By Paul Jones, Head of Technology, SAS UK & Ireland
Fintechs are turning up the heat in retail and corporate banking. As smaller, more agile providers have entered the banking market, customers are getting used to a higher level of service – a personalised, digital experience that guides them to make quicker, smarter decisions about their finances. For traditional banks to compete, they need to transform the way they operate. On the retail banking side, that means digitising customer-facing services. No queuing in branches, no paperwork. And when customers apply for a credit card or loan, they get a decision in seconds.
Meanwhile, on the corporate side, the aim of transformation is often to enable an everything as a service (XaaS) strategy, building smart packaged offerings such as treasury as a service or risk management as a service, which the bank can both consume in-house and provide to enterprise clients.
Data-driven digital transformation
To foster this type of digital business transformation, banks need to redesign both internal and customer-facing processes to embed data-driven decision making. By integrating intelligent automation and decisioning capabilities into their operations, banks can eliminate paperwork and manual processing. This will greatly improve service levels to customers while keeping the cost-to-serve to a minimum.
The creation of these data-driven services depends on the ability to design, build, test and deploy processes that embed predictive models using both well-established statistical methods and new artificial intelligence and machine learning (AI/ML) techniques. The development life cycle for these models is inherently experimental. It’s vital to try different approaches, test the results, and iterate on the candidates that offer the greatest potential. To remain relevant in the digital age, organisations must deliver such experiments with agility and speed.
The obstacle of legacy infrastructure
The problem is that banks’ traditional IT architectures – built around legacy on-premises systems – are a uniquely bad environment for developing these models. Due to the experimental nature of the models, it’s very difficult to forecast what type of infrastructure banks will need for upcoming projects. For example, different machine learning algorithms run best on hardware that has been optimised for that category of model building. If you invest in a cluster of servers with a particular configuration of memory and processors, it may only be suitable for a small subset of the work you actually need to do. And every time you need to change your approach, you’ll face high fixed costs and a long lead time to get the right infrastructure in place.
Instead, you need an IT architecture that allows you to set up experiments quickly and manage them flexibly. When an idea doesn’t work out, you should have the ability to fail fast and cut your losses. And when an idea succeeds, you need to get it into production rapidly and roll it out for enterprise-scale deployment.
The promise of cloud-based analytics
The cloud is the perfect environment for these exploratory projects. It gives you the freedom to spin up almost any type of infrastructure within minutes, and either scale it or shut it down instantly depending on the results.
Cloud environments also free you from dependencies on departmental silos and the quirks of your internal network. They give you a green-field site where cross-functional teams can collaborate freely, enabling you to build models that combine domain knowledge from different areas of the bank and create opportunities for XaaS offerings that would never have been possible in the past.
While most of the major public cloud providers now offer a range of analytics-specific infrastructure services, they come at a price. Once your data and models live in a particular proprietary cloud repository, they can be difficult to get out again. You’re locked into their infrastructure for the foreseeable future.
Besides the commercial implications, this lock-in poses a major regulatory problem for banks. According to the latest consultation paper on outsourcing and third-party risk management from the Prudential Regulatory Authority (PRA), regulators expect banks to be able to port any outsourced services over to another provider or bring them back in house without any risk to business continuity.
The right tool for the job
I’ve had conversations about moving to the cloud with CIOs at banks of various sizes, and this issue of portability has been a recurring theme. They are looking for analytics solutions that work with any vendor and run on any cloud platform – or move between platforms – without significant disruption. In fact, since many banking use cases involve analysing data that is too sensitive to store outside the internal network, one of the most-requested offerings is a hybrid cloud/on-premises solution. Banks could then perform experimental projects with anonymised data in the cloud and then bring the successful models back into their own data centre for deployment in production.
Finally, while there’s a lot of buzz around AI/ML techniques, it’s important to recognise that they are not always the best option. Traditional statistical methods can be equally powerful, cost less to maintain, and can be easier to explain and audit – an increasingly important capability, as a recent legal case in the Netherlands demonstrates. My advice is always that banks should look for a single platform that gives equal support to both statistical and AI/ML modelling techniques and provides easy-to-use visualisations that make models easier to interpret. This allows your data scientists to pick the best tool for the job. And makes it easier for you to ensure the safe and responsible use of your data.
We’re working with a number of leading banks to power their digital transformation initiatives and build towards the XaaS future in the cloud. Find out more about what’s possible with cloud computing.
NO SAFE HARBOUR FOR DIGITAL BANKING
by Konstantin Bodragin, Business Analyst and Digital Marketing Officer at Bruc Bond
At the beginning of 2020, the future of digital banking was pretty clear. Between Open Banking initiatives, regulatory frameworks like the PSD2, and growing customer demand for more advanced digital services, bank-watchers the world over felt confident in their predictions. The course was set for full digitisation, likely brought about by victorious challenger banks replacing stuffy and lumbering traditional banks. Then the winds changed and ongoing disasters shook the world’s seemingly endless confidence in fintech and the bright future it promised to the core.
COVID-19 dropped on us like a sudden thunderstorm on a birthday party. Sure, experts, analysts, prognosticators (and perhaps even meteorologists) all warned of an inevitable pandemic event. But the rest of us, including most leaders and financial giants, were taken almost entirely by surprise. A majority of us managed to get drenched, even though the forecast predicted stormy weathers. Now, leaders and investors are scrambling to reach high ground and keep whatever they can from being swept away in the torrential floods.
In practice this means redirecting funds from aspirational projects towards more immediate goals, and shedding as much unnecessary weight as possible, in case the water rises higher. In the year of COVID, who gets what is not so much a question of wants, but of pure necessity. Unless you’re a government with bottomless pockets, superb credit rating, and a deep desire to stave off a Great Depression-style downturn by means of public works, chances are you too are cutting costs. Big Business is doing the same. Autonomous car projects will be put on hold (if they haven’t been frozen yet), status symbol product launches will be postponed until customers feel confident to spend their extra cash again, and ambitious digitisation projects will be slowed unless their worth can be demonstrated even for the current times.
As they say, when it rains it pours, and this year is particularly wet for fintech. Even if Hurricane Covid hadn’t battered the shores of the global economy quite to so hard, the void left by the sinking of the titanic WireCard would suck much of the industry down beneath the water with it. Just last month, WireCard served as the main provider of banking infrastructure for much of Europe’s Non-Bank Financial Institution industry. NBFIs, tautologically, are not banks. As a rule, until they grow large enough to acquire a bank or banking licence of their own, NBFIs rely on financial and banking facilities provided by another. This is by design, with frameworks like PSD2 regulating access and relationships between various institutions.
Such relationships, under the watchful eyes of local and international regulators, are meant to best serve the interests of customers and consumers. And for the most part they do. Failing or unscrupulous institutions get sidestepped and the system heals around them. Unless, of course, the problem actor is too large. WireCard is one such giant dud, and the sinking of this fintech suppliers will have repercussions that will be hard to mitigate.
WireCard served so many financial institutions that many millions of customers have been affected. Many of these institutions will not be able to survive, and one can only hope that end consumers will be protected from the fallout. On the business end, such hopes for salvation could be too optimistic. Many companies don’t have the resources to withstand several weeks or months of inactivity while they work to replace their financial infrastructure, especially not with extremely depleted budgets due to the ravages of COVID-19.
Those institutions that do survive will face a new reality of confused and likely higher costs, which will almost necessarily have to be passed on to consumers. The more savvy of WireCard’s survivors will try to shore up their defences against the recurrence of such a disaster by spreading the risk and their activity between several providers. This will hopefully lead to a normalisation of costs and a reduction in fees, but by then consumers could once again be too wary to take the risk with digital services whose fees could seemingly spike at any moment.
Loss of confidence won’t be limited to the consumer side, either. Regulators, wary of being made the fool again, are likely to treat fintech and the NBFI sector with much harsher gloves than it did so far. Increased scrutiny, stricter regulatory requirements, and a general lack of cooperation from regulators could sink any hopes of quick recovery for the battered industry. Not to mention the increased costs from such requirements, that are, again, liable to be passed down to the consumers.
Regulators and authorities are not the only power brokers digital banking suppliers will have to contend with. Partners in the banking industry were already eyeing fintechs with suspicion, not least thanks to the egregious claims of the latter to replace the former. Little wonder then, now that the seemingly unbeatable leviathan of WireCard has sunk to the bottom of the deep, that banks will loath to lend a helping hand to NBFIs struggling to find replacement providers.
So what will happen? In this climate, with demands for justice at their peak, some funds will surely be diverted from risky digitisation projects to PR-friendly investment in diversity. Behind the scenes, certain players will carry on their digitisation projects, but their approach is bound to change. The three Ss – slow, steady, stable – are likely to reign supreme, at least until Hurricane Covid passes, and the economic seas are calm once again.
WHY OPEN BANKING SHOULD BE EVERY MARKETER’S BEST FRIEND
By Kathryn Wright, CSO, Upside
To date, Open Banking has been mainly utilised to help consumers with account switching and account aggregation. Being able to have a birds-eye-view of our spending always helps us realise how much money might be slowly ‘leaking out’ of our pockets. As useful as some of the applications have been so far, they are somewhat passive in nature and there is a bigger opportunity at play with Open Banking.
Personalisation has been the holy grail in sales and marketing for some time now, often twinned with omni-channel propositions. According to a study by Gartner in 2018, the brands who personalised discounts and calls-to-action outperform their competitors in revenue by at least 20%. The demand for a completely personalised customer experience has seen many SaaS offerings come to market, promising a complete understanding of your customer.
Many of these technologies are riddled with challenges though, such as customers flitting between devices, moving from mobile to tablet to laptop, and all at different IP locations – which is where omni-channel solutions are needed, but only work reliably when a customer is ‘logged in’. Cookie tracking, or the lack of it, also impacts what is shown to a customer. There’s nothing worse for a customer than clicking through an email and landing on a website just to see a large pop-over asking them to sign up to emails and offers. That’s clear evidence and an example of personalisation not working!
Another bad example in basic segmentation is generalisation. Businesses often take a few pieces of demographic data and then make wildly inaccurate assumptions about the customer. No retailer or marketer needs more data. They need actionable data with insights which can drive action and engagement.
And this is when Open Banking comes into play. By pairing past spending data through Open Banking, marketing teams can better understand their customer base, and brands can personalise which products and offers are shown and when. The end-result is an all-round better experience for the customer, which in turn means an increase in their brand loyalty.
Single Source Of Truth
Businesses currently struggle to know who really is a new customer. It’s kind of tricky when all of the largest discounts are designed to get a new customer on board and marketing teams are heavily focused on new customer acquisition and the cost per new customer.
So who is a new customer? Someone with a new email address that you haven’t seen before? But what about a different delivery address or using PayPal one time and then a card the next time. One customer can potentially register as a ‘new customer’ up to around seven times. Additionally, if I leave my broadband provider this year and come back after a year, am I a repeat or new customer? Brian Dunne from Gift Card Consulting, advisor and investor to Upside puts it well: “There is no such thing as new customers, they’ve all seen you at some point. You are just not getting all their spend most of the time.”
False customer categorisation affects all other business metrics. CAC, CLTV, Repeat purchase rate, customer churn – and these are not trivial metrics, these are metrics upon which huge budgets are committed to or culled. The answer to these questions and challenges in customer personalisation lies in Open Banking. The single source of truth where money can only come out once. Of course, there are credit cards and multiple bank accounts, but the idea is for the customer to have all of these linked.
A new world of data analysis opens up when Open Banking is applied. Retailers can see the frequency of spend, location and average order value. Most brands have this information, but only for themselves. Outside of their walled-garden, it’s more of a mystery. Open Banking allows businesses to benchmark all of these metrics against the rest of their industry, showing what percentage of wallet share they have, which is more meaningful as a metric than an incorrect measure of new customer sign-ups.
For Open Banking to fully show its potential, the conversation with customers needs to change. Brands need to reward repeat purchases and loyalty, instead of offering all of the best discounts to ‘new customers’. Leveraging new fintechs and Open Banking, retailers will be able to know for sure who is a new customer, which will allow them to attract new, win back old and delight their most loyal customers more accurately.
Open Banking – Fiction or the Future of Retail?
Pairing machine learning with Open Banking brings personalisation to a whole new level above simple segmentation and improves the customer experience. Machine learning and AI, combined with Open Banking, are ways to create insights from the masses of data that businesses have. As an example, over time, businesses will be able to recognise when a particular customer looks like they are going to lapse into no longer shopping there, or shop less regularly, and suggest to the brand that at this stage, they offer a special cashback rate. Rather than a ‘spray and pray’ attitude to marketing it means brands can give customers what they need at the right time and ensure their communications are relevant.
Does this sound like a dream? It is not – the technology is ready. Open banking and machine learning can change the way marketing and sales work for any industry. Estimates sit around 95% for the prediction of future revenue which will come from as little as 5% of a brand’s existing customer base. A study by the Center for Generational Kinetics reveals 80% of consumers would visit a store they hadn’t visited before if given a direct cashback. Given statistics like these, retention through delighting and rewarding existing customers, as well as new user acquisition, is imperative.
It’s only the mindset which often holds businesses back. Those retailers, businesses and Open Banking providers who grasp this opportunity and move away from the old discounting culture will rise in the post-Covid-19 world.
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