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A REPORT PRODUCED BY FINEXTRA IN ASSOCIATION WITH HEWLETT PACKARD OCTOBER 2019 HOW EUROPEAN RETAIL BANKS ARE RIDING THE WAVE OF NEW TECHNOLOGY
A REPORT PRODUCED BY FINEXTRAIN ASSOCIATION WITH HEWLETT PACKARD OCTOBER 2019
HOW EUROPEAN RETAIL BANKS ARE RIDING THE WAVE OF NEW TECHNOLOGY
CONTENTS
01 Introduction .............................................................. 3
02 The customer appetite ................................................ 5 03 AI and the flexibility of digital CX ................................. 7
04 Data analytics enhancing back office efficiency .......... 10
05 The big data machine securing endpoints ................... 13
06 Conclusion .............................................................. 15
09 About ..................................................................... 167 What should financial institutions be doing about
blockchain right now? 25
01INTRODUCTION
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3Retail banking is in the midst of change and technology is driving transformation, connecting physical and digital environments.
European retail banks are using emerging technologies such as artificial intelligence, process automation and big data analytics to remodel legacy infrastructure. Furthermore, despite the hype there has been about this technology, blockchain is not being used in retail banking. If anything it is used in moving vast amounts of money via correspondent banks with Ripple or Swift gpi.
Incumbent banks, challengers and neobanks are also focused on developing online and mobile banking services to meet the needs of the consumer, like the chatbot for retail customers, a conversational banking platform that most financial institutions (FIs) have launched.
With this, a bank is able to personalise customer experience (CX) using AI to enable customers to write or speak through a chatbot. Business Insider research predicts that by 2020, 80% of enterprises will use chatbots and according to Juniper Research, by 2022, banks will be able to automate up to 90% of their customer interaction using chatbots. European retail banks are therefore ahead of the curve. FIs are also attempting to disrupt the back office with big data. However, whether these technologies are being adopted with efficient intentions, or just to keep pace with the hype cycle, as has been shown by the use of blockchain, remains to be seen.
Both startup challenger banks and traditional lenders are digitising core banking systems, helping FIs use data analytics to influence buying decisions. However, to start benefiting from new technology, new types of employees need to be onboarded and existing staff have to be retrained. Existing financial players need to catch up in this area, because the fintech retail space is leading in the technology adoption race. Alongside this, digital transformation has already driven a 70% reduction in paper use over the last two years, according to HSBC.
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Another problem is that new technology can add several entry points that enable bad actors to access data at various points along the retail banking route. This is a growing issue in the age of open banking, GDPR and PSD2. These Europe-wide regulations have resulted in the UK leading in the open banking arena with Sweden and the Netherlands following close behind, according to Capgemini and BNP Paribas’s World Payments Report 2018.
Open banking is one of a number of factors disrupting the European retail banking landscape and moving it closer to a tipping point where banks can either implement a platform ecosystem to challenge the big tech giants, or lose in the battle. What retail banks are doing is challenging the status quo by adopting emerging technologies to combat issues that traditional financial institutions have faced for decades. This is just a drop in the ocean of the digital transformation that is to come
“ If you are the only entity with access to the blockchain, why can’t a database be used? Blockchain is a bit of a red herring for back office functionality in my opinion.” JASON MAUDE, STARLING BANK
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THE CUSTOMER APPETITE 02
Retail banks are using emerging technology to transform products and services to provide flexible, personalised services that deliver value to customers. After speaking to a number of retail banks, it has become apparent to Finextra that every bank’s journey to adopt technologies such as AI starts with experimentation. Successful cases are then used to establish the trajectory of business value and revenue generation.
The availability of data and sophisticated machine learning (ML) algorithms have encouraged more financial services players to invest in emerging technologies as a viable means of digital transformation. At this stage, AI and ML are only being used to reduce costs and speed up processes and chatbots, virtual assistants and digital conferencing technologies are an example of how this is being done.
Retail banks have a long way to go before this technology can really improve customer experience. Today, banks are operating under a fear that they will miss out now that AI is coming of age and have attempted to embark on their next phase of online and mobile banking offerings.
A catalyst for this trend is hyper personalisation, mastered by big tech companies such as Amazon, Facebook, and Apple. They offer increased mobility and flexibility to customers, improving the overall customer experience in areas such as account opening and personalisation. One example is the Apple credit card that promises customers cashback, loyalty rewards and a healthier financial life.
Nicolas Kopp, CEO of N26 Inc., the US subsidiary of German mobile bank N26 GmbH is spearheading the launch of N26 in the US on the back of its European retail banking success.
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Kopp says retail banks should remove the “friction out of your financial life”, citing features that alleviate customer service pain points such as early payday and N26 Perks, the new subscription loyalty programme.
“Our account should fit perfectly into our users’ daily lifestyle, where everything is mobile, and everything is real time. We want to give our customers more flexibility and control over their money. Today, people are acutely aware of what they are getting as they spend, and cashback and rewards are an especially pronounced need for those in Europe and the US.”
Choice is necessary. Customers should be able to contact their bank by various means. With a chatbot, account opening is straightforward because the app has the ability to authorise new customers in real time and also provides the option of call centre. Kopp highlights that this is more efficient for European retail customers because queries can be answered by a chatbot much faster than a human could.
Societal demands for a more personal service are powering the shift to conversational banking. Natural language processing will enable banks to understand a customer’s intention and deliver useful experiences. Access to data and APIs allows financial institutions to use real time processes to adapt the conversations so they feel more human.
Also, conversational banking that automates routine support or complex financial advice could potentially replace much of the work of call centres. This would provide much faster and accurate support for customers, while cutting costs.
Finally, the proliferation of voice-enabled Internet of Things devices such as Amazon Echo or Google Assistant into customer lives has also made a significant impact.
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AI AND THE FLEXIBILITY OF DIGITAL CX
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Organisations often use automation technologies such as chatbots as their first line of defence against their customers, rather than creating valuable new experiences for them. It is still early days for AI and ML and a lot of the work is in getting the data in the right shape and place first. Business models are being turned on their head because the ROI for AI isn’t immediately clear, and organisations are consumed with transforming to be more collaborative and selecting the right partnerships, refining the whole procurement process.
Metro Bank was the first UK high street bank to offer selfie technology to open retail current accounts online. David Thomasson, director, digital & payments at Metro Bank, says that Metro Bank is “not using chatbots, virtual assistants or conferencing capabilities in any major way, shape or form”, as the bank does not believe these would yet enhance the customer experience beyond its existing digital offering – including customer onboarding at the branch and online.
Good technology enhances the retail banking onboarding journey and this proves that the chatbot is not the driver to providing good customer experience, rather it is the maturity of the technology that allows these systems to support an end-to-end digital unit. Again, there is also a degree of choice that needs to be offered to customers: the European retail banks that we have spoken to offer a chatbot and/or a physical branch location or a call centre.
Juniper Research shows the operational cost savings from using chatbots in banking will reach $7.3bn globally by 2023, up from an estimated $209m in 2019. Traditional players are making the most of this opportunity. However, challenger banks cannot afford to take this chance and be left vulnerable to disruptors. Instead, they should make the most of the opportunity to take more risks and lead with innovation.
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Jason Maude, chief technology advocate at Starling Bank, says: “I talk to a lot of other banks and the reason they want to implement a chatbot is so that they can make redundant a number of staff in their contact centre, because queries are being automatically answered by the chatbot. They are not thinking about it from the customer’s point of view.”
For any AI model or ML model to be efficiently operational as a chatbot or virtual assistant, banks need to undergo a transformation and allow the system to start making decisions. Machine learning models have the capability of making decisions, they should be allowed to do so once adequate testing and controls have been put in place.
Many banks may force the machine to undergo the change control board procedure and all the benefits that could have been derived from the ML model are lost. It has become apparent that machine learning models should only be adopted if they are permitted to independently respond to changes in consumer demand.
To retain the quality of emerging technology, legacy systems need to undergo huge layers of change management. Challengers such as Starling Bank are not burdened by legacy infrastructure, so FIs such as these can leverage the power of machine learning effectively.
“Much of what defines the legacy portion of the banks has nothing to do with their actual technology. When discussing legacy technology, we are considering systems produced by engineers that have long left the organisation. It becomes legacy at the point where no one really understands how to change the system or how to alter things,” Maude says.
This reveals that if banks act more like technology companies, they are able to position themselves in a place where emerging technologies such as the cloud, machine learning and big data can be adopted and leveraged much easier. While European neobanks are currently doing this, incumbent banks that also adopt this agile method of working, will experience the same advantages. Challengers should lead the way, using chatbots as a customer experience differentiator, rather than just cutting costs. Many industry commentators say customers feel more loyal towards brands that know who they are and treat them uniquely.
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To serve the customer, it is essential that marketers know about big data, artificial intelligence and machine learning. But to build trust and customer confidence long term, organisations must also treat customer data sensitively and securely in full compliance with data protection and privacy regulations.
Years ago, no one thought online and mobile banking would take off and the same is true for data sharing. As it becomes clear that the benefits outweigh the risks, comfort levels will rise. Customers are becoming increasingly comfortable sharing data with the brands they trust after experiencing features such as access to more personalised and tailored offers.
Josh Bottomley, global head of digital, data & development at HSBC, details how, with customers’ permission, AI and ML can help create personalised “in-app trigger-based messaging” that sends “relevant proactive messages to them based on what their data is telling us. We will also use data insight to fully personalise online journeys.”
While HSBC is already doing it, if all retail banks adopt conversational banking services, it would establish a global dialogue between the banks and their customers, on the device or app the consumer chooses.
“ Today, people are acutely aware of what they are getting as they spend, and cashback and rewards are an especially pronounced need for those in Europe and the US.” NICOLAS KOPP, N26
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DATA ANALYTICS ENHANCING BACK OFFICE EFFICIENCY
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Banks operate increasingly in a time sensitive industry. Instant notification and interaction is essential to meet demands of regulation, risk, compliance and customer expectation. At the heart of this is managing data in a real-time economy. The scale and complexity of data, drawn from multiple sources is greater than the human workforce can handle efficiently.
While emerging technologies should be used to serve the retail consumer and improve the customer experience, big data and data analytics capabilities will disrupt back office efficiency and influence buying decisions in both startup challenger banks and big corporate global banking brands. Financial institutions can then make great strides using AI, ML and IoT to digitise traditional systems.
It is becoming clear that big data is deemed the first stop when implementing machine learning models and for this technology to be given the agency to independently make decisions in the back office.
The Internet of Things produces a small degree of efficiency and automation while it essentially collects an increased number of data points from individual items. The use of the many forms of artificial intelligence, primarily but not exclusively machine learning, is essential, and its augmented deployment within financial institutions is well publicised. AI helps to reshape business models in line with digitally-led transformation strategies. The two can happily co-exist and the result is manifold-improved productivity, enhanced processes.
This was a key theme at Finextra’s annual AI London conference (https://www.nextgenbanking.co.uk/), put forward by AI experts from banks including Lloyds, Barclays, RBS, Santander, Starling and more: AI augments (rather than replaces) the human role; automation presents the data to enhance the human audit and decision-making process.
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The same applies to blockchain. Starling Bank’s Maude says banks must ask themselves why this technology needs to be used and whether the same result could have been accomplished on a centralised database.
He says: “The only reason to use blockchain is where there isn’t a trusted party in the system, so you have two or three people that interact with each other, but no person is trusted to maintain the dataset. Here, it can be quite useful. But if you are the only entity with access to the blockchain, why can’t a database be used? Blockchain is a bit of a red herring for back office functionality in my opinion.”
However, in this age of open banking, GDPR and PSD2, data promises great leaps forward in terms of back office efficiency. For new entrants, technology offers a clear path to innovation as these banks are not burdened by having to patch multiple systems together. From the outset, the IT and data systems are structured.
In theory, the ability to collect, process, and analyse large amounts of discrete yet interrelated data points can give businesses the insights they need to increase efficiency, ensure compliance, and reduce fraud. In reality, C-level executives in traditional banks struggle to drive business models into action.
They find it difficult to procure different data collection and analysis technologies from different vendors and communicate on a technical level while engaging data scientists in constructive conversations with managers. Progressing from raw data to actionable insights is no small feat: a strategy is required to execute.
Several businesses have already experienced the growing pains involved with bringing viable big data initiatives into the mainstream. They have taken different approaches, but those that work the best have a common link: a clear line of communication between business-facing executives who are ultimately responsible for bottom line growth and data-facing scientists who design analytics that help further that growth.
All employees working at a financial institution also need a basic understanding of technology, but this is lacking. Even if banks buy rather than build technology, a certain level of knowledge and understanding is still very much required in order to make an informed decision and separate the wheat from the chaff.
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Discussions about hiring data scientists are rife, but what the retail banking industry really needs is more data engineers. While the analysis of data is crucial, an engineer has the talent to clean the data, insert the correct queries and obtain actionable insights from the information. This is a problem that both challenger banks and traditional players are facing in Europe.
Focusing on big data development initiatives presents a clear cut business mandate. However, when banks dive deeper into new tech-assisted business processes and artificially intelligent analytics, they find the process of managing those efforts to create practical, real-world solutions is just as important as the innovation that goes into them.
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BIG DATA MACHINE-SECURING ENDPOINTS
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AI and ML are very much to the fore in fraud detection, anti-money laundering and spotting anomalies.
While new technology results in several entry points that bad actors can use to access data throughout retail banking, achieving meaningful customer insights isn’t just dependent on data and AI processing capabilities. It is also influenced by what regulators will allow the bank to do and what data customers will be comfortable allowing their bank to access, including data privacy, data consent and data rights management.
Mandated open banking initiatives have led to a renewed interest in application program interface (API) capabilities. Retail banks that share customer behaviour insights to develop effective tools for third-party players are part of a new landscape in which customers reap the biggest benefits.
APIs also allow activity to flow through an organisation in real time. When merging regulations like PSD2, financial services providers can offer a more diverse range of services. While traditional banks are in various stages of their digital transformation, fintech startups are already leveraging their API-first strategies to unlock innovative business models, reducing their barrier to entry.
However, a reduced barrier to entry increases the number of security risks involved with sharing data, creating new opportunities for customer information to fall into the wrong hands. Therefore, financial institutions must update their risk management capabilities to ensure customer data is being handled securely.
The threat landscape is complex today, but API abuses are expected to be the most frequent attack vector for web applications in the near future. Online banking applications are a lucrative target for cybercriminals, so mismanaged interfaces could cause havoc across the globe.
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The problem with APIs is that they offer a blueprint that describes the implementation of an application – information that would otherwise be hidden under web functionality. Hackers can use the self-documenting characteristic of an API to their advantage and establish a path to gain access to a computer or network server. As well as increased visibility, APIs also introduce risk by increasing the number of potential calls and, in turn, the attack surface that hackers can exploit. Risk increases with opportunity.
However, the benefits of APIs overshadow the dangers. To harmonise and secure APIs internally while also ensuring the integrity of the whole chain, developers must recognise the importance of sharing APIs despite varying end goals. This helps to build safer APIs. With the changes in regulation that have been welcomed with the open banking rollout, API security has become a critical consideration for banks.
Data sharing in the context of open banking, combined with AI, can improve the efficiency of operations and decision-making through automation. With GDPR and PSD2, which regulate the availability and accessibility of data in the era of open banking, AI is expected to make an impact on processes, products and services, and markets.
AI is mainly used for competitive reasons to create leaner and faster operations and tailored products and services. However, it could be used collaboratively to create a more secure and efficient financial system, for example by identifying patterns of threats that cross institutional boundaries.
In 2018, HSBC became the first bank to deploy AI and ML to combat financial crime. The AI solution doubled the speed and accuracy of screening for sanctions and money laundering in cross-border payments. The lender has also recently launched an ML solution called Cog-I for transaction monitoring.
If financial institutions want to realise the potential of collaborative AI, they need to make the most of the data that’s available and accessible through open banking. Therefore, efforts to standardise APIs across the EU should focus on increasing the functional scope and the quality of the data, so AI can be used to transform data into valuable insights.
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CONCLUSION06
European retail banks are providing the blueprint of how to serve customers. Taking a page out of big tech giants’ books, incumbents and new entrants alike are offering hyperpersonalised conversational banking products and services, reducing friction and allowing consumers to bank how they want to bank.
The next step that retail banks need to take in their digital transformation journey is to allow these emerging technologies to make decisions and respond to changes in consumer demand independently, which in turn, allows the FI to fully reap the benefits of the tech.
For this to be possible, the technology must be used in the correct manner and banks must not get caught up in adopting tech for no defined purpose or end goal. In addition to this, the right employees need to be given the right roles and existing employees retrained for this age in which open banking is heralded across Europe.
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07ABOUT
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
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