THE NEXT BIG WAVE: HOW FINANCIAL INSTITUTIONS … · a research paper from finextra in association with intel may 2017 the next big wave: how financial institutions can stay ahead

A RESEARCH PAPER FROM FINEXTRA IN ASSOCIATION WITH INTEL MAY 2017

THE NEXT BIG WAVE: HOW FINANCIAL INSTITUTIONS CAN STAY AHEAD OF THE AI REVOLUTION

01 Foreword .............................................................. 3 02 Introduction .......................................................... 5

03 Which AI technologies should banks be focusing on? ..................................................... 6

04 What are the key applications of AI in financial services? .....................................10

05 The Challenges: Technology ...................................13

06 The Challenges: People and culture ........................18

07 What are the risks of engaging with AI? ..................23

08 … And the risks of not engaging? ............................27

09 Where can banks look for AI inspiration? ................29

10 Conclusion: Finextra’s five key takeaways ...............30

11 About ..................................................................317 What should financial institutions be doing about blockchain right now? 25

| THE NEXT BIG WAVE: HOW

FINANCIAL INSTITUTIONS CAN STAY AHEAD OF THE AI REVOLUTION

03

01FOREWORD

It is clear that Artificial Intelligence (AI) is the next big wave in computing, but it’s even bigger than that. It’s poised to usher in a better world, on the order of major transformations before it, like the agricultural revolution, the industrial revolution and the information age, accelerating solutions to large-scale problems, unleashing new scientific discovery, extending our human senses and capabilities, and automating undesirable tasks.

AI transformation is now possible because of the convergence of data and compute-compute breakthrough supporting the intense demands of machine learning, the wealth of data and the innovation surge all around us.

Today, we are just scratching the surface of AI. In order to unleash the next wave of AI, there is much work to be done as an industry to move past challenges in performance, accessibility and wariness of this technology. As a technology leader invested in the growth of AI, Intel is committed to fueling the proliferation of ever more intelligent, robust, collaborative and responsible AI solutions.

Advanced analytics and AI requires capabilities to manage and process the data in real-time, overcoming the barriers of legacy and data silos, through an agile, scalable and open architecture optimised for data.

By Mike Blalock General Manager, Intel Financial Services

AI: CHANGING OUR WORLD FOR THE BETTER



04

Intel’s vision is to both democratise and accelerate the potential for AI by delivering a complete portfolio of capabilities for machine learning, deep learning and memory-based reasoning in a software to silicon optimised stack that can accelerate transformation and time-to-value in financial services.

In short, AI has the power to change society for the better, and will bring new capabilities to everything from smart factories, to driverless cars, to new financial services, to advances in healthcare.

Data is the common thread across all of these applications, and our strategy is to make Intel the driving force of the data revolution across every industry. Intel has committed to three key areas to get us there. These are:

1) Developing a broad product portfolio benefiting from the advancements of Moore’s Law

2) Investing in training and resources to help make AI technologies accessible for everyone, and

3) Fostering beneficial uses of AI technology through partnerships and investments.

Through these collaborations, we expect a massive leap forward in AI solution delivery in the next few years. For more information visit our Intel AI web page and other resources: Intel Financial Services website and AI: Transforming Banking.

“ Today, we are just scratching the surface of AI. In order to unleash the next wave of AI, there is much work to be done as an industry to move past challenges in performance, accessibility and wariness of this technology.”

http://www.intel.com/content/www/us/en/analytics/artificial-intelligence/overview.html

http://www.intel.com/content/www/us/en/analytics/artificial-intelligence/overview.html

http://www.intel.co.uk/financialservices

https://www-ssl.intel.com/content/www/uk/en/it-managers/fintech-ai-banking.html?cid=em-elq-25856&utm_source=elq&utm_medium=email&utm_campaign=25856&elq_cid=33859

https://www-ssl.intel.com/content/www/uk/en/it-managers/fintech-ai-banking.html?cid=em-elq-25856&utm_source=elq&utm_medium=email&utm_campaign=25856&elq_cid=33859

02INTRODUCTION



05There is no doubt that AI represents the next wave of computing and will unleash major change in all industries, bringing benefits in all aspects of our lives. For financial institutions, AI represents an opportunity to radically improve efficiency, risk management and fraud detection as well as customer service.

As Roberto Ferrari, Managing Director, CheBanca!, puts it: “AI will become the most defining technology of the new banking and financial services of the future.”

But to stay ahead of the AI revolution, financial institutions need to navigate a complex array of AI techniques, possible application areas, cultural challenges and technology decisions, to ensure they lay solid foundations for their AI-driven futures.

The research paper, produced by Finextra in association with Intel, brings together the views of a broad range of AI experts and users from across the financial industry on how to tackle the key challenges and opportunities banks face as they embark on their AI journeys.

The paper addresses a number of important questions including:– What are the key AI techniques financial institutions should be exploring?– In which application areas does AI have the most potential now – and in

the future?– What are the technology and cultural considerations banks must address to

move forward with AI?– What are the risks banks face in the AI space – and what risks do they face

if they don’t engage?– What should the leading providers of technology financial institutions be

doing to support banks in exploiting AI, and are they delivering so far?– To which leading examples of AI success can banks look, and what can

the financial industry learn from other industries when it comes to taking advantage of AI?



06

03WHICH AI TECHNOLOGIES SHOULD BANKS BE FOCUSING ON?

Artificial intelligence (AI) is not new. In fact, as Steve Ellis, Head of the Innovation Group at Wells Fargo, points out, “it’s a concept that has been around for 60 years”: “2001: A Space Odyssey – that was AI. The promise of machines being able to think and act like people has been around for a long time.”

David Bannister, Principal Analyst, Financial Services Technology, Ovum, agrees. “We saw investment businesses in the 1980s trying to implement self-improving, goal-orientated algorithms – and I first saw voice recognition in 1983 in a chip factory in Japan.”

Old it may be, but it is also very hot right now, following “a rebirth in the significance of these types of tools”, says Ellis. This is no surprise, suggests Frank Derks, Head of Advanced Analytics at ING. “During the past 18 months the words artificial intelligence have certainly been used more often – but this is logical, as computer speed is increasing, and data science is expanding.”

Indeed, there is a perfect storm of factors pushing AI into the limelight. As Julie Conroy, Research Director, Aite Group, says: “We are finally seeing this convergence of advanced analytics, with a robust set of data, at a time when data storage costs are incredibly low, and so bringing those three elements together you can actually leverage this vast amount of data that is generated today, apply advanced analytics to create truly meaningful intelligence and that can inform not only better customer experiences but also much better risk controls.”

Ken Dodelin, Vice President of Digital Product Management at Capital One, shares this excitement. “Recent advances in technology have unleashed the potential of machine learning, including increased computer power, access to significantly more data, algorithmic advances in neural networks and deep learning, and open source machine learning software,” he says. “This is really exciting for the financial industry because it will open up the potential to help people with their financial lives in ways that we can’t even imagine today.”



07Technology developments in recent years have certainly brought the promise of AI first identified so many decades ago closer to reality, agrees Matt Cox, Head of Insight and Innovation at Nationwide Building Society. “There is a new breed of emerging technologies that can take in and make decisions on huge amounts of data, especially unstructured data including images and speech, and there has been a massive step up in the capability to do that in the past few years. Now that we have all this data to apply algorithms to, this is driving a new wave of interest – and all these techniques are possible now at scale, in a way that they just were not before now. The ability to apply these techniques to huge amounts of data at speed in real-time is what offers the real potential to be a game-changer going forward,” he says.

AI as a term is pretty meaningless of course: in reality there is a “spectrum” of technologies, Cox suggests. “At the simple end, there are solutions around robotic process automation (RPA) and natural language processing, and at the other end of the scale there is cognitive computing and machine learning. A lot of solutions claim to be leading edge AI but are actually at the simple end and I have to work hard to see where different offerings fit on the spectrum.”For some observers, the question of which AI technologies are important is less critical than what can be done with such tools. As Dodelin puts it: “The most important component is to be thoughtful about the ‘why’ behind the application. Leveraging AI technology is only worthwhile if you can connect the dots between why it will make someone’s life better and how it will actually go about accomplishing that goal.”

Jason Mars, Co-Founder and CEO of Clinc, makes a similar point. “AI is something of a buzzword right now. There’s a coolness factor, but the lasting impact of AI will be where it reduces complexity in the lives of people.” Hari Gopalkrishnan, Client Facing Platforms Technology Executive for Bank of America Merrill Lynch (BAML), confirms: “We clearly think in everything we do about how to make our customers’ financial lives easier, and the capabilities we could bring to bear to do that.”

“ There is a new breed of emerging technologies that can take in and make decisions on huge amounts of data, especially unstructured data including images and speech, and there has been a massive step up in the capability to do that in the past few years. Now that we have all this data to apply algorithms to, this is driving a new wave of interest – and all these techniques are possible now at scale, in a way that they just were not before now.” MATT COX, NATIONWIDE BUILDING SOCIETY



08

Julia Krauwer, Artificial Intelligence Expert at ABN AMRO, agrees. “I prefer not to start with the technique, but would much rather ask the question: What do we want to achieve and whom do we help by doing so? Do we want to give our clients more personal advice, more timely advice? Do we want to start offering new means of communicating with our clients, such as intelligent virtual assistants? Do we want to empower our employees by taking repetitive tasks out of their hands? Do we want to enhance the way we assess risks?”

That said, there is clearly a strong focus on the solutions at the more sophisticated end of the AI spectrum. “Although the go-to technique could be derived from the answer to the aforementioned question, I do see deep learning attracting a lot of attention nowadays,” continues Krauwer. “Deep learning is a technology that is loosely inspired by the way the human brain works, and is very well suited for learning from big volumes of unstructured data, such as images, voice and even text. Its ability to detect non-linear relationships could help banks move from using linear, expert-based models with set variables to models that expose relationships humans could not even have thought of.”

Deep learning and machine learning are certainly featuring strongly in the financial industry’s thinking about AI, confirms Francesco Gadaleta, Chief Data Officer, Abe. “Deep learning allows machines to simulate what a human brain does to solve a given task,” he says. “These are complex algorithms. They are data hungry. They need a lot of data to work properly and financial institutions have now in the past few years got together the large data sets they need.”

His colleague, Abe CEO Rob Guilfoyle, says deep learning is one of the two main areas of focus for the AI start-up. “We look at two types of implementation of AI,” he says. One is how deep learning and machine learning can “help you derive insights from your data you couldn’t otherwise see”. The other is “conversational AI,” he continues. “Conversational AI involves utilising natural language processing (NLP) and natural language understanding (NLU) as a means for banks to communicate with individuals concerning their banking needs,” Guilfoyle adds.

Gopalkrishnan from BAML also highlights the importance of conversational AI in enhancing “human computer understanding”. “We have the ability to ease the process for people dealing with computer systems through speech recognition and NLP. So when a customer says, I dropped my card at the gym, can you fix it?, the computer understands what that means and reacts accordingly,” he says.



09

“Talking to something that can understand me and do things I would like it to do – that’s the real promise here,” agrees Wells Fargo’s Ellis. “We are in the early ideas of being able to talk to a device and it understanding me and doing the things I ask, but we think that will be the standard way we interface with our homes, cars, fridges. We also think that banking fits very well inside these common platforms: financial services is essentially digital, so it fits well in the new ways of interacting with the world around us,” he says.

Oliver Bussmann of Bussmann Advisory and former Group Chief Information Officer of UBS highlights a third important AI component: RPA. “We are seeing banks using RPA to automate existing legacy environments, especially in the middle and back offices, to streamline them,” he says.

Robotic automation techniques are at the simpler end of the AI spectrum, but no less important for that. As Philippe Ruault, Head of Innovation and New Digital Business Lab for BNP Paribas Securities Services, points out, while the bank is using NLP and natural language generation (NLG) (to parse and process unformatted information such as tax and collateral contracts) and machine learning (in an application to manage investment fund compliance), it decided to start its AI journey “with the most mature and most easy to deploy technologies which are around process automation”. “Our people on the ground really expect more tools to optimise their processes, and it is also a requirement from our clients to get more bandwidth, volume absorption, better opening times, lower latency and so on,” he says.

Overall, there are a number of components in play to enable the potential of AI in financial services, believes Derks at ING. “The main ingredients are data engineering and data science, including neural networks and machine learning,” he says. “We think that using these techniques we can emulate human qualities, understand voices and develop intuition. Based on these capabilities we see the development of chatbots and agents, and robots to take on certain human jobs – and the delivery of augmented insights to the account managers who work with our clients. These are the three examples that are already implemented and are available now – although the maturity of the implementation of augmented intelligence will definitely increase in the future.”

Recognising AI for the broad church it is and keeping on top of the wide range of developments will be important, believes Foteini Agrafioti, Chief Science Officer, Head RBC Research. “There are a variety of fields of study and techniques within AI that each has their own use cases and ideal applications. It makes sense for financial institutions to explore a variety of areas within AI to use the science at its full potential.”



10

04WHAT ARE THE KEY APPLICATIONS OF AI IN FINANCIAL SERVICES?

This is a difficult question to answer precisely right now, suggests Ferrari at CheBanca! “It is hard to say given the very large spectrum and the very early stage of the technology,” he explains. “It will take time, but the technology is harnessing in many directions. More in the short term I believe digitalisation of simple customer services tasks is something most of the players are looking at (so- called conversational banking and virtual assistant). The areas of anti-fraud and risk management and process automation are also very hot and promising. But the spectrum is very large, and this is not just a new technology to be used here and there: it is totally pervasive and will redefine the whole industry in the long term, when machine and deep learning get to the right maturity and applicability in finance.”

The uncertainty notwithstanding, observers tend to agree on the broad application areas for AI. “At ING we are looking at four key topics,” says Derks. “Optimising processes on behalf of clients, working to create hyper-personalised interactions, applying AI to augment decision-making and developing new products and services to offer to clients.”

Hopes are high for AI in the area of customer interactions. As Capital One’s Dodelin puts it: “There is so much opportunity to harness data and analytics to become a trusted advisor and partner for our customers, creating interactions that go beyond transactions to create experiences that add value and help you succeed in your financial life. We’re going beyond what the customer wants, to identify what they don’t even know they want yet, and delivering the products and tools to help manage their money simply and with confidence.”

The potential to significantly improve customer experience is strong, according to Gadaleta at Abe. “A chatbot is not just for when the customer wants to talk to the bank, but also for when the bank wants to talk to the customer,” he says. “An expensive human resource is never going to check in with a customer on the phone just to confirm that a loan decision is still pending, for example, but a chatbot could definitely start a quick conversation about that.”



11

Being able to ask a chatbot your credit card balance or what Microsoft is trading at is clearly an order of magnitude easier than looking such information up by more traditional means, says Alan McIntyre, Senior Managing Director, Global Banking, Accenture, and “AI sits behind that type of service - a whole series of applications including natural language processing and machine learning which means the system improves over time”, he says. “Customers have been trained by Amazon and others to trust automated advice in a way that was not true a few years ago. Now if Amazon recommends something to you, it is based on a trust that it understands your behaviour. That is the goal for financial services: personalisation, and AI is one of the catalysts for banks to move away from thinking about advice as something that happens occasionally to thinking of it as something that happens in real-time, in the moment, in context, with a strong degree of personalisation. This is all part of banks moving towards making our lives better.”

Improved, personalised customer interactions can also lead to more revenue generation, adds Gopalkrishnan from BAML. “The more we can anticipate needs, the more we can offer to customers. Knowing where in their lives they are could be the difference between customers deepening their wallets with us, or going somewhere else.”

Meanwhile, AI is already making an impact in fraud management, says Dodelin at Capital One. “As a historically data-driven company, machine learning is not new to Capital One, but it is an area we feel is ripe with opportunity – and one that has applications in all major areas of our business. We’re currently harnessing the power of machine learning and artificial intelligence for fraud protection and cybersecurity.”

Indeed, using AI in areas such as fraud, security and AML is leveraging the “traditional power of AI – pattern recognition”, points out McIntyre. “For these back end aspects, it is not only about improving speed and taking out costs – sometimes these systems can make even better decisions,” he says.

“ Customers have been trained by Amazon and others to trust automated advice in a way that was not true a few years ago. Now if Amazon recommends something to you, it is based on a trust that it understands your behaviour. That is the goal for financial services: personalisation, and AI is one of the catalysts for banks to move away from thinking about advice as something that happens occasionally to thinking of it as something that happens in real-time, in the moment, in context, with a strong degree of personalisation.” ALAN MCINTYRE, ACCENTURE



12

Conroy at Aite agrees. “The application of machine learning techniques to fraud prevention is an area where we’re seeing a lot of production installations that are bearing some very significant increases in detection capability,” she says. “I think in the future we’ll see that extend to other risk areas. In fact, I recently spoke with a bank that has applied machine learning to customer onboarding, and has not only significantly reduced the identity fraud it was experiencing but also, because it was making much better use of data from internal and external data sources, was able to increase the number of consumers that it actually approved. That’s a real win-win-win: more revenue, less fraud, less expense.”

AI has real potential to modernise and simplify customer onboarding, believes Husayn Kassai, CEO and Co-Founder at Onfido. “Traditionally, only complex and expensive software has been able to scan and compute identity documents (for example airport scanners),” he says. “What’s cool about machine learning is that it will help us accurately identify documents which have been uploaded using commodity technology (scanners, low-res smartphones et cetera). All you need is a basic smartphone and internet access, which allows even people in developing countries to access financial services online.”

Corporate customers too are keen to tap into improved safety through their banks’ deployment of AI, suggests Tom Durkin, Managing Director and Head of Global Digital Channels for Global Transaction Services at Bank of America Merrill Lynch. “If there is one thing corporate customers want to leverage it’s how we can help them better protect their assets,” he says “Our customers know we can derive deeper insights, especially as they go global as part of extending their supply chains globally, and they want to tap more into our understanding to help them get on top of the threats they face.”

“ One should keep in mind that AI is not a plug-and-play solution. For most bank-specific purposes, you will need large quantities of data and a great amount of effort to train the models that lead to intelligence.” JULIA KRAUWER, ABN AMRO



13

05THE CHALLENGES: TECHNOLOGY

One of the most obvious technology challenges related to AI is that, even though such solutions have been around for decades, not all of them work very well yet. As Cox at Nationwide says: “We see lots of personal assistants and chatbots but for me they are quite immature. They only do very simple tasks and activities. It’s interesting for those members who want to engage in this way – but until they get more sophisticated I think they will struggle to deliver huge value.”

Key technologies such as NLP, NLU and NLG are improving, but still have some way to go, agrees Ovum’s Bannister. “These solutions used to be terrible. They have certainly improved in leaps and bounds – but to get to the next level, eliminating all elements which lead to misunderstandings, will take another 3-5 years at least.”

Kassai at Onfido says language is “probably the biggest blocker to commercial AI”. “We still haven’t cracked how (nor do we entirely know if it’s possible) to get a computer to fully understand human beings,” he says. “A lot of work needs to go into this to make it available to everyone in commercial products, especially when we want human beings to directly interact with AI.”

By far the most often-cited technology challenge related to AI implementation is of course data. As ABN Amro’s Krauwer says, “one should keep in mind that AI is not a plug-and-play solution”: “For most bank-specific purposes, you will need large quantities of data and a great amount of effort to train the models that lead to intelligence.”

And that “heavy lifting around collecting, processing and analysing the data” is “a huge task”, warns Accenture’s McIntyre. “It’s not necessarily the sexy part but we mustn’t lose sight of that data supply chain,” he says. “There is a lot of work to be done, because a lot of that data is very fragmented.”



14 Hard though the task is, it simply must be done, suggests Bussmann. “I think the critical success factor is all about data and data quality,” he says. “If you have bad data, it’s garbage in, garbage out. It’s all about having your house in order with respect to data – from a data governance perspective, data ownership, how you manage the data in the organisation. This is becoming a major topic within the banks and is a prerequisite, especially to take advantage of new technologies such as AI.”

Ruault confirms this is a priority for BNP Paribas Securities Services. “We really have a big plan around leveraging the data that we have to predict customer behavior and customer needs and anticipate requirements,” he says. “A great deal of our focus will be on leveraging data to optimise the model.”

The banks’ problem is clearly not lack of data but rather its usability. As Gadaleta at Abe says: “The problem we get with machine learning is integrating heterogenous data. On one side, all the silos are crashing down and creating a huge pool of data but on the other side it can be very difficult to integrate the data. Everybody thinks that deep learning can solve a problem in a nanosecond, but it cannot do so without the data.”

As is so often the case, legacy challenges are at the heart of the matter. “One of the biggest challenges banks talk to me about is just getting their data together in a usable state, overcoming the barriers created by all of this legacy technology that exists in banks at present,” says Aite’s Conroy. Nationwide’s Cox agrees. Nationwide’s Cox agrees. “Data is at the heart of this. We need good control and governance because the value of the machine learning is only as good as the data that we are putting in. This requires a level of control and governance to address the impact of decades of legacy data.”

Indeed, the imperative for banks to tackle their legacy challenges is intensified by their desire to leverage AI. In the current environment, “achieving a true end-to-end experience is difficult because it involves interfacing with old legacy systems – patching across to things which are not as nimble or agile as the front end you have built”, says Accenture’s McIntyre.

“ The problem we get with machine learning is integrating heterogenous data. On one side, all the silos are crashing down and creating a huge pool of data but on the other side it can be very difficult to integrate the data. Everybody thinks that deep learning can solve a problem in a nanosecond, but it cannot do so without the data.” FRANCESCO GADALETA, ABE



15

Adds Wells Fargo’s Ellis: “If you want to create a really good upfront experience you can’t take existing back end processes and put new front ends on them. You have got to think end to end. Using AI for customer service at the front end will change the way we have to approach the back end. We have to digitize a process end-to-end, get rid of all paper, and use RPA tools to perform rudimentary manual tasks.”

As financial institutions look out into the supplier marketplace, there is at least no shortage of potential partners to work with in transforming their operations to be AI ready. As Bussmann points out, “there is a significant start-up community”. “Over the last three years across all industries almost $6 billion has been invested in AI start-ups,” he says. “Supporting these very domain specific AI use cases I think is very important for the future and there’s a significant opportunity for technology provider start-ups and service providers to help the banks to come up to speed as fast as possible.”

Speeding up banks’ AI journeys is clearly a way in which technology providers can usefully contribute. Mars at Clinc says: “We see that banks are very keen to have this functionality: they all want to have their own Siri in their mobile app. But to build their own virtual assistant they have to think about every possible variation and their back end systems are so messy that the app breaks, and then people lose trust. It’s a tedious process to build your own virtual assistant, but products like ours are really quick to train, and get better and better over time.”

Indeed, “there is a whole range of products which offer personal assistant and chatbot services and they are becoming increasingly sophisticated”, agrees Cox at Nationwide. “Our member trust us with their information and we need to be able to control, govern and manage our data –there has to be a good handover to a human operator. There are some aspects for which we will partner and some aspects we have to do ourselves. We are a firm believer that while digital provides convenience, it is people that provide the service.”

“ If you want to create a really good upfront experience you can’t take existing back end processes and put new front ends on them. You have got to think end to end. Using AI for customer service at the front end will change the way we have to approach the back end.” STEVE ELLIS, WELLS FARGO



16

Aite’s Conroy agrees that the vendor community has a role in expediting the financial industry’s successful adoption of AI, but points out that it won’t always be plain sailing. “I think many of the cases are can you help speed banks’ progress towards this, help them better connect their silos between their data, are there managed services offerings, cloud based offerings that you can bring to bear to help the banks get out of their own way?” she says.

“I think we’re seeing a willingness of technology providers to do this, and we are seeing some capacity out there, but I also think it’s still early days. The other challenge is that the banks have to be comfortable relinquishing some of these activities and engaging with some of these cloud-based environments in order to speed their progress. That’s one of the major hurdles I hear about as I talk to many banks about this.”

Effective partnering with the supplier community is critical, believes ING’s Derks. “We work with big technology providers as well as small ones, and to be honest we learn from both,” he says. “There are upsides and downsides of interacting with them. The big players standardise more but are less flexible, and the small players give us a huge opportunity to innovate with these new technologies. The strength is in working with both.”

For Ruault at BNP Paribas Securities Services, this partnership ethos is evolving well. “I hear much more about co-operation between financial institutions and leading technology providers, to perform proofs of concept and then jointly assess where further developments make sense. Financial institutions are more and more partnering with each other and also fintechs to work in a much more co-operative way,” he says.

There can of course be challenges in making co-operation work, cautions Kassai at Onfido. “While AI solutions are already being used by more agile online banks, AI is seeing slower adoption by more risk-averse, traditional institutions – but it is only by being open to these new technologies will financial services across the spectrum be able to stay competitive,” he says. “However, without the infrastructure in place at a high level to support this, it is difficult for incumbent financial services to embrace innovation without exposing themselves to risk. Initiatives like industry sandboxes can be really helpful in encouraging collaborative innovation in a safe space, and will hopefully drive further uptake and development of AI solutions.”



17

To make a truly valuable contribution, the vendor community does need to recognise and respond to the real needs of the financial services industry around AI, however. “The risk is that we see in the next decade what was done about 15 years ago with CRM: suppliers just selling as much software as they can without really building long term value for the banks,” says Ferrari at CheBanca! “This is something leading vendors should avoid if they want to have a long term success with their clients, aiming instead to custom-build solutions as a real partner, as there is here a real need for understanding and developing as you go from both sides.”

Observers also suggest that the supplier community needs to present a more coherent picture of what is on offer, and how it fits together, to help banks progress. “There are so many different providers, it is hard to see how they all can connect and work with us to solve the problems we have,” says Cox at Nationwide. “I don’t think any of us, including the suppliers, know how to work together to deliver a single experience quickly, cheaply and at scale. There is no shortage of AI solutions – but there is not yet an ecosystem of new technologies to plug into,” he adds.

Indeed there is no “end-to-end technology solution”, says Gopalkrishnan at BAML. “There are different pieces, some of which are available off the shelf, others trying to present themselves as commodities that we can buy but in reality we are nowhere near that yet,” he explains. “For every piece of the solution we ask, is this something we could get off the shelf?”

The second question, he says, is “how do we interface between the solution and our existing ecosystem?” Once again, everything comes back to the data – and the vendor community understanding the banks’ data challenges is vital, observers say. “I was at a trade show recently and there were Amazon Echos everywhere, each with the message that the solution using them can be easily integrated,” recalls Durkin from BAML. “The fact is that the underlying data set needs to be well-established and the ease of doing this depends on the degree of legacy in place. Everything relies on good data and we have to make that good data underneath: we can’t just buy into every bit of hype that we see.”

Cox at Nationwide agrees. “If we started to provide an Amazon Echo type experience for all interactions, what would the technology provider give us? The algorithms and the ability to translate speech and understand it and deliver it back,” he says. “On our side the work on our data and developing APIs for them to connect into would be huge. Many of the suppliers in the marketplace don’t get that what they provide is just the tip of the iceberg.”



18

06THE CHALLENGES: PEOPLE AND CULTURE

Putting in place the right culture within a bank to ensure success with AI will likely require some changes, suggests Ferrari at CheBanca! “There needs to be a mindset shift that requires the bank to understand (and sponsor) the fact that digital comes first,” he says. “It is not just a channel, it is a way of rethinking and designing processes, products and customer relationships in a very integrated manner across all the channels you want to use to serve your customers. It about renovating the way financial institutions think and work, inside out. AI is at the very core of the fourth industrial revolution, and this is just a matter of understanding that and behaving consequently - allocating resources, but most of all allocating the right priority from the top the organisation, and making decision processes accordingly faster and leaner.”

Ruault at BNP Paribas Securities Services also sees a need for organisational change. “What’s important for these technologies is to make sure that we have teams organised to allow them to be explored and tested in ways that match business needs and client requirements,” he says. “You need to implement an innovation culture so that people are open to thinking about the impact of these technologies on their day to day working lives. We have created a digital team, whose members can work and test with the different teams in operations and IT technologies which will bring value to our customers and ourselves. It’s a combination of culture, and being open to innovation,” Ruault continues.

Cox at Nationwide agrees firms must “make sure that our business models are revisited in line with this change”. “This technology will be part of every business unit in the organisation: they will all need to be consumers of it and identify opportunities from using it,” he says.

Banks’ AI approaches can also expose some skills gaps within banks, Ruault adds. “Obviously, we need some new talents. It is possible to find these internally through training and indeed we have found we have very good people internally, people who, with some training, can help us make this transformation. In addition, we need some younger people, and this is what we are seeking.”



19Finding the right people to take the AI revolution forward will be a challenge for “every industry, not just banking”, suggests Guilfoyle at Abe. “When you think of the types of individuals that have been around deep learning for more than a few years, it’s usually academics,” he says. “The important factor to recognise is that it’s not about whether you wear sweatpants or a suit: it’s more about what change you are trying to effect within the industry.”

Onfido’s Kassai agrees there is a need to better disseminate AI understanding and access within organisations. “We are at the stage where many people understand the potentially transformative nature of AI and machine learning, but the technology is still largely locked in labs and R&D departments,” he says. “Work like Elon Musk’s on ‘democratising access to AI’ is fundamental to fully achieve the real potential of AI.”

Education is also required about the opportunities of AI and how AI will impact in practice, in order to dispel the “anxiety around whether AI is going to replace humans and take jobs”, Kassai says.

This fear has been overblown, suggest observers. As Accenture’s McIntyre says: “Whenever you get productivity improvements, you see a fall in headcount, but that is not the same as the whole headcount across the industry falling. As we apply these technologies we create other roles. There was no job in financial services even five years ago that was developing a robo adviser. Creative disruption is how the industry evolves.”

Wells Fargo’s Ellis agrees. “Yes, AI changes the nature of the role of people in a process – but technology has been doing that for the past 20 years,” he says. “This is just another tool and people will continue to add value, because the choice isn’t black and white around financial product options: it’s not like an algebra equation where there is a right or a wrong answer. A lot of financial decisions have pluses and minuses over the long term and related to how customers think about risk. These are very human types of questions, more suited to being answered by humans, who are emotional, rather than machines, which are rational.”

“ We are at the stage where many people understand the potentially transformative nature of AI and machine learning, but the technology is still largely locked in labs and R&D departments. Work like Elon Musk’s on ‘democratising access to AI’ is fundamental to fully achieve the real potential of AI.” HUSAYN KASSAI, ONFIDO



20

The “human touch” will remain crucial in financial services, Ellis suggests. “There are lots of areas in which people still need to talk to somebody. I think we should be really careful about how we think about AI. This is not just about digital. In the past we had a branch platform and we hooked digital services into that. In the future we will have a digital platform with people supporting it.”

This shift will create intrinsically more interesting roles in banking, says BAML’s Durkin. “When I think about the role of the dedicated support person who works in corporate client servicing – who knows the customer organisation so well – I see that position moving to a different level with the advent of AI to automate more rudimentary tasks,” he explains. “When you use technology to move the more transactional elements away, that elevates the role of the trusted adviser, propelling them into a position that is more important and challenging than fact-finding or checking. It’s an interesting evolution.”

For Conroy, too, there will continue to be a strong role for people in banking. “We see a lot of talk of various AI capabilities coming in and digitising the experience and replacing the human element, but there is still in many areas of banking the need for the human element, whether that be on the fraud side where you still need human beings to review the output of some of the analytics and determine what is truly risky and what is not, or on the marketing side where there are still many cases where a customer wants to interact with a person,” she says.

“The challenge is finding the right balance between these good innovations and technologies and keeping the human element in banking - and that’s going to be a balance that we’ll find by trial and error,” she suggests.

Financial institutions do need to play fair by their employees, however. “For our colleagues, we need to make sure that in the short term the technology is working alongside them and that they are empowered and skilled to be able to extract benefit from it for our members and themselves,” says Cox at Nationwide. “We need to manage their transition away from basic tasks to more sophisticated roles, that 5, 10 or 15-year drift-up the stack in terms of skillset.”

“ When I think about the role of the dedicated support person who works in corporate client servicing – who knows the customer organisation so well – I see that position moving to a different level with the advent of AI to automate more rudimentary tasks.” TOM DURKIN, BANK OF AMERICA MERRILL LYNCH



21

There is also a responsibility towards members, he continues. “We need to be sure that we are clear with them when they are talking to a bot – because it won’t always be obvious when clever natural language processing is in play – and when they are handed off to a human operator.”

The way financial institutions interact with their customers as they develop AI-based services will be critical, suggests BAML’s Durkin. “It is important to remember the aspect of learning from our customers,” he says. “When we look at the different proofs of concept we have done, we can see there is so much value for us when our customers engage.”

Krauwer at ABN AMRO agrees co-creation with customers is essential. “Whether clients will use a certain AI-powered service very much depends on their wishes and needs,” she says. “Some clients wish to turn to an actual employee with their questions, others just want their questions to be answered as quickly as possible and prefer a virtual assistant that is available 24/7. It also depends on the specific case. For example, some cases are sensitive or highly complex and therefore require empathy or creative problem solving, making human involvement indispensable. It’s easy to make assumptions on this matter, so we consider it essential to keep our clients closely involved when experimenting with new technologies. This way, we can learn what does and what doesn’t work for them.”

An important factor in making sure new AI-led services work for customers will be successfully treading the “fine line between helpful and creepy”, reckons Accenture’s McIntyre. “The banks will have to be careful not to make use of the information they gather through transactions to make inappropriate offers,” he says. “This technology can gather and use a tremendous amount of information, and clearly some thought needs to go into what constitutes a healthy customer intimacy versus a creepy one. Our research shows there is a growing demand for reciprocity. If customers provide information they expect something in return: they give information so their providers will serve them better in the future, either with better recommendations or better offers.”

“ Banks need to examine pretty closely some of the marketing use cases that become possible when they can leverage data to provide better insights and recommendations. You don’t want to come across as looking like Big Brother.” JULIE CONROY, AITE GROUP



22

Aite’s Conroy too cautions against “straying into that creepy zone for your customer”. “Banks need to examine pretty closely some of the marketing use cases that become possible when they can leverage data to provide better insights and recommendations,” she says. “You don’t want to come across as looking like Big Brother.”

In short, as McIntyre puts it “there is a judgement factor in providing appropriate advice”. There is also a judgement factor to consider when it comes to the decisions AI-driven solutions make, says Cox at Nationwide: “The difference with this technology is that at its heart it is a technology making decisions based on data it’s being fed. There is an ethical question around it making decisions – and how we control the output to ensure the best outcome for our members.”

Gopalkrishnan from BAML says this is a question of “learning as we go along”. “If a customer asks a virtual assistant, what is my balance?, the assistant needs to work out on which product,” he says. “This can be determined from use patterns and the state of different accounts, but while this is a simple thing for a human to do, it is not necessarily simple for a programme. When a system is trying to think for itself, it has to apply judgement. It’s part of the journey to make sure that judgements are properly monitored,” he suggests.

Critical to this judgement capability will be ensuring that the AI mirrors the diversity of actual human life, believes Ellis at Wells Fargo. “When you think about what we did when the web came out and became commercialised, we built virtual stores. Now we are building virtual people,” he says. “Life is pretty diverse – gender, ethnicity, age – and we have a better chance of getting AI right if we ensure it reflects that diversity. The opportunity we get with AI is to think of every single consumer as an individual. They are all different. And if we don’t mimic that difference, we could miss the opportunity.”

“ If you utilise a new process automation tool and it’s not successfully set up, then it can cause a problem on a much wider scale than when a person is carrying out the task. To prevent this, AI needs to be implemented in a very proper way.” PHILIPPE RUAULT, BNP PARIBAS SECURITIES SERVICES



23

07WHAT ARE THE RISKS OF ENGAGING WITH AI?

In addition to the technology and cultural challenges discussed above, observers identify a number of specific risks inherent in AI implementations. One is a risk of malfunction, says Ruault from BNP Paribas Securities Services. “Implementing these new technologies can create additional operational risk,” he suggests. “If you utilise a new process automation tool and it’s not successfully set up, then it can cause a problem on a much wider scale than when a person is carrying out the task. To prevent this, AI needs to be implemented in a very proper way. The way we organise this is by making sure that each and every process in which we will implement AI is signed off by people from legal, compliance and operational risk. We need to be very organised in the way AI is deployed,” he adds.

Another risk is algorithmic bias, says ABN AMRO’s Krauwer. “Models can’t look beyond the data they have been trained on, so whenever the training data is skewed or too narrow (and this is often the case), the model output will be biased too,” she says. “One should be aware that a model, however intelligent the output may seem, is a mere representation of reality. A model can help users grasp certain elements of reality (a prediction, a categorisation) but don’t necessarily show the complete picture. That is why a human in the loop is essential: we are, unlike machines, able to take into account context and use general knowledge to put AI-drawn conclusions into perspective.”

Given the central role of data in AI implementations, data privacy aspects must also be addressed, Krauwer adds. “AI is inherently data-driven, and this means banks (and every other company that is looking into AI) will have to make sure it is developed and maintained in a privacy-preserving way. In addition to complying to privacy-by-design principles, banks should be fully transparent about what data they use and for what purposes.”



24 With the General Data Protection Regulation (GDPR) coming into force in Europe next year, regulatory scrutiny on data privacy is set to intensify, McIntyre points out. Data security is paramount, agrees RBC Research’s Agrafioti. “As with any new technology there are always potential risks, and financial institutions in particular need to be thoughtful and careful about ensuring the safety and security of their data.”

Meanwhile, data privacy is not the only regulatory hazard for banks’ AI implementations, says Accenture’s McIntyre. Another challenge lies in convincing regulators that the best way to comply with regulation could be to deploy AI and cloud, he suggests. “Take the US mortgage industry which is trying to go digital very quickly. Some regulations impacting this industry still require multiple pieces of paper to be sent to the client, and as a result we see some very mixed customer experiences, not because the bank wants this, but because the regulator requires it. We do think banks need to step up and shape the regulatory agenda to help the regulators see the value of low risk, high efficiency solutions,” he adds.

The way in which deep learning works could also pose problems for banks with their regulators, as Guilfoyle at Abe points out. “The application and productisation of algorithms has been very difficult for the banks because the black box concept creates regulatory and compliance issues,” he says.McIntyre agrees. “The lack of an audit trail can be a problem,” he says. “When the regulator asks how you have come to a decision, pointing to the AI box in the corner and saying, that did it, is not going to be satisfactory. Realistically, the lack of audit trails restricts the types of technology that can be used. It will be essential to be able to explain decisions about relative risk and why one solution was deemed better for a customer than another.”

“ When implementing AI technologies, it’s crucial that we and other industry players act as responsibly as possible and with the customer in mind at all times. Since we operate in a heavily regulated industry, we’ve tapped explainable AI technologies so we can explain both internally and externally why we’re making decisions, and make sure we’re making decisions for the right reasons.” KEN DODELIN, CAPITAL ONE



25

In fact, AI adoptions holds out the potential for improved audit trails, McIntyre suggests. “We know through the work we are doing with the major consumer technology providers that they take very seriously the responsibility to document decisions that are made, to make sure they are not creating black boxes. There are actually a lot of opportunities with AI to create more of an audit trail. If you ask Alexa to make a trade for you, then that is recorded forever.”

Being able to explain decisions is essential, according to Dodelin at Capital One. “When implementing AI technologies, it’s crucial that we and other industry players act as responsibly as possible and with the customer in mind at all times,” he says. “Since we operate in a heavily regulated industry, we’ve tapped explainable AI technologies so we can explain both internally and externally why we’re making decisions, and make sure we’re making decisions for the right reasons.”

Gopalkrishnan from BAML agrees there is a “need to focus on areas which are value-added and where the right rules and regulations are in place”. Derks from ING adds: “We have to learn what it means if a machine comes up with a proposal for what to do and we don’t exactly understand why. Banks individually are finding out how to apply AI, and I think we have to define together what we do when the regulators ask for output.”

Collaboration is critical, agrees Agrafioti. “Since AI innovation requires access to massive amounts of data, it’s important for financial institutions and tech companies to come together and collectively try to address industry-wide challenges around access to data. The same can be said for other industries too, like healthcare.”

The question of responsibility in an AI driven world is a critical one, confirms Gadaleta at Abe. “I see a very realistic risk with deep learning which is about responsibility,” he says. “Soon we will have intelligent agents undertaking actions on behalf of real people and this presents the same problem as we see with self-driving cars. Who is responsible for an accident? The software engineer? The software manufacturer? The driver? Nobody?”

Ironically, AI working too well also presents a source of risk for banks, says McIntyre. “Our research shows that the digital experience banks offer is getting better every year, and that customers are more and more happy with it. But this shines a light on other interactions such as in the branch – which are not improving as quickly. So it feels worse, and the overall impression of the omnichannel experience deteriorates,” he explains.



26

“The teller may know less about the customer’s transactions than the customer’s own phone. There has been a lot of focus on the digital aspect, but are the banks yet able to deliver a consistently good broad-based experience? Nothing frustrates customers more than a great digital experience, followed by a request to print out forms and send them back. But that true end-to-end digital experience is in place in only a very small minority of financial services interactions. In fact, in the US, end-to-end, pure, digital origination that doesn’t involve a physical interaction or a physical document or a signature happens for only about 2% of the products purchased in the US.”

Another factor creating complexity for banks as they embark on the AI journey is the impact of the move to open banking, driven in Europe by regulation (the revised Payment Services Directive) but also a global trend. “In an open banking world, even if banks can continue to manage their customer relationships, they can’t provide everything,” says McIntyre. “Sometimes banks’ customers will come to them via other apps such as Facebook Messenger – and then how they pull those customers back into their own environments will be down to the quality of the advisory.”

Navigating the level of openness required while also tackling security concerns around technology such as cloud will also be a challenge, he suggests. “There are a lot of questions that banks must answer as they play in the open savannah of digital.”

“ There is a first-mover advantage for banks from an efficiency point of view, and you can imagine that especially in European markets that are not growing, efficiency is important from a cost-income ratio perspective.” OLIVER BUSSMANN, BUSSMANN ADVISORY



27

08… AND THE RISKS OF NOT ENGAGING?

Whatever the technology and cultural challenges on the path to exploiting AI to its full potential, banks have no choice but to push forward on their AI journeys. Future excellence in customer service depends upon it, says Krauwer at ABN AMRO. “A risk banks face if they don’t engage is missing out on an opportunity to service clients in a more personalised, frictionless and maybe even more proactive way,” she says.

“As consumers, already AI is infused in our daily lives in ways we may not even realise – from movie and music recommendations services to virtual personal assistants in our phone or in our home. Financial institutions that aren’t exploring new technology and the potential applications of AI could end up falling behind in the industry and losing touch with their clients,” adds RBC Research’s Agrafioti.

Nationwide’s Cox agrees. “In the long term if we do not engage we risk not being able to provide the kind of service our members expect, in the way they want to engage in them and in the most efficient way possible for the Society.”

Bussmann concurs that because AI is the new “UI” [user interface], as a bank “you risk falling behind the competition if you don’t explore these technologies”. The efficiency play is also critical, he adds. “There is a first-mover advantage for banks from an efficiency point of view, and you can imagine that especially in European markets that are not growing, efficiency is important from a cost-income ratio perspective.”

Ferraria at CheBanca!, makes a similar point. “If you are an incumbent, the very first risk you run is to be very slow and to underestimate the change. This will strongly erode your business in the long term - no doubt - but also in the short term you risk losing sight of new business opportunities that you could exploit if you had more courage and vision. Those business opportunities could give you a far stronger competitive advantage mid-term, much more efficiency, and will make you learn along the way,” he says.



28

The ever-present threat of disintermediation is also reinforced by the potential of AI, suggests Aite Group’s Conroy. “The risk if banks don’t engage is the risk of marginalisation,” she says. “Things are moving so fast that I think if you are not making ample and good use of your data and not using these techniques, you will get left behind by both your direct competitors as well as the many, many emerging start-ups and fintechs that are seeking to chip off bits and pieces of banking for themselves.”

Gopalkrishnan from BAML concurs. “If you look at where the customer is going, they are more and more using Siri and WhatsApp and WeChat. As a provider of consumer services if we are not where they are we will fall behind. Changing customer expectations and demands mean that if we don’t act now and get on the front foot, there is a high probability of being disintermediated.”

Kassai at Onfido, agrees. “Over the last 20 years we have seen big multinationals realise the power of start-ups and disruptive technology. To combat this existential threat, we’ve seen the launching of many corporate venture funds and innovation labs. But this isn’t enough: over the next 5-10 years we will see more and more of them launch AI labs as they gradually realise there is going to be a digital divide between those who leverage AI and those who don’t,” he says.

“ If you are an incumbent, the very first risk you run is to be very slow and to underestimate the change. This will strongly erode your business in the long term – no doubt – but also in the short term you risk losing sight of new business opportunities that you could exploit if you had more courage and vision.” ROBERTO FERRARI, CHEBANCA!



29

09WHERE CAN BANKS LOOK FOR AI INSPIRATION?

If it’s true that banks are earlier in their AI journeys than other industries, then where can they look for inspiration as to how to progress more quickly and more successfully?

Naturally, observers recommend taking a leaf out of the consumer tech industry’s book. “Look at what the tech industry is doing with AI from a voice recognition perspective, for example Amazon and Alexa,” says Bussmann. “All the tech companies now are providing AI-based use cases so that’s the new customer interface. Working with those tech players and building on how they are now utilising this technology is I think something that is definitely a must-do for banks.”

There is also inspiration to be found in some early Internet of Things (IoT) applications, suggests Aite’s Conroy. “One of the best examples for banks to look at is the internet of things and some of the use cases we are seeing there, things as mundane as smart garbage collecting using sensors that are communicating back to the grid, to help inform which bins need emptying and which don’t,” she says. “It’s possible to produce some very real efficiencies and savings through that type of activity.”

Studying the experiences of different activities is critical, believes Ruault of BNP Paribas Securities Services. “Looking at very sophisticated industries, and very different ones from banks, such as aerospace, pharmaceutical and automotive, is to me a very big benchmark for financial institutions - especially financial institutions like us, where process is the art of the game,” Ruault explains. “We are a big process factory, so looking at what has been done elsewhere in other industries is a big plus. These companies have optimised their processes, from the design of the product, up to the set-up of the robot that is building the car. To me they are quite ahead in the way they have streamlined their processes.”

The automotive industry certainly presents an interesting example of what rigorous automation of processes through robotics can achieve, agrees Bannister from Ovum. “Modern cars are very complex by comparison with my first VW Beetle, but their reliability and safety is extraordinary – and that’s what automation has brought to car manufacturing,” he says.



30

10CONCLUSION: FINEXTRA’S FIVE KEY TAKEAWAYS

1) No longer the stuff of science fiction, AI’s time has come, and banks must get on the bandwagon. Big data, faster processing and cheap storage make it possible to make the vision of AI in banking developed over decades real – today. The technology will only improve and not engaging today would be to run the risk of falling behind, never to catch up.

2) Customer interaction and experience, fraud management and process optimisation are the three most obvious application areas for AI in financial services. Customers are used to interacting with AI-based interfaces in other aspects of their lives, and they want to interact with their banks in the same way. The power of machine learning to do an immeasurably better job in spotting fraud, money laundering and evidence of cyber attacks cannot be ignored. And RPA and additional process automation tools hold out the prospect – finally – of achieving true, end-to-end straight-through processing, reducing risk and cutting costs at a time when this is essential.

3) People are afraid AI will cost them their jobs and are not always comfortable dealing with AI-driven interfaces – so financial institutions must tackle these ethical issues with their employees and their customers. To smooth the implementation of AI in financial services, firms must present a transition path for employees to help them understand their route up the stack into more skilled roles. They must also be transparent with their customers about when they are being exposed to AI-driven services, what is being done with their data – and what the consequences of them sharing (or not sharing) their data will be.

4) Regulators will have concerns about how AI is being used, and financial institutions must consider regulatory implications when they implement these technologies. Black box solutions and a lack of audit trails won’t please regulators and this will inevitably put limits on how far financial institutions can go – but to ensure that the value of AI can be exploited to the benefit of customers and the industry as a whole, firms have a responsibility to talk to the regulators and help them understand the benefits AI can generate.

5) It’s all about the data. Financial institutions need to get their data houses in order – and the supplier community needs to understand the data challenge, and help firms to tackle it. Any number of Amazon Echo-based solutions won’t help financial institutions mired in legacy data challenges, and while firms recognise that data governance and management is their responsibility, they are looking to the vendor community for solutions to support them in creating the data architectures they need to power their AI ambitions for the future.



31

11ABOUT

Finextra This report is published by Finextra Research.

Finextra Research is the world’s leading specialist financial technology (fintech) news and information source. Finextra offers over 100,000 fintech news, features and TV content items to visitors to www.finextra.com.

Founded in 1999, Finextra Research covers all aspects of financial technology innovation and operation involving banks, institutions and vendor organisations within the wholesale and retail banking, payments and cards sectors worldwide.

Finextra’s unique global community consists of over 30,000 fintech professionals working inside banks and financial institutions, specialist fintech application and service providers, consulting organisations and mainstream technology providers. The Finextra community actively participate in posting their opinions and comments on the evolution of fintech. In addition, they contribute information and data to Finextra surveys and reports.

For more information:Visit www.finextra.com, follow @finextra, contact [email protected] or call +44 (0)20 3100 3670

IntelIntel powers the incredible technology at the heart of transforming any business or industry. See how Intel is changing the world through powering supercomputers and the cloud, and discover the tools and resources that will help you evolve your financial services business. Additional information available here.

http://www.finextra.com

https://twitter.com/Finextra?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor

mailto:contact%40finextra.com?subject=

https://www-ssl.intel.com/content/www/uk/en/financial-services-it/financial-services-overview.html

Finextra Research Ltd 1 Gresham StreetLondonEC2V 7BXUnited Kingdom

Telephone+44 (0)20 3100 3670

[email protected]

Webwww.finextra.com

All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system, without prior permission in writing from the publisher.

© Finextra Research Ltd 2017

mailto:[email protected]

www.finextra.com

Documents

THE NEXT BIG WAVE: HOW FINANCIAL INSTITUTIONS … · a research paper from finextra in association with intel may 2017 the next big wave: how financial institutions can stay ahead