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Banking

DIGITAL TRANSFORMATION: WHAT CAN BANKS LEARN FROM OTHER SECTORS?

By Paul Jones, Head of Technology at SAS UK & Ireland

 

The banking sector sets a benchmark for other industries in many areas. When businesses need to process large volumes of transactions reliably, maintain 24/7 availability, meet complex regulatory requirements, analyse risks or make financial plans, they often aim to follow the same practices and adopt the same technologies that banks have established as a gold standard.

However, there are other areas where banks aren’t necessarily ahead of the field – and where they can learn from the successes and failures of other sectors. Digital transformation is a prime example. According to McKinsey: “Years of research on transformations has shown that the success rate for these efforts is consistently low: less than 30 percent succeed.” And as almost all established banks see digital transformation as a top priority to help them counter disruption from challenger banks and fintech startups, this is a major concern.

 

Technology as the catalyst for transformation

In their struggles to get digital transformation initiatives over the line, banks typically take one of two approaches. The first is to create a separate internal organisation with a remit to develop new digital products and services, unencumbered by the bank’s existing legacy processes and technology. In some cases, banks have even acquired one of their former fintech rivals to take advantage of its digital skills and provide this internal innovation capability.

The second approach is to focus on incremental digitisation by enhancing existing processes with digital technologies. For example, a bank might seek to enhance contact centre operations by embedding intelligent decisioning capabilities that use artificial intelligence and machine learning to help operatives make more personalised offers to customers. This strategy has the advantage of building on the strengths of existing ways of working, instead of starting from scratch. But it may also be more difficult to implement and require significant investment from senior leaders to drive the required cultural change.

It’s difficult to say which of these approaches is best. And in practice, banks will probably require both, depending on the type of transformation they are trying to achieve. But one interesting insight, again from McKinsey, is that whichever approach they follow, organisations whose transformation initiatives are successful tend to deploy or try more technologies than those who fail, particularly in areas such as cloud, mobile, IoT and artificial intelligence. And this links strongly to the fail-fast mantra in introducing digitalisation.

 

The importance of first principles engineering

There’s a connection here. Banks are buying fintechs to take advantage of their digital expertise, and fintechs have earned that expertise through their willingness to adopt and experiment with new technologies. But that experimental approach isn’t something that the fintechs invented on their own. It’s a lesson they learned from the big technology companies.

For example, while Facebook’s famous mantra of “move fast and break things” sounds like a frightening idea in the highly regulated world of the financial sector, it’s basically the same idea that Tesla calls “first principles engineering.” You take a new idea, try to implement it using whatever technologies seem most promising and expect your first attempts to fail. But because you expected some form of failure, you learn from the experience and do better on the next iteration.

Perhaps some of the new technologies you try end up in the final product, and perhaps they don’t. The point is, you make the cost of the experiment and the price of failure as low as possible so that you have space to explore the problem and come up with the right design for your business.

 

Learning from the tech giants

Take Monzo, for example, which is one of the UK’s biggest success stories in the new wave of challenger banks. In its mission to build a banking system from the ground up, Monzo’s engineering team decided to build a loosely coupled microservices architecture, specifically because “large internet companies like Amazon, Netflix and Twitter have shown that single monolithic codebases do not scale to large numbers of users.”

In its willingness to learn from the tech giants, Monzo experimented with different technologies before settling on Kubernetes – the same technology that Google uses to manage containerised workloads at a massive scale. (Incidentally, at SAS, we’ve been through a similar journey in developing our own cloud analytics platform and came to a similar conclusion. We’re now running our new services on Kubernetes too.)

The same principle applies to the adoption of analytics tools for artificial intelligence and machine learning. Even more so than classical statistical modelling, AI inherently requires an experimental, iterative approach where you learn as much from your failures as you do from your successes. In many cases, the wisest path is to try a wide range of different approaches and technologies, including all the latest open source frameworks, to discover what works best. Once you have found the right approach, you can then industrialise it using a production-grade analytics platform such as SAS Viya, and even provide it to your clients as a service.

 

The human element

We’ve established that banks can profit by following the example of the big tech companies when it comes to designing the technical architecture and processes around digital transformation. But technology isn’t everything. Successful digital transformation also has a strong human element.

To see why this is important, let’s look at a counterexample. Another fintech company that has enjoyed rapid growth is Robinhood Markets, whose mobile app has made it easy for a new generation of investors to start trading stocks, ETFs, options and cryptocurrencies. However, in early March 2020, the Robinhood app suffered a series of systemwide outages that prevented users from opening or closing their positions.

The cause of the problems was a technology failure. In a subsequent blog post, the company’s founders noted that their infrastructure couldn’t handle the combination of “highly volatile and historic market conditions; record volume; and record account sign-ups.” But the impact was human. When the app failed, there was no contact centre to act as a backup for booking trades.

 

The risks of failed tech

The result? Many of Robinhood’s small investors were helpless as the markets turned against their positions, or unable to make trades to take advantage of opportunities they spotted during a week when the coronavirus pandemic sparked a mass selloff. While it’s not yet clear how Robinhood will weather the storm, it’s reasonable to expect that there will be compensation claims, potential lawsuits and, worst of all, a catastrophic loss of customer confidence in the business. As one customer quoted in The New York Times put it: “For me, the moment they get [back online]I am going to try to get out and switch out to someone else.”

Without a human element that can take over when technology fails, businesses expose themselves to significant risk. And even if the technology is completely bulletproof, it’s a bad idea for banks to use it to replace human contact entirely. When customers apply for a mortgage or a loan, they’re often going through a high-stress situation, such as moving house or expanding a small business. While the loan approval decision can and should be handled by sophisticated modelling techniques, the customer wants to hear more than just “computer says yes” (or “no”).

The best customer experience comes when the model is able to explain its decision to a customer service agent, who can then act as an intermediary to break the good or bad news to the customer. This is assistive AI in action.

 

Learning from the public sector

This is a lesson that the public sector has been faster to learn than the private sector. Health care organisations, for example, are investing significantly in the use of AI to assist with diagnoses – for example, using image recognition models to identify potential tumours in X-rays and other medical images. But the principle from the beginning has been that AI can only play an advisory role; the final decision is always made by the physician.

At SAS, we’ve seen the success of this approach in other areas of the public sector too. We’ve recently worked with a large government department to embed intelligent decisioning into its contact centre to give staff the insight they need to provide a better service to each caller. We’ve helped translate the same principles over to the private sector, as well. One of our clients is a car insurance company that uses AI to assess whether a damaged vehicle needs to be written off, and the model now explains its decisions to the customer service team so that it can advise policyholders.

 

Banking

WHY AGILE, SCALABLE DATA MANAGEMENT IS KEY TO DIGITAL BANKING

By Jason Hand, Global Account Executive – Enterprise Sales, Commvault

 

Back at the start of 2019, before we’d ever heard of COVID-19 (hard to imagine these days, I know), mobile banking was predicted to overtake high street branch visits within two years. But the restrictions placed on daily life to get to grips with the pandemic proved to be a catalyst in speeding up adoption.

Although banks haven’t had to close during the UK lockdowns, they discouraged unnecessary visits — and many people new to online banking discovered that it could provide a quick and easy (and COVID-safe) way to manage their finances. No surprise then, that as summer came to an end, over three-quarters of the UK population were using some form of online banking and one in ten people had switched to a digital-only bank.

When it’s implemented well, online, digital and app-based banking is as easy as shopping with Amazon, booking a cab on Uber or grabbing a takeaway via Deliveroo. With so much potential to create a similar customer experience — and so much to lose if they fail — banks are under pressure to deliver on digital services. But their success (or otherwise) will depend on how well they manage their digital data and, in particular, how willing they are to adopt more agile, scalable, cloud-based solutions to underpin their new services.

 

Adopting New Technology in a Risk-Averse Sector

The UK’s financial services sector is undoubtedly slow when it comes to adopting new technology. Indeed, many UK banks continue to rely on mainframes. This cautiousness stems from the continued rise in cybercrime and the fear of non-compliance with FCA and data protection regulations.

Banks have to tread a thin line. They do want to embrace technology that will help them scale and support customer demand for digital services. But they can only do so with an IT infrastructure that keeps out cybercriminals, hackers and anyone else without explicit authorisation to view the data. So, if their legacy IT systems are secure and protect customer data from cybercriminals, banks do not want to risk implementing new solutions that could leave them exposed — even if those old systems make them less nimble and less responsive to changing customer demands.

 

Open Banking and Shared Financial Data

The increased digitalisation across the sector leaves banks facing a second security and data management challenge. Once, they only had to worry about managing their data and keeping it safe within their closed IT environments. Now Open Banking — a UK government-backed programme — encourages banks to securely share their data with trusted third-party financial services providers via an API (Application Programming Interface).

Typically, these third-party providers offer apps to assist with utility bill management, accounting and auditing, and savings (usually rounding up apps). Once a user grants authorisation, the app directly interfaces with that user’s current account. Customers — whether individuals or SMBs — love them, but for banks, they’ve meant a reassessment of security and data management strategies.

 

What Constitutes Good Data Management?

To begin with, it could mean switching to a single data management solution. Banks historically have deployed several different products to manage their data. Multiple applications add complexity and  need more people to oversee them operationally. This approach will add cost, risk, and ultimately will not align to their digital transformation agendas.

Running multiple data management solutions makes it harder to get a holistic view, understand customer behaviour and predict future trends. It also creates unnecessary security risks. Consolidating data management platforms reduces these risks and costs. At the same time, fewer inter-app data transfer points decrease the number of potential weak-link entry points for hackers and cybercriminals. From a practical point of view, using a single data management solution also enables all relevant data points in a hybrid world to be viewed on a single pane of glass — making it much easier to digest, interpret and deliver data management as a service back to their internal clients.

Automating data management components can improve security and cut costs by reducing human contact. In addition, it enables faster and more accurate data management that can accelerate cloud adoption where data management is key to success.

It’s worth saying at this point that banks have been slow on the uptake of both public and private cloud technology, and are clearly still concerned about security and privacy threats. This is despite the fact that cloud computing — particularly with a zero-trust approach to security — has become a lot safer and carries far less risk.

In the middle of 2019, the Bank of England published a report that estimated the world’s largest global banks conducted just a quarter of their activities in the public cloud or software hosted in the cloud. But change is happening, albeit slowly. Larger banks have started to recognise that cloud computing holds the key to running an agile business  — allowing them to scale their online services and safely store, process and mine vast amounts of digital customer data.

The maturation of the hybrid cloud market may have played a role in increased adoption and allayed many of the sector’s previous doubts. A hybrid cloud infrastructure combines public cloud, private cloud and on-premises architecture, giving users the flexibility to keep some applications and systems (those with particularly sensitive information, for example) within their own four walls while still being able to migrate other systems. It’s an elegant and cost-efficient way to balance security, scalability and compliance.

 

Demand for the Future

With so much change taking place across the UK banking sector, data management has never been more critical. Open Banking, consumer demand for digital banking, and app-based banks like Starling and Monzo are all shaking up the market. But the threats from cybercriminals and the risk of falling foul of FCA regulations are still very much present. And, while navigating all these challenges, banks still face pressure from shareholders and investors to make a profit, retain customers and grow the business.

For these reasons, data management strategy — and linked to that, the pace and effectiveness of cloud computing adoption — are now two of the most significant determining factors in how banks cope today, and how effectively they will operate in the future. As such, 2021 should be the year that most banks and financial organisations embrace and invest in new technology when it comes to data management.

 

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Banking

SEIZING THE OPEN BANKING OPPORTUNITY

Nick Maynard is a Lead Analyst at Juniper Research

 

Open Banking has made significant progress in 2020, having recently launched across much of Europe and now starting to emerge in other markets too. And there are two primary reasons why Open Banking is disrupting the banking industry so much:

  • Banks have begun to discover the real competitive advantage of a more open approach to banking. Offering a superior Open Banking experience to customers can be a compelling differentiator from other competitors as part of a wider digital app experience. Open Banking also creates a level playing field in markets where regulatory intervention has led to Open Banking deployment. As all banks are required to deploy APIs in this scenario, the situation is the same and does not put any one particular bank at a disadvantage.
  • Legislation – for example, in October 2015, the European Parliament adopted PSD2 (the revised Payment Services Directive). By early 2020, major banks in the EU had adopted Open APIs. There have however been many cases of late deployments of APIs and problems with the availability of APIs.

 

Nick Maynard

The Disruption Factor

Open Banking is a major disruptive factor for banks. The reason for this being that it opens up account data to both AISPs (Account Information Service Providers) and PISPs (Payment Initiation Service Providers), which can attempt to carve out a role in the banking area.

  • AISPs: These new vendors are able to access transaction data and balance information, as well as related information. This has, in particular, led to the rise of vendors such as Emma, Yolt and Connected Money. These vendors combine information from multiple sources, adding value to the user.
  • PISPs: In this case, the vendors are able to leverage Open Banking API connections to initiate payments directly from the bank accounts in question. This means that these players are able to bypass traditional payment methods, such as cards. Vendors such as American Express and PayPal have already launched solutions that have taken full advantage of this action.

 

PSD2 Changes

Generally, the implementation of the new PSD2 European regulation for electronic payment services effectively reduces the entry barriers for new digital players. It also opens up banks to the potential for competition, enabled by their own APIs. This allows these players to compete with existing services in fields currently offered by the banks. In the case of AISPs, it is possible that third-party applications could displace the role of the apps from incumbent players, which would dilute the bank’s relationship with their users.

As with any fundamental change to markets in the banking area, there is the potential to bring a number of both opportunities and challenges to consider with Open Banking.

Open Banking Opportunities & Challenges to Consider

Source: Juniper Research

Banks and other parties that are looking to become involved in the Open Banking ecosystem must weigh these opportunities and challenges carefully. Open Banking certainly needs a more collaborative approach than traditional banking models, which will require significant effort to make them successful.

 

The Forecast for Open Banking

The total number of Open Banking users is set to double between 2019 and 2021, reaching 40 million in 2021 from 18 million in 2019. The ongoing Coronavirus pandemic is increasing the need for consumers to have the clarity of combining their accounts and gaining insight on their financial health, and also boosting momentum in the adoption of Open Banking.

This extraordinary growth is being driven by Europe, where the regulator-led approach to Open Banking has created a standardised market, with low barriers to entry. This contrasts with markets like the US, where a lack of central regulatory intervention is limiting growth potential.

 

Open Banking – Delivering Opportunities and Threats

It is worth noting that Open Banking can be both a threat and an opportunity for traditional banks. While Open Banking exposes user information and access to potential competitors, this threat has the potential to affect all players in the market equally. Consequently, established banks must create innovative Open Banking services that will provide benefits for the user, while also attracting customers from less innovative competitors.

Payments will be critical to the emerging Open Banking ecosystem; accounting for over $9 billion in transaction value in 2024. However, payments in this ecosystem are at a particularly early stage. While eCommerce is dominated by card networks, there is the potential that this role will be eroded over time by ‘direct from account’ payments. Consequently, card networks should look to offer Open Banking-enabled payment services, in order to offset the risk of future disruption.

Open Banking Users in 2021 (m), Split by 8 Key Regions: 40 Million

Source: Juniper Research

 

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