By Tiffany Carpenter, Head of Customer Intelligence Solutions at SAS UK & Ireland
How intelligent decisioning solutions can help you stay relevant in the era of digital banking
Fierce competition, advances in technology, and consumer expectations for hyperpersonalised services are forcing the financial services sector to evolve. To adapt to rapid market developments, many banks and insurers are launching ambitious digital transformation projects. But do they actually deliver results?
The short answer: not often. In a recent study, McKinsey & Company found that fewer than one-third of organisational transformations succeed at improving a company’s performance, and a staggering 70% of large-scale change programmes don’t reach their stated goals. That’s a lot of effort and upheaval for very little reward. So what can we learn from this unsettling trend?
Every business and digital transformation strategy is unique, but there are four avoidable mistakes that financial services companies repeatedly make when approaching digital transformation:
- Misunderstanding the challenge
Whilst almost every financial institution has some kind of digital transformation strategy in play, many are focused on the technical aspects of digitisation and adapting to new channels and tools.
For example, many business leaders believe that digital transformation is mainly about replacing manual processes with automated workflows. That’s why there has been a rush to invest in robotic process automation (RPA) across the big banks and insurance players.
However, while automation can play an important role at the implementation stage, digital transformation is much more about reimagining traditional business models to succeed in new, fast-changing digital economies. In a banking context, that means redefining products and services to reflect the realities of a market where the customer is king.
- Pursuing disjointed initiatives
When rolling out digital transformation projects, banks often focus on innovation in individual functions or departments without considering how changes on one side of their business will affect other areas.
That’s a problem because banks have traditionally been structured along vertical product lines such as current accounts, savings, mortgages and credit cards, and horizontal business functions such as marketing, technology and finance. As a result, change programmes inevitably get stuck in the interdepartmental crossfire.
Instead, digital transformation initiatives must seek to disrupt the complex legacy operating model of the traditional bank and replace it with a more holistic, customer-focused approach.
- Making data unreachable
The letter “d” in digital transformation should stand for data. Without the ability to collect, store and access data, and the tools to refine it into actionable insights, banks won’t be able to leap ahead of their competitors.
For example, in an effort to serve business units with fast access to key information, many banks have established centralised data lakes. While this approach succeeds in eliminating individual data silos, it often ends up replacing them with a single large silo that is equally inaccessible.
Placing all data under the stringent governance of the IT department can make it very difficult for other business units to access and analyse time-sensitive data quickly. This can limit the value of insights and diminish the return on investment for large-scale change initiatives.
- Enabling cultures of resistance
The most challenging aspect of transforming any business is inspiring its employees to become advocates for change. Inertia, doubt and cynicism from people within the bank can stop transformation initiatives dead in their tracks.
That’s why setting a clear vision for change and encouraging employees to experiment with new ways of working is essential for banks to achieve a smooth adoption of new technologies and processes.
Shopping for success
In designing successful transformation initiatives, banks and other financial institutions can learn a lot from companies in other sectors that have harnessed analytics to avoid falling foul of these common pitfalls.
Take Shop Direct, which is not only the parent company of retail brands such as Very and Littlewoods, but also one of the UK’s largest nonbank lenders. While the company had thrived for many years on its traditional catalogue-based sales model, it realised that the future was not in paper. To pivot the business and remain relevant, it had to establish an online presence – and fast.
Shop Direct knew that moving its retail and financial services businesses over to an online-focused operating model would not be easy, but it had a secret weapon: vast amounts of data on customer buying habits, sales information and inventory records.
Within 12 months, Shop Direct built a solution based on intelligent decisioning software from SAS that was capable of mining useful insights from more than two years of data of customer interactions. By combining historical data with real-time context such as browsing behaviour, the company can now make instant decisions to tailor the user experience for each customer: personalised sort orders, personalised recommendations and real-time credit risk decisions.
Intelligent decisioning in banking
Similarities between the challenge faced by Shop Direct and the current ambitions of traditional banks are striking. Banks face an urgent need to reinvent their traditional business models for the digital world. Moreover, banks also possess huge volumes of customer data that they can analyse to find valuable insights about how to enhance existing services and develop new products.
AI and machine learning technologies have the potential to help traditional banks transform – but analytics on its own is not enough. Banks need to harness the insights generated by analytics to automate decisions at scale.
This means basing analysis not just on departmental data sets, but on all the information the bank possesses. It needs to include both historical transactional records and live data streams that provide immediate context on customers’ behaviour and actions. Furthermore, the analytics needs to take place in real time and drive automated actions to respond to immediate customer needs in order to truly affect the customer journey.
OPEN BANKING: ARE CONSUMERS KEEPING AN OPEN MIND?
Last September, the European Union’s regulatory requirement for banks to open up their payment accounts via application programming interfaces (APIs) came into effect. Since then, open banking has taken centre stage within European retail banking and payments. In this blog, Elina Mattila, Executive Director at Mobey Forum, shares insight into how emerging consumer attitudes may impact open banking services in the coming months.
It has been over six months since the revised Payment Services Directive (PSD2) came into full effect and with it, required banks to allow third party providers to access payment initiation and account information. While the regulation was designed to facilitate open banking, the market demand was uncertain. Would we, as consumers, choose to embrace the new services enabled by open banking? And if so, under which conditions?
To understand consumer attitudes, Mobey Forum and Aite Group partnered on a pan-European study to determine the appetite for open banking services amongst 1000 consumers in Finland, France, Germany, Spain, and the United Kingdom. The study, launched in November 2019, revealed many important consumer trends and attitudes, including key priorities and potential barriers for adoption.
Consumer appetite for change
The consumer benefits of open banking are largely perceived to be compelling, yet this counts for little if the providers of those services are not deemed trustworthy. This is an observation reflected in the study, which highlighted consumer confidence in service providers as critical to open banking adoption. People want clear visibility of who is managing their finances, and the overwhelming majority (88%) would prefer their primary source of open banking services to be their main bank, as opposed to other banks or third-party providers (TPPs).
Consumers also indicated high levels of trust in their current bank of choice, reflected by 77% preferring to use a financial product comparison service offered by their main bank. By enabling customers to compare the pricing and conditions of a range of financial products on the market, they feel more comfortable that banks have their best interests at heart. This is a welcome trend, and one which should be celebrated in the aftermath of the 2008 financial crisis. For the banking industry to have rebuilt trust levels in this way bodes well for consumer adoption of future innovations.
With a trusted provider, one third of consumers were then either ‘very interested’ or ‘extremely interested’ in integrating open banking services into their financial routine. This applied to specific use cases: account information services (32%), pay by bank (33%), purchase financing (25%), product comparison (35%) and identity check services (35%). Unsurprisingly, consumer willingness to adopt these services relies heavily on providers continuing to prove that they can be trustworthy stewards of personal data.
For those unwilling to adopt open banking, concerns largely focused on reservations around security and privacy. As open banking becomes more sophisticated, it will be interesting to analyse the nuances around how consumers engage with third parties. Established brands are perhaps more likely to be trusted by consumers than lesser-known online retailers. For this reason, consumers may hesitate to engage newer companies than brands they are already familiar with. In an industry as varied as finance, this creates additional intrigue in the ongoing battle for market share between the newer ‘challenger’ banks and the older, more established European banks.
Consumers might, however, be willing to deprioritise trust and, instead, favour convenience and usability. When questioned over their willingness to adopt a new payment method, for example, 91% of respondents indicated that they could be tempted to switch either by financial incentives or the promise of greater convenience.
The path forward
While open banking is still in the relatively early stages of development, it has made significant progress in a very short period of time. Not only is it allowing consumers to share financial data with authorised providers as they wish, but it is set to spark more competition and innovation within the market.
From a business perspective, open banking is expected to create lucrative new revenue streams, particularly for companies which are able to innovate quickly and react to consumer demand. It is prompting consumers to reconsider how they manage their finances and – most excitingly – it’s not even close to reaching its full potential. It should bring a whole new era of service partnerships between banks and TPPs, which will enable a new generation of innovative financial services.
For the industry to truly fulfil its potential, it is vital that stakeholders are able to explore new business models, innovations and changing customer expectations for open banking in a commercially neutral environment. Mobey Forum’s open banking expert group provides exactly this, and we look forward to supporting our members as they shape the future of digital financial services.
Where to find out more
The opportunity for open banking is explored in more detail in a report by Mobey Forum and Aite Group, entitled Open Banking: Open Minds? Consumer Appetites for New Banking Services. It provides banks and other financial services stakeholders with a market view on consumer appetites toward new open banking services and explores the possible roadblocks to consumer adoption. It is also discussed in a podcast featuring key representatives from Interac, Erste Group Bank and Strands Finance.
HOW CAN PLATFORM AS A SERVICE UNLEASH COMPETITIVE ADVANTAGE FOR BANKS?
By Paul Jones, Head of Technology, SAS UK & Ireland
Due to both regulation and practical realities, banks spend much of their time, effort and money on activities that make zero difference to their competitive position. Processing transactions, booking trades and managing compliance for anti-money laundering (AML) and know your customer (KYC) efforts are vital tasks for any bank, but they make almost no contribution to differentiating a bank from its competitors.
According to McKinsey’s 2019 Global Banking Review, outsourcing these activities presents a huge opportunity for optimisation: “By transferring non-differentiating activities to modular industry utilities, banks could potentially improve return on equity by 60 to 100 basis points.”
Besides the immediate financial benefits, if banks can optimise their resources to spend more time focusing on developing new digital services and delivering an outstanding customer experience, it’s a clear win-win in terms of both saving costs and growing the business.
Dissecting your differentiators
But how far can we stretch the idea of “non-differentiating activities”? Is risk management a differentiator for banks? How about fraud detection? Or even marketing? I think the answer is it depends. Within each of those three functions, there are areas where top banks can develop competencies that give them a real edge over the competition. If you have the best risk models, you’re likely to make more advantageous trades than your counterparties. If you’re the smartest at catching fraudsters, they’ll focus on weaker prey. And if you understand your customers better than your competitors do, you’re more likely to keep them.
In fact, McKinsey estimates that the opportunities to enhance capabilities such as risk, fraud detection and marketing through artificial intelligence and machine learning could deliver up to $250 billion in value across the banking sector.
In each case, the data scientists who devise your predictive models for calculating exposure, detecting anomalies and segmenting customers are the key to your success. Their skills put them at the pinnacle of all your employees in terms of creating real business value. But data science isn’t a standalone activity, and there are other elements of risk, fraud and marketing operations that don’t add much competitive value – what we might call the “platform” elements.
Data science as team sport
On the scale at which most banks operate, data science isn’t just about the individual brilliance of your PhDs. It becomes much more of a team sport – and like any professional sport, it quickly develops its own back-office requirements. You need software, databases, development tools, infrastructure, processes, data governance frameworks, monitoring and analytics, auditing and compliance capabilities, and business continuity/disaster recovery strategies. That’s what I mean by “platform” – all the basic components you need to run a successful enterprise-scale data science programme and get innovation into production.
The good news is that you can absolutely outsource your marketing, fraud and risk analytics platforms, just like any other non-differentiating activity. Running analytics and data science platforms at scale is known to be a tricky problem, even for tech giants like Google, but with the right combination of technology, processes and expertise, it’s perfectly possible to let an expert partner take care of the day-to-day operations.
What to look for in an outsourced platform
When you are assessing analytics Platform as a Service (PaaS) offerings, there are a few key things to look for. First, your partner should provide a fully managed cloud infrastructure that enables quick onboarding and makes it easy to ramp up new projects and close down old ones.
McKinsey estimates that the opportunities to enhance capabilities such as risk, fraud detection and marketing through artificial intelligence could deliver up to $250 billion in value across the banking sector.
Second, your partner should have the right expertise to take responsibility for handling all day-to-day system administration and model management duties, as well as batch analytics tasks such as regulatory calculations. Offloading this routine work will reduce costs for the bank and also slim down the risk profile because your partner will keep the platform fully up to date with the latest security updates and patches.
A good PaaS offering will also include process automation to increase throughput for the data science pipeline. This is a well-known issue in the industry. For example, Gartner estimates that over 50% of models don’t make it to production, and a recent survey by SAS showed that it takes organisations on average three months to deploy a new model.
Speed production with DevOps
You should look for a PaaS with built-in DevOps procedures that help to accelerate deployment to a fraction of that time while maintaining rigorous quality controls. The ability to put models into production more quickly will make you much more agile – so you can respond more quickly to emerging market risks, counter new types of fraud, and adopt the latest artificial intelligence and machine learning (AI/ML) techniques to support your marketing campaigns.
Critically, any PaaS contract should guarantee that your data and models remain your intellectual property and that you have complete control of where your data is stored and how it is used. With the right separation of duties between you and your PaaS provider, your data science team can focus on the valuable, exciting aspects of model design and training, while your partner handles all the mundane operational work around deployment, data processing and governance.
We’re working with banks across Europe to provide exactly this type of PaaS for marketing, fraud and risk analytics. If you’re interested in how to help banks drive digital transformation with cloud-based analytics, please read my previous blog post here.
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