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.
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.
NO SAFE HARBOUR FOR DIGITAL BANKING
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