By Gerry Gibney, senior strategist, financial services, OpenText
Consumers today expect easy and convenient access to all of their services – and this is no different for their interactions with the financial services industry. Whether it’s useful banking apps which enable easy mobile-based transactions or a quick chat tool to get in touch with a customer service representative, users want smooth, consistent experiences.
Digitally-enabled lifestyles are driving heightened expectations for service providers not only to meet these customer needs, but to anticipate them. While no single easy-fix solution exists to improve a customer’s experience, artificial intelligence (AI) can play a key role here.
Google’s Ray Kurzweil has already estimated that AI will have human-level intelligence by 2029. While we’re unlikely to see robots taking over, we should expect the service that customers receive from their bank, insurance firm, phone company and utilities provider to improve. But how can financial services implement AI technology to specifically improve customer experience and streamline the customer journey?
Across industries, more transactions are moving from manual to digital, while data – both structured and unstructured – is exploding inside and outside the organisation. As a result, financial services are simultaneously facing rising customer demands while attempting to manage and gain from the huge amounts of data available today.
AI has the potential to help businesses do many things better, faster, cheaper and with fewer errors than any manual process. When combined with advanced analytics, it can also deliver insight into what customers are doing today and what they are likely to do tomorrow – turning reams of data into actionable intelligence that can be applied to improve business processes.
A frequently used statistic suggests that it costs five times as much to attract a new customer as it does to keep an existing one. Faced with this, financial services organisations must carefully consider how to implement AI technology and advanced analytics to transform those parts of the business that will have an impact on customer experience.
Organisations must select those processes that are closely aligned to business objectives so they can see maximum impact from the introduction of AI and analytics. In this way, financial services companies can streamline the customer journey to drive customer loyalty and reduce churn.
Boosting efficiency – and customer satisfaction
AI is not a new concept for the banking industry. The technology is already embedded within some key surveillance processes such as fraud prevention and detection. However, the amount and variety of data that must be analysed is new. With advances in natural language processing technology, machine learning algorithms and expert systems, AI is simply getting better and better at automating repetitive, transaction-intensive work processes.
Predictive analytics has also been implemented for business optimisation for some time. However, we now have access to the missing piece of the puzzle – a cognitive approach that can handle the huge quantities of data available today, intelligently uncover trends and patterns, and use the insight to inform better decision-making. Getting useful insights out of this automation is the key value-add.
By combining AI and analytics, banks can take steps to become more customer-centric. This can range from client onboarding and tracking customer sentiment to identifying areas where customers should be involved. Similarly, AI can be implemented to decrease time to a new market, allowing a bank to build upon its successes elsewhere and quickly translate that into another market to meet customer needs. Across the board, AI has the ability to enhance the organisation’s overall operation and make it more efficient, thereby improving customer satisfaction.
Removing data siloes
Financial services face a key barrier when attempting to implement AI and analytics. Their information is often contained in siloes – stored across different systems as well as different types of hardware and software. Furthermore, despite often holding valuable data, these systems may not have been recently updated and may not be readily accessible by employees in other departments.
Overcoming this issue is essential. It is only by tapping into one pool of data across the business that
AI-enhanced analytics can deliver useful insights that take in the big picture of all the available business data and content. Algorithms and cognitive models help identify trends and patterns in the vast amounts of structured and unstructured data while ‘data noise’ is simultaneously filtered out.
Machine learning and natural language processing become more effective with higher volumes of data. So, as more data is analysed, the more accurate the conclusions and recommendations become. As a result, ensuring access to all of the data across a business will generate more useful insights to improve customer experience.
Customers want products and services specifically suited to their requirements that help them achieve what they want to accomplish. While the financial services industry is expanding its digital capabilities to face this challenge, innovation for innovation’s sake is not enough. Implementing AI and analytics to become more customer-centric will be key to ensuring technological innovation in the industry drives results – great user experiences and happy customers.
Making Connectivity A Key Part of Cloud Strategy for Finance
By Eric Troyer, CMO at Megaport
Finance organisations across the board are facing unprecedented disruption, with new technology entering the industry and consumer demands constantly evolving. Firms are trying to adapt to the rise of digital-first and the insatiable hunger of customers for increased speed and new types of services.
Leaders in the finance industry see cloud adoption as vital for multiple reasons. For example, they can leverage the cloud to speed up processing, eliminate data silos, surface deeper insights, and lower infrastructure and operating costs. As Peter Williams, global head of financial services technology, at Amazon Web Services, argues: “Taking advantage of virtually unlimited data storage and compute power to mine their core data allows financial institutions to make better trading, investment, and policy underwriting decisions. Analysing patterns across exabytes of data used to require significant investment and time, but cloud services that automate machine learning (ML) algorithms are improving how quickly organisations can innovate for their customers.”
Many financial organisations have long realised the importance of the cloud, however adoption has picked up steam in the last 12 months, and this only looks to be accelerating. Eighty-four per cent of fintechs, 82 per cent of corporate banks, 74 per cent of retail banks and 79 per cent of intermediaries globally plan to move mission-critical workloads into public cloud infrastructure by the end of this year, according to Ovum and ACI Worldwide.
It isn’t necessarily surprising that the finance industry is now fully embracing the cloud and understanding the innovative benefits it provides, but are they taking a step back to make sure the infrastructure is in place to support these goals? Are they thinking about how to actually make the cloud work for them?
Connecting the tightly-knit financial services ecosystems
The global professional services firm EY argues, “The benefits of cloud technology can’t be contested. It’s less expensive, easier to use, and in many ways, safer than private data centres. In addition to its benefits, cloud technology helps solve some of the most pressing concerns of financial institutions.” It goes without saying that the cloud is going to play a vital role in how the finance sector develops for years to come. However, that comes with pressure to get it right now.
For example, a stockbroker may use data to influence their decisions to purchase or sell, but they need to have access to this information quickly, then marry it with intelligence on their customer (their appetite for risk, etc.), and then be able to seamlessly make the purchase or sale. Having access to the data and partners that solves just one part of this equation isn’t helpful; it all has to be linked up.
This all requires speed, but, more importantly, it relies on a tightknit ecosystem for its success. Therefore, high performing cloud connectivity must be at the heart of any cloud strategy for finance organisations, so that they can tie these ecosystems together and move data quickly and securely. Despite this being a critical ingredient to success, many businesses have focused on what cloud service provider they will use or what application they want to run without considering how they will actually connect it all together.
While many CTOs focus on their cloud strategy, what really needs to be refined is their connectivity strategy.
Cloud connectivity — elastic is fantastic
When it comes to networks and connectivity, many finance organisations would have traditionally utilised a Multiprotocol Label Switching (MPLS) network. However, when private MPLS networks were first deployed, accessing cloud providers may not have been top of mind. Today, this is a critical factor in the deployment.
To support the enterprise costs of deploying a large-scale MPLS network, an organisation most likely entered into a long-term contract, not necessarily planning for how cloud could affect that decision in the future and the challenges of being locked into their existing topology. Adding multiple clouds and managing connectivity on an existing MPLS platform is typically slow, costly, and complex. This is the last thing financial organisations want when trying to boost speed and flexibility.
This is where Software Defined Networks (SDN) and virtual routing services come into their own. They can solve the cloud connectivity problem without requiring financial organisations to rip and replace MPLS networks and manage the expense that comes with that.
Working with an SDN to connect to the cloud helps reduce complexity of IT infrastructure and costs. More importantly, for financial services, SDN can reduce downtime as it helps to virtualise most of the physical networking devices, making it easy to perform an upgrade for one piece rather than needing to do it for several devices.
Virtual routing services, meanwhile, are a great option to solve the complexities and costs that come with connecting an MPLS network to the public cloud. Essentially, it is a way of easily and virtually routing an existing MPLS network to the cloud via an SDN.
Another major consideration for IT leaders is whether their organisation can quickly adapt to peaks and troughs in demand. Increasing bandwidth when needed can be critical to quickly adapting to changes in the stock market, for example. Therefore, financial organisations should ensure that connectivity to the cloud is elastic — where bandwidth can be turned up or down in an instant This flexibility ensures that core business operations don’t fall down if there are peaks in demand and, equally as important, IT leaders are not paying for unused bandwidth that they don’t need.
Architecting a future-proof cloud infrastructure
There is no question that cloud technology will continue to change the way financial organisations operate. The task for CTOs now is to make sure they can easily get to the cloud, and they can quickly connect the tight-knit ecosystems they rely upon for success. The question CTOs at finance organisations should be asking themselves, therefore, is not what innovation can the cloud drive, but rather how do I make the most of it? They can expect that connectivity will be a big part of the answer.
The (U)X Factor: The software bringing biometric payment cards to market
By Jonas Nilsson, Product Manager at Fingerprints
With over 20 bank trials in progress and a second commercial roll-out imminent in France with BNP Paribas, contactless biometric payment cards are steadily but surely making their way to our wallets, marking what has been called the ‘biggest development in card technology in recent years’.
Innovation cannot stand still now, though. Key learnings and insights from the trials, combined with expertise from mobile biometric systems, are driving more optimized products. As you’d expect, security and privacy are always front of mind but a seamless user experience (UX) is just as important for any new technology to achieve widespread consumer adoption.
Our research found that 64% of consumers identified a low rejection rate and ergonomics as key priorities for adopting the new technology. To succeed, biometric payment cards must not only improve the security of contactless, but deliver the same seamless UX too.
Getting these aspects right has been a balancing act of hardware and software innovation. Let’s have a look at the innovation that’s taking the biometric payment card from trial to the hands of consumers.
Time and time again, research shows that consumers and retailers alike want to avoid friction at the point-of-sale (POS) that might cause frustration, embarrassment or – most critically for retailers – dropouts.
As with any payment technology, a potential source of friction lies in the interaction with the traditional payment acceptance terminal itself. R&D has zoomed in on this to ensure transaction speeds remain as slick as traditional contactless. By optimizing the power consumption of biometric sensors in payment cards, the sensor and on-card matching process can all be powered from the payment terminal in the same way contactless cards are. The ultra-low power sensor is always on ‘standby’, meaning it is ready to go at a ‘tap’ on the terminal. Care has also been taken to ensure the cards are compliant with 100% of current payment terminals power levels, greatly reducing the possibility of friction.
Given these concerns about friction, it’s unsurprising that 64% of consumers in our research emphasized avoiding false rejections, where the correct fingerprint “doesn’t work” or isn’t read, as a point of hesitation.
While security is measured by the False Acceptance Rate (FAR) – where the wrong user is authenticated – convenience can be measured by the False Rejection Rate (FRR). This rate has historically been relatively low, but is in a constant balancing act with the FAR, with greater security provisions usually meaning a slight trade-off in convenience.
However, further refinements to the hardware which captures the fingerprint image, and the algorithm which process it, have succeeded in reducing false rejections even further. Drawing on improved image quality and more efficient internal software, the sensor can now read and authenticate the fingerprint source from more angles than ever. Even better, these improvements have also reduced the False Acceptance Rate (FAR), making authentications even more secure at the same time.
Real-time, all the time
A key measure of UX in payments is speed – especially when it comes to contactless. To be able to compete, biometric payment cards must deliver the same less-than-a-second authentication as unauthenticated contactless.
The challenge, of course, is that security must remain a priority – but imposing too much latency with new protection and anti-spoofing provisions is a threat to convenient response times, and ultimately, the UX.
Once again, further innovation has been crucial here. Thanks to refined sensor technology, the latest biometric sensors are able to increase transaction speeds by some 30% compared to earlier trials.
Ready to rock and enroll!
First thing’s first, when users receive their new payment card, they want to enroll quickly and securely. A laborious enrollment process risks curbing enthusiasm for the tech and ultimately, its adoption.
The good news is that enrollment is in fact very similar to the authentication process. It benefits directly from the same refinements to image capture and quality which are reducing rejection rates and speeding up transactions. Now, with improved image quality, capture and processing, enrollment can be done at any angle – quicker than ever before.
Fingerprints at the ready
As the market stands on the cusp of major commercial rollouts, the momentum behind biometric payment cards seems unstoppable. Convenience, safety and security are making a compelling case to banks and consumers alike.
Still, it’s important to remember that continual advances in the tech are fundamental to take the cards to the consumer. Fine-tuning and further optimization of sensor technology and accompanying software and algorithms has smoothed out any remaining concerns to maintain the all-important UX appeal.
Embracing digital automation without compromising on customer experience
By Mang-Git NG, CEO & Founder of Anvil
Community banks have always prided themselves on their ability to serve their local community with an unmatched level of customer service. My family has experienced this first hand when my parents immigrated to the United States as graduate students with no credit history and very little income. When no national bank would open an account for them, the local community bank provided the banking services my parents needed to help them find their feet.
You can expect to be anonymous at a large bank but as a community bank customer, you expect a more personal connection with your banker—after all, you live in the same community.
While the ability to nurture personal relationships remains a critical differentiator, community banks face a number of ever-evolving external pressures, from the scale of large incumbents to evolving customer expectations, threatening the ability to grow their customer base and even retain existing ones. It can be especially frustrating as a long-standing customer to be asked for your basic personal information over and over again on bank forms when your relationship goes far deeper than that.
Automation and customer experience are no longer a trade-off
Small business owners are increasingly willing to pay more for products and services that make their lives easier. This trend favoring convenience is likely to accelerate with the rise of Gen Z given their preference for mobile-first instant messaging apps. With this in mind, the need to transform products and services with digital technology adoption is a top priority for many banks. In a recent KPMG survey, 72% of bank CEOs said they were prioritizing investment in new technology, and 58% even said they have begun using artificial intelligence (AI).
However, community bank leaders have faced a dilemma in the past. The adoption of automation technology often meant compromising on customer experience. We’ve all dealt with frustratingly unhelpful chatbots that are ill-equipped to handle complex queries or advice that often come up in financial services.
Fortunately, automation technology has progressed beyond simple chatbots and now offers smarter ways to authenticate users without adding more friction, predictive analytics are helping bank employees make more strategic product recommendations that match a customer’s needs, and workflow automation is enabling banks to improve customers’ account opening process while also dramatically reducing the overhead of processing applications.
Combining these digital tools with a human touch to create personalized automation, and applying each in the right way, will help community banks stand apart from large banks, keep existing customers happy, and attract new ones.
Automation and profitability go hand in hand
In 2018, fraud against bank deposit accounts amounted to $25.1 billion, including $2.8 billion in losses. Intelligent fraud detection can help minimize such losses, thus impacting the bottom line. Banks that embrace security and fraud detection technologies have a significant advantage over their peers.
Similarly, automation to help streamline existing processes can prove invaluable. Every year, paper and PDF-based processes cost banks billions of dollars. Adopting paperwork automation technology leads to faster processing times for routine, repetitive processes like account openings and loan applications, resulting in greater efficiency and reduced costs. The use of dynamic forms with built-in validation also eliminates human error and the need for manual checks.
As an added benefit, automating away mundane processes like data entry allows community banks to invest time and human capital in what they do best: getting to know their customers and developing personal relationships.
Finally, process automation can enable banks to unlock growth. As an example, Minnesota’s Sunrise Banks adopted paperwork automation technology earlier this year to increase their ability to process Paycheck Protection Program loans from 85 to almost 500 per day, thereby allowing them to extend their lending capabilities beyond their existing customer base.
Thoughtful automation is crucial for survival
Embracing new automation technologies that allow community banks to match customer expectations while improving profitability is the key to long-term sustainable growth and success. A majority of bank CEOs recognize this and believe that, without agility, they would likely face bankruptcy.
While poorly applied automation technology can be an expensive way to create a bad customer experience, it is undeniable that meeting evolving customer expectations demands thoughtful application of such technologies. To avoid automation for the sake of automation, community banks should evaluate solutions based on their ability to improve the customer experience and the bank’s bottom line.
Ultimately, bank leaders should think about their top challenges and how automation can be a part of the solution, consider if the organization is ready for an investment in both time and money, and if they have the infrastructure in place to support thoughtful automation.
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