James Buckley, Vice President and Director for Europe, Infosys Finacle
Following in Amazon’s footsteps, enterprises from every industry are using insights from analytics to improve the customer experience. The push marketing of old has been replaced by a market-of-one approach, where businesses ascertain a customer’s needs and try to fulfill them using micro segmentation and data-driven insights for contextual and timely intervention. In addition to customer-specific insights, businesses can offer recommendations and pitch solutions based on data and preferences of other customers with similar buying habits, needs, and interests. For the digital customers of the millennial generation in particular, this Amazonified experience hits the sweet spot.
In financial services, the closest parallel is the experience provided by Personal Financial Management (PFM) solutions. From primitively classifying account transactions into buckets, PFM has come a long way, and current solutions use comparative insights based on consumer history, attitude and behavior extensively to provide personalized financial advice.
Another important analytics use case in financial services is the systemic digitisation of customer experience. Here, analytics helps to improve sales and target the right proposition to the right customer at the right time. But more importantly, it provides intelligence to ensure that the customer can be positively contained within the digital experience planned, without needing to drop into costly interaction channels.
Increasingly, financial service organisations are turning to analytics to prevent digital churn, directing the customer into higher contact channels only when the bank wants to interact. Analytics is playing a huge role in reducing customer attrition; this is highly desirable for the bank since retaining an existing customer takes far less money and effort than finding and onboarding a new one.
Banks are going the same way as telecom companies which replaced their high street operations in Europe and the United States with an online presence that relied on digital distribution and self-service. Take the example of Telefonica and Tesco Mobile in the UK – both have large teams working with active analytics software to specifically mitigate customer churn and contain costs.
Onboarding has a huge bearing on the quality of banking experience and is a key focus area for banks looking to benefit from the convergence of analytics, artificial intelligence and automation for improving experiences. Together, these technologies are helping reduce the onboarding time significantly – to below three minutes – by digitising and automating the entire process, from document collection to customer authentication. Caveat: while this is driving down the dropout rate during onboarding for the most part, it may also backfire if there is friction in the process.
On the flip side, using analytics to improve experience has become more complicated in the last two years, after the General Data Protection Regulation (GDPR) came into effect. Financial service enterprises, which could freely aggregate “anonymised” information, must now seek explicit agreement from customers on how and where to use their data. In such circumstances – where a customer may accept or reject a data sharing agreement depending on context – banks will find it more difficult to build a systemic data analytics and aggregation platform that standardises data aggregation across customers. In the absence of a standardised analytics approach, banks may be forced to add layers of intelligence to understand what they can and cannot access to build a customer profile.
It is clear customers use different financial service providers for different needs – credit card, mortgage, insurance, investment and so on – which means that even so-called “primary” banks don’t have complete customer data to create an aggregated view. This limits their ability to harness the full potential of analytics. On the other hand, because banks, especially in Europe and the United Kingdom, are becoming more open and ecosystem-driven, they can harness external data and third-party relationships to advance their entry into the customer journey to the point of primary need (when they’re still looking for a house or car, for example).
Banks are looking to increase their relevance by providing lifestage platforms. One such West European bank is servicing the needs of an ageing population by developing a platform for end-to-end healthcare requirements of the elderly. By joining up pharmacies, hospitals, healthcare centres, and specialist gerontology facilities together with transport, the platform is a one-stop-shop serving all the needs of an individual.
This type of “life stage banking” will mature in the next five to ten years as banks try to remain relevant to consumers. In fact, banks don’t have a choice because there are hordes of non-bank providers, from FinTech and BigTech, bringing both disruption and disintermediation. To escape that fate, banks must gather deep insights into customer need, behaviour and context and respond with highly personalised, customer-centric propositions sourced from the best providers in the ecosystem. Analytics will not only play a big role in supplying these insights but also in identifying the best-fit products and services available in the market.
In the case of corporate customers, banks can also leverage analytics to inform next best actions, or to compare and contrast alternative funding and liquidity options, such as overdraft, short-term loan or sweep to tide over a shortfall for example. Five to seven years from now, analytics and AI will not only automate most banking operations, but also have the potential to automate switching between different financial service providers, making the relevance of value added services ever greater and leading to commoditisation of manufacturing in financial services.
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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