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By Steve Dunne, Staff Writer, Workday

In a recentMcKinsey survey,only 13 percent of CFOs and other senior business executives polled said their finance organizations use automation technologies, such as robotic process automation (RPA) and machine learning. Whats more, when asked how much return on investment the finance organization has generated from digitization and automation in the past 12 months, only 5 percent said it was a substantial return; the more common response was modest or minimal returns.

While that number may seem low right now, automation is coming to the finance function, and it will play a crucial role in furthering the CFOs position in the C-suite. Research suggests corporate finance teams spend about 80 percent of their time manually gathering, verifying, and consolidating data, leaving only about 20 percent for higher-level tasks, such as analysis and decision-making.

In its truest form, RPA will unleash a new wave of digital transformation in corporate finance. Instead of programming software to perform certain tasks automatically, RPA uses software robots to process transactions, monitor compliance, and audit processes automatically. This could slash thenumber of required manual tasks, helping to drive out errors and increase the efficiency of finance processeshanding back time to the CFO function to be more strategic.

According to the report Companies Using AI Will Add More Jobs Than They Cut, companies that had automated at least 70 percent of their business processes compared to those that had automated less than 30 percent discovered that more automation translated into more revenue. In fact, the highly automated group was six times more likely to have revenue growth of 15 percent per year or more.

In the right hands, automation and machine learning can be a fantastic combination for CFOs to transform the finance function, yet success will depend on automating the right tasks. The first goal for a finance team should be to automate the repetitive and transactional tasks that consume the majority of its time. Doing this will free finance up to be more of a strategic advisor to the business. AnAdaptive Insights surveyfound that over 40 percent of finance leaders say that the biggest driver behind automation within their organizations is the demand for faster, higher-quality insights from executives and operational stakeholders.

Accentures global talent and organization lead for financial services, Andrew Woolf, says the challenge for businesses is to pivot their workforce to enter an entirely new world where human ingenuity meets intelligent technology to unlock new forms of growth.

Transaction processing is one of the major barriers preventing finance from achieving transformation and the ultimate goal of delivering a better business partnership. It's not surprising that its the first port of call for CFOs looking toward automation.

RPA combined with machine learning provides finance leaders with a great way of optimising the way they manage their accounting processes. This has been a painful area of finance for such a long time and can have a direct impact on an organizations cash flow, says Tim Wakeford, vice president, financials product strategy, EMEA at Workday. Finance spends a huge amount of time sifting through invoices and other documentation to manually correct errors in the general ledger, while machine learning could automate this, helping to intelligently match payments with invoices.

Machine learning can also mitigate financial risk by flagging suspect payments to vendors in real time. Internal and external fraud costs businesses billions of dollars each year. The current mechanism for mitigating such instances of fraud is to rely on manual audits on a sample of invoices. This means looking at just a fraction of total payments, and is the proverbial needle in the haystack approach to identifying fraud and mistakes. Machine learning can vastly increase the volume of invoices which can be checked and analyzed to ensure that organizations are not making duplicate or fraudulent payments.

Ensuring compliance to federal and international regulations is a critical issue for financial institutions, especially given the increasingly strict laws targeting money laundering and the funding of terrorist activities, explains David Axson, CFO strategies global lead, Accenture Strategy. At one large global bank, up to 10,000 staffers were responsible for identifying suspicious transactions and accounts that might indicate such illegal activities. To help in those efforts, the bank implemented an AI system that deploys machine-learning algorithms that segment the transactions and accounts and sets the optimal thresholds for alerting people to potential cases that might require further investigation.

Read the second part of this story, How Automation and Machine Learning Are Reshaping the Finance Function, which takes a closer look at how automation and machine learning can drive change.

This story was originally published on theWorkday blog. For more stories like this, clickhere.

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Automation And Machine Learning: Transforming The Office Of The CFO - Forbes

Google has an update that might make you hate your alarm a little bit less: a new feature lets it automatically change up what your alarm plays based on the time of day and the weather, theoretically playing something slightly more appropriate than the same awful song you hear day in and out. At least, itll be nice as long as youre okay with waking up to AI-generated piano.

The feature is confined to a single device for now: Lenovos Smart Clock, a small smart display that basically has the functionality of a Google Home Mini paired with a screen that can show the time and weather. Google says this feature which it calls Impromptu is part of Google Assistant, though, which suggests it should reach other smart displays, and perhaps even phones, in the future. The announcement doesnt say when or whether itll expand, however.

Google says all of the music is created and chosen by Magenta, an open-source music tool built around machine learning that Google has been creating. In a blog post, Google says the system might select this song if the weather is below 50 degrees (Im assuming Fahrenheit) and early in the morning. I dont know exactly what about this song says cool and pre-dawn, but Id be down to listen to anything other than the default alarm tone that Ive heard every day for years.

The feature is rolling out globally today to Lenovos device. The smart clock, which used to retail for $80, now appears to be down to $50, making it a lot more competitive with Amazons $60 Echo Show 5.

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Google is using machine learning to make alarm tones based on the time and weather - The Verge

Discoverys data science team has been blazing new trails in the payment integrity industry through advanced analytics and machine learning capabilities.

ITASCA, Ill. (PRWEB) December 16, 2019

Discovery Health Partners has been named an honoree of a 2020 FutureEdge 50 Award from IDGs CIO. This prestigious honor is given to organizations that are pushing the edge with new technologies to advance their business for the future. Discovery will accept its award at the AGENDA20 Conference held March 23 25 at the Sawgrass Marriott Golf Resort & Spa in Ponte Vedra Beach, Florida.

Discoverys Case Open Logic initiative was piloted earlier this year to test machine learning as part of its claims ranking process within its Subrogation practice. Already a leader in analytics-powered technology solutions for some of the top health payers in the country, Discovery saw an opportunity to leverage its decade of case outcomes data to fuel a subrogation model that eliminates hours of manual work, results in more accurate case identification, and reduces member friction. Discovery is implementing improvements and enhancements and intends to roll out similar machine learning capabilities to other lines of business such as Data Mining, Clinical Audit and Coordination of Benefits (COB).

Discoverys data science team has been blazing new trails in the payment integrity industry through advanced analytics and machine learning capabilities, stated Discovery CIO Dan Iantorno. Combining technology innovation with our teams decades of experience in the health payer space is at the heart of how we deliver game-changing results for our customers.

The organizations in the FutureEdge 50 are doing exciting things that would have been unimaginable just a few years ago. For example, theres a project using blockchain and quantum computing to establish trust; an indoor navigation system; and many uses of AI/ML for insights and efficiency, said Anne McCrory, group vice president, customer experience and operations, events, and the FutureEdge and AGENDA20 conference chair. We are honored to showcase these innovations and many others as we enter into a new era of sophistication with cloud, devices, and environments powering the technology-driven business.

For more information about how Discovery Health Partners award-winning team and technologies are helping health payers solve payment integrity challenges to improve financial and member outcomes, visit https://www.discoveryhealthpartners.com/.

About Discovery Health PartnersDiscovery Health Partners mission is to deliver unique, actionable analytic insights and technology-powered solutions to help healthcare payers improve payment integrity, increase revenue optimization, and maximize efficiencies with government programs. Serving more than 70 health plans across the U.S., including six of the 10 largest U.S. health plans, Discovery has been recognized consistently for its growthearning a spot on the Inc. 5000 list six years in a row and the Crains Fast 50 three times. For more information on Discovery Health Partners, go to http://www.DiscoveryHealthPartners.com.

About the FutureEdge 50 Awards The FutureEdge 50 awards recognize organizations pushing the edge with new technologies to advance their business for the future. The successor to the Digital Edge 50 awards, the FutureEdge 50 will recognize not only established initiatives driving business success but also early-stage projects pursued for their watershed potential. These initiatives may be in R&D, proof of concept or pilot phases. With this, the FutureEdge 50 awards aim to bring the most cutting-edge trials and applications of emerging technologies and the innovative cultures enabling them to our audience at the AGENDA conference.

About CIO CIO focuses on attracting the highest concentration of enterprise CIOs and business technology executives with unparalleled expertise on business strategy, innovation, and leadership. As organizations grow with digital transformation, CIO provides its readers with invaluable peer insights on the evolving CIO role as well as how leading IT organizations are employing technologies, including automation, AI & machine learning, data analytics and cloud, to create business value.

The award-winning CIO portfolio CIO.com, CIO events, CIO Strategic Marketing Services, CIO Forum on LinkedIn, CIO Executive Council and CIO primary research provides business technology leaders with analysis and insight on information technology trends and a keen understanding of ITs role in achieving business goals. CIO is published by IDG Communications, Inc. Company information is available at http://www.idg.com.

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Discovery Health Partners' Case Open Logic Machine Learning Initiative Receives FutureEdge 50 Award from IDG - PR Web

When one technology replaces another, its not easy to accurately ascertain how the new technology would impact our lives. With so much buzz around the modern applications of Artificial Intelligence, Machine Learning, and Data Science, it becomes difficult to track the developments of these technologies. Machine Learning, in particular, has undergone a remarkable evolution in recent years. Many Machine Learning (ML) techniques have come in the foreground recently, most of which go beyond the traditionally simple classifications of this highly scientific Data Science specialization.

Read More: Beyond RPA And Cognitive Document Automation: Intelligent Automation At Scale

Lets point out the top ML techniques that the industry leaders and investors are keenly following, their definition, and commercial application.

Perceptual Learning is the scientific technique of enabling AI ML algorithms with better perception abilities to categorize and differentiate spatial and temporal patterns in the physical world.

For humans, Perceptual Learning is mostly instinctive and condition-driven. It means humans learn perceptual skills without actual awareness. In the case of machines, these learning skills are mapped implicitly using sensors, mechanoreceptors, and connected intelligent machines.

Most AI ML engineering companies boast of developing and delivering AI ML models that run on an automated platform. They openly challenge the presence and need for a Data Scientist in the Engineering process.

Automated Machine Learning (AutoML) is defined as the fully automating the entire process of Machine Learning model development right up till the process of its application.

AutoML enables companies to leverage AI ML models in an automated environment without truly seeking the involvement and supervision of Data Scientists, AI Engineers or Analysts.

Google, Baidu, IBM, Amazon, H2O, and a bunch of other technology-innovation companies already offer a host of AutoML environment for many commercial applications. These applications have swept into every possible business in every industry, including in Healthcare, Manufacturing, FinTech, Marketing and Sales, Retail, Sports and more.

Bayesian Machine Learning is a unique specialization within AI ML projects that leverage statistical models along with Data Science techniques. Any ML technique that uses the Bayes Theorem and Bayesian statistical modeling approach in Machine Learning fall under the purview of Bayesian Machine Learning.

The contemporary applications of Bayesian ML involves the use of open-source coding platform Python. Unique applications include

A good ML program would be expected to perpetually learn to perform a set of complex tasks. This learning mechanism is understood from the specialized branch of AI ML techniques, called Meta-Learning.

The industry-wide definition for Meta-Learning is the ability to learn and generalize AI into different real-world scenarios encountered during the ML training time, using specific volume and variety of data.

Meta-Learning techniques can be further differentiated into three categories

In each of these categories, there is a unique learner, meta-learner, and vectors with labels that match Data-Time-Spatial vectors into a set of networking processes to weigh real-world scenarios labeled with context and inferences.

All the recent Image Processing and Voice Search techniques use the Meta-Learning techniques for their outcomes.

Adversarial ML is one of the fastest-growing and most sophisticated of all ML techniques. It is defined as the ML technique adopted to test and validate the effectiveness of any Machine Learning program in an adverse situation.

As the name suggests, its the antagonistic principle of genuine AI, but used nonetheless to test the veracity of any ML technique when it encounters a unique, adverse situation. It is mostly used to fool an ML model into doubting its own results, thereby leading to a malfunction.

Most ML models are capable of generating answer for one single parameter. But, can it be used to answer for x (unknown or variable) parameter. Thats where the Causal Inference ML techniques comes into play.

Most AI ML courses online are teaching Causal inference as a core ML modeling technique. Causal inference ML technique is defined as the causal reasoning process to draw a unique conclusion based on the impact variables and conditions have on the outcome. This technique is further categorized into Observational ML and Interventional ML, depending on what is driving the Causal Inference algorithm.

Also commercially popularized as Explainable AI (X AI), this technique involves the use of neural networking and interpretation models to make ML structures more easily understood by humans.

Deep Learning Interpretability is defined as the ML specialization to remove black boxes in AI models, providing decision-makers and data officers to understand data modeling structures and legally permit the use of AI ML for general purposes.

The ML technique may use one or more of these techniques for Deep Learning Interpretation.

Any data can be accurately plotted using graphs. In Machine Learning techniques, a graph is a data structure consisting of two components, Vertices (or nodes) and Edges.

Graph ML networks is a specialized ML technique used to connect problems with edges and graphs. Graph Neural Networks (NNs) give rise to the category of Connected NNs (CNSS) and AI NNs (ANN).

There are at least 50 more ML techniques that could be learned and deployed using various NN models and systems. Click here to know of the leading ML companies that are constantly transforming Data Science applications with AI ML techniques.

(To share your insights about ML techniques and commercial applications, please write to us at info@aithority.com)

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10 Machine Learning Techniques and their Definitions - AiThority

Processing big data in real-time for artificial intelligence, machine learning, and the Internet of Things poses significant infrastructure challenges.

Whether it is for autonomous vehicles, connected devices, or scientific research, legacy NoSQL solutions often struggle at hyperscale. Theyve been built on top of existing RDBMs and tend to strain when looking to analyze and act upon data at hyperscale - petabytes and beyond.

DBTA recently held a webinar featuring Theresa Melvin, chief architect of AI-driven big data solutions, HPE, and Noel Yuhanna, principal analyst serving enterprise architecture professionals, Forrester, who discussed trends in what enterprises are doing to manage big data in real-time.

Data is the new currency and it is driving todays business strategy to fuel innovation and growth, Yuhanna said.

According to a Forrester survey, the top data challenges are data governance, data silos, and data growth, he explained.

More than 35% of enterprises have failed to get value from big data projects largely because of skills, budget, complexity and strategy. Most organizations are dealing with growing multi-format data volume thats in multiple repositories -relational, NoSQL, Hadoop, data lake..

The need has grown for real-time and agile data requirements, he explained. There are too many data silos multiple repositories, cloud sources.

There is a lack of visibility into data across personas -- developer, data scientist, data engineers, data architects, security etc..Traditional data platforms have failed to support new business requirements such as data warehouse, relational DBMS, and ETL tools.

Its all about the customer and its critical for organizations to have a platform to succeed, Yuhanna said. Customers prefer personalization. Companies are still early on their AI journey but they believe it will improve efficiency and effectiveness.

AI and machine learning can hyper-personalize customer experience with targeted offers, he explained. It can also prevent line shutdowns by predicting machine failures.

AI is not one technology. It is comprised of one or more building block technologies. According to the Forrester survey, Yuhanna said AI/ML for data will help end-users and customers to support data intelligence to support new next-generation use cases such as customer personalization, fraud detection, advanced IoT analytics and rea-time data sharing and collaboration.

AI/ML as a platform feature will help support automation within the BI platform for data integration, data quality, security, governance, transformation, etc., minimizing human effort required. This helps deliver insights quicker in hours instead of days and months.

Melvin suggested using HPE Persistent Memory. The platform offers real-time analysis, real-time persist, a single source of truth, and a persistent record.

An archived on-demand replay of this webinar is available here.

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Managing Big Data in Real-Time with AI and Machine Learning - Database Trends and Applications