lab into production, and closely monitors and revalidates the performance of these models.

“Organisations have a good handle on building and training analytical models, including open source ones, but there is often a gap when it comes to operationalising those models and pushing them into production, and a lot of the work done by data scientists is lost,” said Chandana Gopal, Research Director, Business Analytics at IDC. “There is a need in the market for a new generation of model management solutions that allow data scientists to develop models in any language of their choice, and to properly catalog and deploy their analytical models. With this capability organisations can harness the value of their analytical assets and improve transparency through continuous monitoring.”

Realising business value with model management

Philippines-based Globe Telecom faced model deployment challenges. While the mobile and broadband provider was implementing models in both SAS and open source, its process was manual, slow and lacked governance. With SAS, Globe has dramatically reduced deployment time while seamlessly working in both SAS and open source software.

“Globe uses analytical models to make the right offers to our 65 million customers, to build and strengthen our relationships with them, and to drive better and faster business decisions,” said Dan Natindim, VP and Enterprise Data Officer at Globe Telecom. “With SAS, Globe analyses all the data available, including customer, billing and network data, and through SAS and open source analytical models, we work to meet each customer’s individual needs.” 

Registering, deploying and monitoring open source models

Bringing together data scientists and IT/DevOps, SAS Open Model Manager helps organisations register, deploy and monitor open source models in one central environment. Available in November, the solution offers seamless integration with Python and R. Users can compare and assess different models, manage champion and challenger models, and access built-in performance reports to quickly evaluate whether to retrain, retire or develop new models.

Simplified publishing and scoring steps provide flexibility to deploy models with just a few clicks, both in batch and real time, with different operational environments. SAS Open Model Manager also improves governance by helping users better understand the function and performance of deployed models over time. Without the ability to continuously monitor a model’s degradation, business value and opportunity is rapidly lost.

SAS Open Model Manager will be delivered through container-enabled infrastructures, including Docker and Kubernetes, providing a portable, lightweight image that can be deployed in private or public clouds. Designed specifically to meet the needs of the open source community, no additional SAS technology is needed.

 ModelOps is another key ingredient in the last mile of analytics, where organisations move models from the data science lab into IT production as quickly as possible while ensuring quality results. The practice of ModelOps enables organisations to manage and scale models to meet demand and continuously monitor them to spot and fix early signs of degradation. Organisations that fail to embrace ModelOps face increasing challenges in scaling analytics and fall short of the competition.

Today’s announcement was made at the Analytics Experience conference in Milan, Italy, a business technology conference presented by SAS that brings together thousands of attendees on-site and online to share ideas on critical business issues.

* IDC’s Advanced and Predictive Analytics survey and interviews, n = 400, 2017 – 2019