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NEW REPORT REVEALS FINANCIAL SERVICES INDUSTRY EMBRACING HYBRID CLOUD

Sector Still Runs a Significant Percentage of Traditional Data Centres

 

Nutanix, Inc. (NASDAQ: NTNX), a leader in enterprise cloud computing, today announced findings of its Enterprise Cloud Index Report for the financial services sector, measuring financial firms’ plans for adopting private, public and hybrid clouds. The report revealed that the financial sector outpaces other industries in the adoption of hybrid cloud, with the deployment of hybrid cloud reaching 21% penetration today, compared to the global average of 18.5%.

 

Financial services firms today are facing mounting competitive pressure to streamline operations while delivering a differentiated experience to their customers, including leveraging new technologies such as blockchain. This FinTech revolution, combined with the growing burdens of regulatory compliance, data privacy, and security issues are pushing CIOs to fundamentally transform the technological underpinnings of their institutions. The report reveals exactly how the financial services industry is embracing the capabilities of cloud computing to address these needs.

 

It is also abundantly clear from the survey results that many financial organisations are still struggling with modernising their outdated legacy IT architectures and processes, resulting in inefficient operations and potential vulnerability to data breaches. In fact, the report revealed financial services run more traditional data centres than other industries, with 46% penetration. Despite their progressiveness on the hybrid cloud front, financial organisations have lower usage levels of private clouds than any other industry, at 29% penetration compared to the average of 33%.

 

Like other industries, the financial services sector cites security and compliance as the top factor in deciding where to run its workloads. Nearly all respondents also indicated that performance, management, and TCO are critical factors in the decision. However, more than 25% cited these same factors as challenges with adopting public cloud. In other words, as is often the case with new IT solutions, the most important criteria are also the most difficult to achieve. This could account for part of the disparity between the high desire to adopt hybrid cloud, and today’s relatively low hybrid cloud penetration levels of just 21% in the financial services sector.

 

The bullish outlook for hybrid cloud adoption globally and across industries is reflective of an IT landscape growing increasingly automated and flexible enough that enterprises have the choice to buy, build, or rent their IT infrastructure resources based on fast transforming application requirements.

 

Other key findings of the report include:

 

  • The financial sector values application mobility across clouds. Application mobility is the ability to move apps and workloads back and forth across private and public cloud infrastructures as workload type or economics warrant, while enjoying unified management and operations. Both financial companies and other industries chose application mobility between clouds second most often as the number one perk to hybrid cloud, and the financial sector chose it 3% more often than the average. 63% of financial industry respondents said they considered inter-cloud application mobility “essential.”
  • Financial services companies control cloud spend better. Another motivation for deploying hybrid clouds is likely enterprises’ need to gain control over their IT spend. Organisations that use public cloud spend 26% of their annual IT budget on public cloud, with this percentage set to increase to 35% in two years’ time. More than a third (36%) of organisations using public clouds said their public cloud spending exceeded budgets. In comparison, 33% of financial respondents reported being over budget, revealing that they are doing marginally better than others at managing public cloud expenses.
  • IT skills are a barrier to adopting hybrid cloud in the financial industry. While 88% of respondents said that they expect hybrid cloud to positively impact their businesses, hybrid cloud skills are scarce in today’s IT organisations. These skills ranked second in scarcity only to those in artificial intelligence and machine learning (AI/ML). Financial services respondents generally reportedly slightly greater deficits in skillsets across all categories except for AI/ML.

 

91% of financial services organisations surveyed said that hybrid cloud was the ideal IT model. This belief in hybrid cloud, and the fact that the sector has higher than industry average adoption of hybrid cloud, is likely driven by the recognised need for digital transformation. Yet conversely, the data shows a lower adoption of private clouds than the global average across industries. This might be explained by the fact that portions of the financial services space have been change-averse and also an indication of the overall complexity of modernising existing legacy infrastructures.

 

“Increased competitive pressure combined with higher security risks and new regulations will require all of the industry to look at modernising their IT infrastructure,” said Chris Kozup, SVP of Global Marketing at Nutanix. “The current relatively high adoption of hybrid cloud in the financial services industry shows that financial firms recognise the benefits of a hybrid cloud infrastructure for increased agility, security, and performance. However, the reality is that financial services firms still struggle to enable IT transformation, even though it is critical for their future.”

 

To create this report, Nutanix commissioned Vanson Bourne to survey more than 2,300 IT decision makers, including 333 worldwide financial services organisations, about where they are running their business applications today, where they plan to run them in the future, what their cloud challenges are and how their cloud initiatives stack up against other IT projects and priorities. The survey included respondents from multiple industries, business sizes and geographies in the Americas; Europe, the Middle East, Africa (EMEA); and Asia-Pacific and Japan (APJ) regions.

 

To learn more about the global report and findings, please download the “Nutanix Enterprise Cloud Index 2018,” here.

 

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Technology

WHAT EVOLUTIONARY AI MEANS FOR FINANCIAL SERVICES

FINANCIAL SERVICES

by Babak Hodjat, VP of Evolutionary AI at Cognizant

 

Many banks and other financial services institutions (FIs) are beginning to recognise the benefits of AI-driven solutions as a way to get ahead in the market and challenge the competition. Amongst many other benefits, the technology enables organisations to offer hyper-personalised customer experience,  dramatically improve internal decision making, and drive operational efficiency. However, many businesses are struggling to move beyond the experimental phase and reach actual AI deployment. It is those organisations that are at risk of being left behind.

The financial world has already been transformed by AI, and this transformation is continuous. A new breed of AI, known as ‘evolutionary AI’ has begun to further accelerate innovation. It is capable of automatically designing itself with little need for explicit programming by humans – innovatively creating complex AI models, and optimising decisions considering multiple scenarios.

This technology is revolutionary for industries across the world, but in particular it is set to transform the financial services sector. Enabling businesses to spot novel strategies that would never have been identified by human data scientists, and, in turn, allowing companies to take full advantage of today’s massive data sets – evolutionary AI will soon be a vital tool in all FIs’ arsenals.

 

The nuts and bolts of evolutionary AI

Emerging technologies that enable AI algorithms to design themselves are allowing organisations to transcend human limitations. Evolutionary AI operates iteratively. Firstly, it randomly generates a set of potential solutions to form an initial population and assigns a score to each solution based on how well it performs relative to other solutions. In the second round, it retains the solutions that performed best, perhaps only 5% of the total, and recombines their components, sometimes “mutating” them to create a new population. This new population is then tested, and the process begins again. Over multiple generations, the appropriate components of the more successful solutions become increasingly prevalent in the population, and eventually a solution is discovered that yields the best outcomes.

 

Advantages and use cases

Compared to human design, evolutionary AI can be deployed far more quickly, avoids biases and preconceptions, and typically performs better. Furthermore, the chosen model will evolve and improve over time based on new data.

Evolutionary AI can be applied in a wide variety of areas at FIs. Some examples include designing quantitative trading strategies to maximise returns while minimising risk and loan underwriting. Rather than relying on human analysis, evolutionary AI solutions can quickly analyse all the combinations of relevant variables to create models that more accurately assess the risk of default by a potential borrower.

 

A recipe for success

In order to reap the benefits of the technology, FIs should focus on the following:

  • Responsible AI – Behave in ways that make customers and employees comfortable, i.e. not making decisions that are unethical or exhibit bias. Companies need to monitor them to ensure they continue to act appropriately, as they learn and evolve.
  • Viewing AI through a business lens – Having AI projects managed by cross-functional teams with business executives in the lead is a good place to start. Companies also need to look across their organisations to identify opportunities to generate concrete business value from AI — not only in reduced costs but also in boosting revenues by delivering enhanced customer experiences and through improved decision-making.
  • Enhance data management – AI applications depend on access to timely and accurate data, which is a challenge for many FIs that have fragmented data architectures with multiple legacy systems. Companies need to identify which types of data are required for each AI project and ensure they can be captured in an appropriate format.
  • Approach with speed and caution – AI projects need to be rolled out quickly, while at the same time be rigorously measured, so failures are terminated promptly while successes are moved into production.

The sophistication of AI technology is set to significantly improve over the coming years as it continues to design and test itself. As a result, it will become more critical to the productivity of FIs, and soon businesses will recognise it as a vital tool for consulting on important business decisions. It will not be long before humans and AI are working alongside each other, with robots handling routine tasks, enabling employees to focus on more complex and sensitive activities. Delivering more value together than either could on their own.

 

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Technology

6 EXAMPLES OF ARTIFICIAL INTELLIGENCE IN USE TODAY

ARTIFICIAL INTELLIGENCE

We’re probably not aware of the moment when artificial intelligence sneaked into our lives and became a part of our everyday existence.

Was it the first movie recommendation we got from Netflix? Or the item we’ve purchased using Amazon’s suggestions?

AI is now all around us, and it’s holding tremendous potential, with new application possibilities being discovered every day.

We’ve gathered six very practical examples of AI, to show just how widespread this technology is today, and how prevalent it is yet to become.

1.    Healthcare

The use of AI in healthcare is already bringing many benefits – at the beginning of the year, we’ve witnessed  AI outperforming six radiologists in reading mammograms and identifying breast cancer more accurately and quickly.  A computer algorithm can now analyze images in just a few seconds, significantly improving the speed of diagnosis.

The first AI-designed drug molecule, by Excienta, is currently being tested on humans. While it usually takes three to four years for traditional research to reach this stage, it just took 12 months for the algorithm to make it possible.

As for the current COVID-19 crisis, the Chinese technology giant Alibaba has recently developed an algorithm that can detect coronavirus in seconds, with 96% accuracy. The algorithm that analyzes CT images of patients’ chests is used by more than 100 healthcare facilities to distinguish the disease from other viral pneumonia cases.

2.    Virtual Personal Assistants

Alexa, Siri, Cortana, and Google Assistant are just some of the most notable examples of artificial intelligence we’re all familiar with.

We interact with our PAs regularly – ask them for directions, information about the weather and the news. They allow us to finish various tasks easily, without having to use our hands – we can stream podcasts, play music, make to-do lists, set alarms, schedule our meetings, or order pizza.

These voice assistants use machine learning technology and natural language processing so that they can get smarter and more capable of understanding voice queries. In other words, with the help of AI and its subsets, voice assistants learn from your previous searches and preferences and use all the information they collect to offer you better search results and service.

3.    Customer Service

Even if you’re still not interacting with your devices using your voice assistant, you’ve surely communicated with AI at least once, most probably in the form of a chatbot.

There is hardly any good online store that doesn’t offer at least some kind of AI-powered customer support.

The most common are conversational chatbots that are now able to answer 80% of most common customers’ queries in an accurate and timely manner, without any need for additional human intervention.

With data AI chatbots gather on the customers, they are now able to facilitate human-like interaction and personalize it more than their human counterparts.

Conversational AI algorithms can now give offers and recommendations that are more likely to fit customers’ needs and interests, boosting both their satisfaction and retailers’ revenue.

4.    Finance

The banking and finance sector has recognized the possible advantages of AI early on and is now successfully implementing the technology for various purposes.

The finance industry relies heavily on large amounts of data and accurate real-time information. With the use of AI, it’s now much easier to detect frauds, money-laundering, or any other suspicious behavior. Some of the financial advisors’ tasks can now be automated too, as  AI-powered advisors can quickly scan the market data, and predict the best portfolio or stock.

One of the best examples of how useful AI in the banking industry can be, is Erica, an AI employee of the National Bank of America. This digital financial assistant has already served over 7 million customers and handled over 50 million queries. Apart from managing many other different tasks, Erica helps customers with their transactions and budgeting, tracks their spending habits, monitors duplicate charges, and gives useful advice.

 

5.    Smart Vehicles and Delivery

Cars and drones are also shifting towards the use of AI.  Even though artificial intelligence is widely used in car manufacturing, its use in the automotive industry is most commonly related to the use of self-driving vehicles, that are leveraging machine learning and vision to find their way through the traffic safely.

Autox is, for example, currently testing their autonomous grocery delivery within San Jose. Their vehicles use AI software, real-time cameras, and sensors to navigate within a geofenced zone, with plans for gradual expansion.

Amazon and Walmart are already investing large amounts of money into drone delivery programs. Amazon’s goal for its Prime Air service is to create fully electric drones that can deliver packages lighter than 5 pounds, to their customers located within 15 miles, in less than 30 minutes.

6.    Smart Homes

One of the finest examples is Nest, the thermostat algorithm, which uses an intelligent machine learning process to learn about your behavior and the temperatures you like. It then anticipates and adjusts your home or your office to your temperature needs, at the same time saving significant energy resources.

Google acquired Nest back in 2014 for $3.2 billion and now aims to create a smart and helpful home. The tech giant is trying to connect devices such as thermostats, cameras, alarm systems, doorbells, and locks under the same roof, offering an easy to use smart home solution.

However, for this to work seamlessly, technological advancement in the field of IoT and 5G needs to be utilized. Near-zero latency is what allows for smart homes to be remotely controlled.

As you can see, AI is impacting and improving every aspect of our lives and our society. It will revolutionize the way we do different things, from driving our cars to receiving medical treatment. Although this technology is still in its infancy, it has already managed to disrupt the above-mentioned areas and offer us a sneak peek into the future.

 

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