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abundantly clear from the survey results that many financial organisations are still struggling with modernising their outdated legacy IT architectures and processes, resulting in inefficient operations and potential vulnerability to data breaches.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.
DISPELLING BIOMETRIC MYTHS AND MISCONCEPTIONS
By Lina Andolf-Orup, Head of Marketing at Fingerprints
Gangsters cutting off enemies’ fingers to access secret locations and spies lifting fingerprints from martini glasses – the imagination of the entertainment world has been running wild ever since biometrics entered the scene.
Couple that with the limitations of some early biometric solutions from fifteen years ago, still anchored in the minds of many consumers, and you have the perfect recipe for an apprehensive and uncertain public.
Thawing lukewarm attitudes with a biometric touch
The biometrics industry has made great strides in the last few years – something particularly true for smartphones. Fingerprint authentication has replaced PINs and passwords as the most popular way to authenticate on mobile, with 70% of shipped smartphones now featuring biometrics.
And it doesn’t end there. Many adjacent markets are now eager to benefit from the secure and convenient authentication solutions that biometrics offer. Take the payments industry, for example, where biometrics payment cards are currently gathering real momentum.
However, some consumers are still uneasy about accepting biometrics. A recent study found that 56% of US and EU consumers are concerned about the switch to biometrics as it’s not enough understood to be trusted.
Although attitudes are shifting for the better, stats like this demonstrate there is still some work to do to disprove common biometric myths and showcase just how smart today’s solutions really are.
Dispel, adopt, repeat
The evolution in consumer biometrics in the last two decades has been phenomenal. And today’s solutions are far more advanced and safe than many may think.
To help bring an end to the myths, let’s expose some of the most common misconceptions around biometrics.
Myth: Biometric data is stored as images in easy-to-hack databases.
A leading myth about biometrics is that when a fingerprint is registered to a device, it is stored as an image of the actual fingerprint. This image can then be stolen and used across applications. In reality, the biometric data is stored as a template in binary code – put simply, encrypted 0s and 1s. Storing a mathematical representation rather than an image makes hacking considerably more challenging. In most consumer applications, this template is also not stored in a cloud-based location, its securely hosted in hardware on the device itself for example in the smartphone, in the payment card. Thus, it stays privately with its owner.
Myth: Fingerprints can be easily replicated to ‘trick’ devices.
The internet is full of articles and videos that claim it is possible to use materials from cello tape to gummy bears to craft fingerprint spoofs and access biometric systems. Although there may have been a time where gummy bear spoofing was the go-to party trick, todays’ consumer biometric authentication solutions have too many technological defences, such as improved image quality and matching algorithms, to simply ‘trick’ devices. Plus, on top this, the criminal needs to have access to the person’s device where this fingerprint is enrolled e.g. smartphone, payment card, before he/she notices and blocks it. This is not scalable nor common, in comparison to gaining access to someone’s PIN code or skimming a contactless card.
Myth: Physical change will prohibit access to my device.
Although our irises don’t change as we age, our fingerprints can and our faces will. Does that mean we have to update our biometric devices every few months to capture these changes? Not quite! Unless there are drastic, sudden changes, the ‘self-learning’ algorithms in modern-day biometric systems are able to keep up with our developing looks.
Who you gonna call? Mythbusters!
These are just some of the common biometric myths and misunderstandings perpetuating in consumer mindsets. Thankfully, though, while we’re working hard to rid the world of the myths, belief in the value of biometrics is only expected to grow. But as solutions expand and diversify, the myth-busting fight will continue.
Fingerprints has been a leader of innovation in biometrics for the last two decades. We’re proud of the expertise and R&D we’ve been able to pour into our biometrics solutions to deliver stronger security and a better user-experience. To learn more about the most common biometric misconceptions and the modern-day technology that allows us to dispel them, download our eBook here.
WHAT EVOLUTIONARY AI MEANS FOR 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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