To help read the results, we have used “Bank” for Cases A, B, C and D and “FinTech” for Cases E, F, G.

Based on the framework illustrated, in this section, we discuss the main results of our research, which also emphasises the dichotomy between FinTechs and traditional players. The results are organised into two subsections (Customer value and Data strategy), where the cases are presented and discussed from several angles, covering all the dimensions of the framework. The references to emblematic single cases are included as an explanation to help in the discussion. The three approaches to Open Finance that emerged from the study are highlighted in “Three different approaches to open finance” section, while the main findings are summarised in “Main contributions” section.

Customer value

The relevance of customer value first emerged “when banks realised that the weight of the customer’s emotions and the value that the customer finds in a brand were becoming increasingly important. From that moment on, they really started to shift their focus to placing the customer at the centre” (Bank A).

What first attracted our attention was how banks refer to their customers. There was no clear distinction between FinTechs and traditional banks. FinTech G refers to a customer as a “human” or “person”, someone with certain types of behaviour, preferences, habits and needs that go beyond mere demographic segmentation, while Bank D “still calls their customers ‘counterparts’. I do so myself, I call them counterparts, we use counterpart in our slides. What I mean, it’s really nasty, how can we even think of putting customers at the centre and then call them counterparts?” (Bank D). Bank A and FinTech F, with their data-driven approach to their customers, hid the fact that they consider customers primarily as data, and do not properly take into account the individual traits that cannot be represented exclusively by data. “At the end, in a bank like Bank A, which has a large bank mentality in general (and even more so with what we are going through), a client is in some way a piece of data, a number” (Bank A). “From a numerical point of view, a customer is a data vector. It’s bad to say so, but that’s how it is; a vector (a client) is the set of n variables collected for a single customer identifier” (FinTech F).

The crucial point is that banks interpreted the growing importance of customers in their different ways. The topic was “a central theme in banks that are alert to innovation, and ready for and responsive to shifts in the market. The smaller the bank, the more this is true. While I am not saying that a large traditional bank like Bank D is not innovative, is not ready, but it’s a giant, it obviously has other priorities and it’s also difficult to compare it to what happens outside in the market. It’s the same for […] traditional banks, with a physical network” (FinTech E).


The FinTechs (Cases E, F and G) presented a startup-type approach with their focus on customer needs (Alt et al. 2018; Gomber et al. 2018). They have few cultural barriers to innovation, in part due to being newly established, and organisations are overall geared towards customer orientation. The banks’ value proposition and vision are both centred on the customer, an arrangement aided by the banks’ organisational simplicity (fewer employees and customers than in traditional banks). In FinTechs E and G, the senior managers’ commitment to customer orientation is highly significant, while it is overlooked in FinTech F. In traditional banks, the senior managers’ commitment to customer orientation starts from the banks’ industrial plans, Board decisions and the chief executive officer’s (CEO’s) speeches, “You will also hear our CEO, who wears many hats, talking about this leg of our business plan” (Case D), although “what makes the difference in the end is real behaviour, the choices you make to achieve these goals. We are still old world, we work in the traditional way, and if you look at who has the power to make decisions in banks today… Bank C was lucky, but it made a choice, its Board took the wise decision to start creating a dedicated team with everyone in it together” (Bank C).

In FinTech E, the customer’s importance is emphasised by redesigning work methods along horizontal organisational lines, running cross-functional projects, creating routine procedures to measure the impact of each decision on end customers and aligning performance evaluation to customer-oriented metrics. Traditional banks face more cultural barriers because of their legacy (Sullivan et al. 2014). The interviewees in Banks (Banks A, B, C and D) all underlined that the shift towards customer orientation is heavily hindered by the employees’ mindset. “Obviously there are stops when a section of your colleagues has historically come from that banking mould and are used to that setup with its different way of doing things, its different relationships, and they may still believe in it, and have not taken to heart this new way of relating with customers” (Bank A). The interviews confirmed that the shift in mentality is a slow process and traces of product-oriented methods remain. On the contrary, the employment policies in these FinTechs indicate that their employees come from particularly varied backgrounds, and include internal designers, as in FinTech G. This policy proved to be an enabler: “Let’s say that 40% of our employees come from other spheres, from Netflix rather than from other banks, as we believe that this could be value added and an enabling factor of innovation and customer orientation” (Bank E). The size and features of their staff enabled banks to adopt an end-to-end vision. “We are not a great big bank, with lots of people, […] people who only do one little part […]. It is also a good thing though […] as we can all see the bigger picture […] I know my digital products inside out” (FinTech G).

However, reluctance on the part of traditional banks to change their work habits and adapt to customer orientation practices is not fully consistent with our findings, as only Bank B showed few concerns, while the other Banks (A, C and D) described their many initiatives to drive the customer-oriented shift and lower cultural barriers. These banks invested in overcoming their heritage. They upgraded their technology and embraced digitalisation (implementing advanced CRM projects, renewing their data infrastructure, offering new digital services and welcoming multi-channel approaches) and streamlined their processes (re-engineering of procedures, cross-functional integration, multi-skill development). They addressed cultural blocks directly (introducing internal managers and coaches to support the shift in mindset, and recruiting new people to assess customer-oriented aspects). It emerged that “in everything you do, in whatever you set yourself to do in any company, you must always think of what impacts and benefits there are for your customers in what you want to put forward, in selling your services, and everything else” (Bank A). Unexpectedly, considering the obstacles described by Lähteenmäki and Nätti (2013), the same holds true for the traditional players, except for Bank B, where the whole organisation showed itself capable of understanding the impact on clients of decisions caused by departmental isolation.

The FinTechs attempted to go well beyond these services, turning to the world of design to streamline the user experience in mobile and online banking applications. FinTechs E and G offer conversional banking, where their customers can type questions into a search bar similar to Google’s. By offering this service, these digital banks are trying to provide customer assistance throughout their entire experience, by being always on tap. FinTech F has based its services entirely on conversational banking, complete with an “assistant”. Customers can access this one-to-one chat service for any need or request (from asking banking-related questions to booking holidays and restaurants), while the FinTechs gather large amounts of data relating to the customers’ passions, preferences and habits.


The FinTechs stressed that being smaller than traditional banks gave them an advantage in terms of organisational flexibility. They can adapt rapidly to new customer requirements with a low time-to-market. Compared to traditional banks, it is easier to organise smaller groups of people and steer them towards a new goal. FinTechs F and G currently show a high level of flexibility, while FinTech E is facing the challenge to scale up and structure itself more like traditional banks, with departments for macro-areas/products and transversal teams for customer-oriented practices.

FinTech G, an independent digital bank, stands out for its approach of combining the product and marketing functions in its organisational structure to manage its promotional campaigns, so the people creating a product are the same as those designing the marketing campaign, as they have a comprehensive overview of the needs that could be addressed by the product and market it accordingly.

This arrangement did not apply to traditional banks, as they had distinct units for these functions. Traditional banks have always been and still are organised into vertical units, each related to a specific product, producing organisational barriers to customer orientation. The obstacles that emerged clearly were the many isolated departments, the significant number of people to be managed and the slowness of procedures burdened by regulations. “Look at all the layers in a traditional bank. I get an idea, I tell it to my line manager who tells it to his line manager and then it becomes like that little game, Chinese whispers. When it reaches the big boss, the person who has to give the go-ahead, it has become something else” (FinTech F). Today, the banks A, C and D have re-organised their structures and processes, integrating functions (cross-functional integration), bringing in multi-skilled employees and job rotations or bringing up internal communication enablers. Bank A has focused on connecting its distinct functions transversally via CRM as the intermediary, meaning that the bank can avoid restructuring its organisational structure entirely (which somehow reflects an internal barrier). Conversely, Bank D has completely reshaped its detached departments by removing the boundaries and only running cross-projects. Bank C set up a laboratory where people from separate departments work together, and it asked the more data-related units (i.e. CRM) to transmit knowledge throughout the bank. “We have created a permanent laboratory where salespeople, IT people and support people for legal, compliance, risk, etc. matters, have all been put together” (Bank C).

The strategy that banks use to promote themselves and connect with their customers is core to customer value. Historically, banks channelled their services through their physical branches. For instance, even today, the vast majority of Bank B’s products can only be sold in the bank’s branch. Under this configuration, banks had management teams, staff units and back offices in their headquarters, with branches to look after the humans, as “the true soul of the bank are the […] thousands of colleagues in the network who develop, do the business, and are the customer’s front end” (Bank A). Colleagues in the branches are also involved “to understand if there are particular circumstances behind certain areas of interest” (Bank B). This structure and these processes, however, hindered the implementation of co-design approaches with customers. A bank branch can capture the voice of local customers, but its actual knowledge of customers is limited. “Because a customer who goes to their local branch and is not happy with how they handled a particular transaction may complain to the branch manager, but it’s all just “words”, it goes no further” (FinTech E).

More recently, traditional banks have also started to introduce digital services, taking up the omnichannel logic, offering products and services through many online and offline touchpoints, which implies going through digital transformation to align themselves to market needs and technological advancements. Traditional banks stressed the need to be consistent, independently of the interaction touchpoint chosen by the customer. In-branch offline processes are easier to execute, as the customer is assisted by a human throughout the procedure. However, when bank branches are spread around the country, it is extremely hard to establish a common vision across the organisation, especially when the branch is in another country. It is difficult to combine qualitative data generated through physical interaction with customers and use them centrally. Banks are, therefore, reshaping the customer experience in branches to give it a “like at home” format, using today’s technology to provide timely data to guide the customer. The proliferation of channels is challenging traditional players to create an integrated and common approach for their end customers. “It’s inconceivable to offer our customers all possible channels, because the cost would become prohibitive. […] So, while in theory, customers are free to use all channels, in reality, the bank must be able to identify the customer’s primary channel and be sure that the channel can create enough of a margin to cover costs and create positive value” (Bank C). “If you know this customer never visits the branch, has never gone there in three years, but has always called customer services or sent an email, then you send them an email or you get customer services to call them” (Bank A).

Although FinTechs started out as digital banks, some decided to include a human presence (Rahi et al. 2021; Akther and Rahman 2021) in segments and value-added services where there was customer demand. A virtual relationship manager can be a way to retain or establish the important relationship of trust (customer trust), while keeping costs down. In FinTech E, despite having no branches, human advisors advise customers at a distance on asset and wealth management. Given their role in guiding relationships with customers, they can receive sensitive information and create empathy, although the bank’s headquarters found that this customer orientation approach reduced its effectiveness, as the advisors were the ultimate beneficiaries.

Customising the way banks interact with their customers is a step towards customer orientation (Prahalad and Ramaswamy 2004). To improve their interaction with customers, FinTechs initiated design thinking, which helps banks to embrace customer orientation by leveraging on data (Knight et al. 2020). “For all the products and services that must be created and launched, they do nothing but listen both to the internal customer, meaning the customer already in the bank, and above all to the non-customer, that is, the prospect” (FinTech E). We found that a specific feature of FinTechs, particularly FinTechs E and F, is to have a design team that operates transversely across the organisation, involving customers directly in their co-creation processes and/or giving them review systems, showing that they deploy advanced feedback collection mechanisms and design approaches (i.e. the use of personas, customer journeys and experience maps). FinTech G, with its team of 20 designers, is capable of instilling a customer orientation mindset throughout all its work methods. “We ran several focus groups even before we started designing and there were people in these focus groups who don’t work at the bank, and we all discussed together, and we understood that we had to help customers build up their piggy banks” (FinTech G). “If I need to release a set of features in three days, I can’t test them with users, so instead I arrange a whirlwind test session, where I ask colleagues who have not worked on the project what they think. So I go to another building, I grab three random colleagues and say: What do you think of this? What helps you understand and what don’t you understand? And this is already the first testing stage” (FinTech G). The same bank representative reported that traditional banks usually outsource design, but the solutions are often quick and dirty and are unable to address the customers’ problems or needs thoroughly and continuously, and so fall short of improving their experience incrementally. Account aggregation (offered by FinTechs E and G and by Bank B through its subsidiary) is another way of interacting with customers, by responding to customer needs in the area of Open Finance to simplify processes and reduce the customer’s effort.

Iterating the design cycles in traditional banks was difficult because of the inflexibility of internal processes, and the need to gather feedback for co-creation or closed loop purposes in product development is often overlooked. “Listening to customers translates into surveys, all the famous surveys we send our customers. We basically use top-down and bottom-up surveys. The top-down ones involve listening to customers, and are therefore basically sample analyses, and we use them for pre-established targets. Bottom-up surveys are triggered by an event; for example, the customer goes to the branch and opens a current account, and within 24 h will receive a welcome email with a link to a questionnaire so we can gage their experience” (Bank B). Additionally, “we take complaints seriously, and complaints are typically spontaneous” (Bank B). Central office handles direct marketing, “mail, text messages, apps, home banking, ATMs, everything that makes customers happy straight away without having to go through that famous human channel” (Bank A).

Most of the cases analysed, as a consequence, turned their attention to marketing and marketing campaign management, which accounts for a substantial portion of the interactions between bank and customers. In the past, the banks used to run mass advertising campaigns, targeting all customers indistinctly or waiting until a customer explicitly asked for assistance. Banks now refrain from deploying this “spray and pray” approach, and have introduced segmentation techniques that can divide customers into clusters, enabling targeted marketing based on everyone’s potential needs.

Regarding group structure, we analysed the advantages and disadvantages of having both a traditional bank and a FinTech in the same group. On the one hand, the FinTech acts as an innovation centre, as a test bench for new products and services or for testing the impact of new proposals in an environment that is highly responsive to change and is more flexible. On the other hand, the FinTech can also operate as an acquisition arm for new customers, leveraging on open banking. Many interviewees noted that this sort of relationship is important in large historical banks, since they can (and already do) import best practice from smaller digital banks, and can also spill over into generating a cultural shift. “People talk about their positive experiences within the group, and sometimes the group replicates, and that leads to other projects. Maybe they are more embryonic, more niche slower in getting off the ground for whatever reason, so you keep them in your drawer until the moment is right” (FinTech F).

Configuration elements, such as the organisational structure in these innovative players, stimulate the parent banks to move away from enrooted practices. Conversely, having a digital bank means introducing higher complexity to management, requiring large investments and with a doubtful impact on the brand image, hindering the traditional bank’s performance. A direct bank with a parent institution will need to invest much less in developing all its back-end systems, as these are shared with its traditional parent (albeit the direct bank will also be taking on a legacy). The direct bank will also improve its ability to offer more complex products, which are backed up by stable financial institutions that can hedge some risks (the parent bank is similar to a “safety net”), help to fund new initiatives or cover potential mistakes. It is also important to mention the benefits of knowledge spillover, marketing improvements caused by connections with an established bank, its physical branches and even its digital-branch data integration. Digital banks can match their customer insights with insights from the parent banks’ branch network. “Our FinTech clearly has some holes in its coverage, what I mean is that they know it and keep on trying to understand why, and talk about it with their colleagues in the brick-and-mortar branch, to see how to tackle and solve certain problems” (Bank D).

However, parent institutions can also introduce constraints. The digital bank could be bound to the siloed-architecture of the old bank, it could have imbued a level of inflexibility determined by the parent bank’s intricate procedures, back-end systems may prove to be legacy technology, and there could be negative culture echoes from the well-established product-centric mindset of traditional banks. “Being part of the group, all the back-end part is within the group, so we were created as a proprietary front-end, all the back-end part belongs to our parent company” (FinTech E). FinTech G showed its independence by not having the constraint of a parent bank, but it also had no safety net, which meant that it had to resort to many partnerships in order to offer more complex products.

Performance management

The results (see Table 2) show the inclusion of both sales- and customer-oriented evaluation metrics to measure the impact of customer-oriented decisions.

Table 2 Details on sales-oriented and customer-oriented metrics (Performance Management)

FinTechs E and F are owned by traditional banks and some of their objectives are connected to the group’s overall profitability (i.e. customer acquisition, FinTech F). Nevertheless, their usage of customer-oriented metrics is advanced and widespread, with about 700 KPIs in FinTech E, and these processes are backed by mechanisms to gather customer feedback. FinTech F’s parent company adopted the churn rate developed by its FinTech, using the formula to evaluate the FinTech itself. FinTech G uses customer-oriented metrics intensively throughout the organisation (e.g. NPS), matching them with sales-oriented indicators to maintain business sustainability. Looking at financial metrics (i.e. measuring the financial impact of customer-oriented decisions), FinTech players are confident that customer orientation can lead to profitability.

Overall, performance management systems in traditional banks are guided by their focus on stability objectives that determine their systemic relevance, leaving little room to face residual risks, and on the centrality of profitability requirements, ahead of adopting a full customer-oriented model. In practice, profitability is a prerequisite for customer-centricity. The banks’ performance management systems tended to exhibit high-level aggregated measures to evaluate customer satisfaction, measuring it for the entire retail bank business and thus not really putting customer-oriented approaches into operation at all levels. Currently, sales metrics still guide the objectives of the different functions. For instance, commercial units are evaluated on services sold, cross-selling and up-selling. Bank A, however, introduced NPS to measure loyalty by spotting promoters and detractors and defining how to manage these results, although it may not be enough to evaluate loyalty while overlooking many other aspects of the relationship between bank and customer (Fisher and Kordupleski 2019). Bank D opted for CLV and Reach-Act-Convert-Engage (RACE) metrics, which indicate the bank’s high commitment to adapt to customer orientation.

Data strategy


“A bank is full of data” (Bank A). While the dataset of traditional banks today relies on larger volumes of data (because of the larger period of collection), FinTechs stand out for seeing the bigger picture.

Transactional data, i.e. demographics, income, number of transactions, product usage and channel data, are almost the same for any bank (see Table 3). The demographics and income data were retrieved from the banks’ account services, while the transaction data were extrapolated from their payment services. Product usage data (i.e. number of products owned by a customer) are typically exploited for cross- and up-selling purposes. The banks also collect data on their service channels, and some gather data about channel usage. Not all the banks routinely gather information on asset ownership (i.e. non-financial assets such as real estate): this practice is followed in traditional banks, while Fintechs apparently do not collect this type of data. One interpretation is that, as FinTechs do not offer asset management services and mortgages, it is less likely that they need their customers’ assets as collateral, or to have a complete overview of their customers’ entire portfolio.

Table 3 Details on type of data collected

Significant patterns emerged in the relational data. Overall, the variables collected relate to lifestyle, habits and preferences, but the banks also gather data on their customers’ lifecycle stage (e.g. how long their customers have been with them) and their future spending predictions and extra-financial needs. These datapoints are extremely relevant for building a more “human” profile of the customer, switching from the old-fashioned “target” approach to the more design-oriented “personas” model, and abandoning the traditional private-to-mass segmentation cluster approach. It is worth noting that, while Banks A and B do not collect relational data, FinTechs F and G have a large set of variables.

Regarding alternative data, the range of variables is broad, with a trend signalling a high level of alternative data within FinTechs. All the banks exploit data on website navigation, e-mail traffic and third-party data. FinTechs E, F and G also collect navigation data through cookies or Google Analytics, allowing them to track the navigation behaviour of customers outside their own digital domains. Banks integrate PSD2 transaction data through innovative interaction features, such as account aggregators, they map in-app and in-chat behaviour to improve the user experience in the features enabled by their mobile and online channels, and utilise geolocation and third-party data, including data from public authorities (e.g. Banks B and D). Additionally, FinTech G offers personal finance management services, helping customers to manage their savings while enabling the bank to gather extremely detailed information on their habits and future projects. These variables can provide valuable information for building more precise customer profiles, and can help in the customer orientation process (Hershey et al. 2007).

Regulation is confirmed to “give us a great opportunity, if used well, to collect a series of data, always within the regulatory framework, and can really help us improve how we profile our customers” (Bank C).

Data value

The findings from the case analysis highlight that FinTechs have a higher propensity for setting data-driven strategies than traditional banks. Their typically smaller size allows them to shift their strategy quickly according to what emerges from the data (as reported by interviewees in FinTechs). However, there is the question of whether such data is of real value.

All interviewees stated that cross-selling and up-selling by leveraging on data is at the core of the banking industry, in order to turn a client into a cash flow. Citing Bank C, it is not possible for a bank to survive solely on new bank accounts, as they are only entry-level products, similarly to “loss-leader” products in the retail industry. “Upselling and cross-selling, thence an increase in customer value. So, if the customer wasn’t at the centre, it would be very hard to achieve these two concepts today” (FinTech F). Designing new tailored products, customising their pricing, offering them through the right channels and targeting their promotion are data-driven initiatives that stem from their cross- or up-selling potential. Relevant findings in the literature support the idea that these forms of data exploitation are compliant with customer orientation (Chan 2005; Hassan and Tabasum 2018; Loshin and Reifer 2013). Additionally, cross-selling and up-selling go hand-in-hand with the level of customisation enabled by the data. Most of the banks analysed emphasised that all these factors represent a competitive advantage in an industry aflush with almost standardised products and services that differ mainly in their pricing schemes. “Listening to the customer, in a world where the offer has flattened out [is] the only way to win the competition, playing everything on relationships, creates relationships” (Bank B).

Loyalty and retention emerged as well (Bank B and FinTechs E, F and G). “The important thing is that this personalisation is not used only to push the commercial side, but also for loyalty schemes and for loyalty to my brand” (FinTech G) as “trying to sell something is the worst way to heal a relationship” (Bank A). Bank D also mentioned that they continuously monitor their customer base by simulating future scenarios based on past data, in order to set their strategic goals.

Each case deemed it important to extrapolate the right data for anticipating customer needs. In order to implement these initiatives, it is fundamental for banks to have sufficient knowledge about their customers, their needs and future spending predictions, and it may help to have a wide range of relational and alternative data at their fingertips. The examples are consistent with proactively identifying their customers aspirations and dreams, or proposing targeted services that satisfy the customers’ very specific needs (such as proposing discounted loans or advantageous joint accounts to recently married couples).

Hughes et al. (2014) and Kshetri (2014) note that customer-oriented firms should not only focus on anticipating their customers’ needs and on cross-selling tailored products, but also on the timing of such proposals. Within the banking industry, the trade-off is between how often and how quickly a bank communicates with its customers and how much of a nuisance it is willing to be. Banks C and D and the FinTechs reported that their data systems are updated in real time for customer actions, hinting at the possibility of sending their customers real-time offers and thus decreasing the time-to-market for cross-selling initiatives. However, these banks (especially Bank D and FinTechs F and G) also reported that they willingly hamper the time-to-market timing. The interviewees explained that time-related offers are now more likely to generate a sense of being controlled in their customers, rather than a feeling of satisfaction. “There are times when it’s better not to do things in real time, but to be a little bit softer in how you implement things, because otherwise you’re saying: I’m tracking you, I’m following you and I’m doing it to make money” (FinTech G). FinTech G set internal limits on the amount and frequency of push marketing communications targeted at customers, with the objective of surprising the customer positively. Counterintuitively, this procedure improved the bank’s cross- and up-selling capacity, as well as its customers’ level of satisfaction (although this was expected). Finally, it is worth mentioning that some banks in the more traditional clusters (Banks A and B) update their systems on a batch basis, and so cannot reach the customer in real-time.

Technological architecture

Three main configurations relating to technological architecture emerged from the cases. While the banks reported on silos, departmental isolation and, in general, fragmented decision making (Velayati 2020), our results show no persistence of traditional legacy systems, whereby customers interact with a business unit via touchpoints and each touchpoint is associated with a single database (silo) (Fig. 3—Silos architecture). These historical configurations caused customer profiles to become fragmented across several databases, hindering the efficiency of internal procedures and the effectiveness of decisions. According to the interviewees, sometimes the CRM unit had to cancel campaigns because the marketing people could not see credit history data, causing loan promotions to reach people with bad credit scores, proposing loans to people with money laundering issues, or investments to customers with low liquidity or investment potential.

Fig. 3
figure 3

Representation of the technological architecture configuration from a silo to an integrated architecture. Internal users are the different internal units that consume the data (e.g. lending, asset management, compliance and risk management). Banks A, B, C and FinTech E have an orchestrated architecture, while Bank D and FinTechs F and G have an integrated architecture

The evidence we collected suggests that traditional banks are undergoing an integration process which is moving them from their legacy systems to newer and more integrated configurations. Today, banks A, B, C and FinTech E have data warehouses that serve the data integration purpose of aggregating the various data marts (silos, databases) that have been deemed strategic by the bank. Banks had to introduce an integration layer (Fig. 3—Orchestrated architecture, a) to deal with the fact that the data semantics are not unified, and also to integrate the data (from the non-strategic silos) left outside the data warehouse. Furthermore, they had to put in place a data orchestration unit. Coherently with the data semantics configuration in these banks, the CRM unit acts as a central hub managing both independent analyses and data-processing requests, where these requests are managed through the previously described multi-step authorisation process, and could involve the IT and compliance units.

As a next step, the elimination of the integration layer and underlying silos (Fig. 3—Orchestrated architecture, b). This stage was designed to remove all the disparities in functions, processes and channels. CRM still plays a central role in the data orchestration unit, as all the other units still need the orchestration unit to elaborate data, meaning that operational disparities continue to persist (Chan 2005).

Data lakes (Fig. 3—Integrated architecture) are sophisticated data management platforms that offer intelligence features and can be used to integrate unstructured data. An interesting point is that, although FinTech F exploits the data infrastructure of its parent company, this parent bank has invested substantially in digitisation, effectively eliminating possible technological barriers for its subsidiary.

In this configuration, which applies to Bank D, and FinTechs F and G, each of the bank’s business units have independent access to the data, without any central controlling entity. This implied developing a need-to-know information policy, educating employees on tools for self-consuming data and teaching them to use both data processing and programming tools (Python, PySpark, Power BI and Microsoft Dynamics were cited). As a consequence, information travels quickly and freely within the bank, with people being given access to the information they need at the time they need it. The data architecture in FinTech E, along with those in Banks A, B and C, are still tied to the past silo configuration, requiring them to define data diffusion procedures that determine the level of information visibility throughout the organisation. The CRM unit still plays a central role here, as it is the only unit capable of accessing and processing the data. “Our channels are integrated in an omnichannel way, so they always talk to each other through the CRM unit” (Bank A). In order to retrieve information and take decisions, the business units in these banks must either wait for the CRM unit to publish periodic reports and dashboards (in Banks A and D) or submit a request for specific reports (the most common option), entailing a multi-step procedure and involving several actors. The request must first be submitted to the CRM unit, CRM then asks the compliance department for authorisation and, lastly, the IT department extracts the data and sends them to CRM to be processed and distributed.

Given the fast advancement in technology, we noticed a fluid approach to the configuration. FinTechs, having been generated more recently in a digital native environment, were able to overcome the more inflexible structure, while traditional banks had to invest and evolve. In both cases, the current configuration will result in new legacy, underlying the continuous need to invest in the area, not just in IT, but overall, as the bank’s data strategy of the company. In Case Y,Footnote 3 “the silo wasn’t there, but now it is […], as the structures we set up are now outdated. Data lakes didn’t exist then. […] Think of a 10-year-old smartphone, it wouldn’t be that good today. […] Rest assured that […] equipment and technological innovation in these banking hardware worlds is much faster than innovation in the world of smartphones” (Bank A).

Three different approaches to Open Finance

Three main approaches to Open Finance emerge (Table 4). The first is a “cautious” approach, showing progress in the traditional siloed product-oriented culture and technological architecture, but limitations in terms of kind of data collected and, above all, the value extracted from those data (Banks A and B). The banks in this cluster will need to work more intensely on their data, on collecting the data, on the data’s potential and on the value that can be extracted for the customer and for the bank. Some banks in this cluster do not offer digital channels. While banks in the other cluster offer the full set, it is left to the final customer to decide which to use. The banks must work on this indistinct approach to the multichannel experience to become truly customer-centric and have a proper data strategy. They need to understand whether the bank’s physical branch can remain its predominant service model and what role and potential exists for the digital channel (e.g. Bank B).

Table 4 Approaches to Open Finance

The second approach “considered” places emphasis on data, data collection and data quality and value (Banks C and D and FinTech E). The banks in this cluster use light approaches, such as in Bank C, and other approaches where the banks are more proactive, including at the organisational level (laboratories, cross-projects). Their common trait is a concordance between the development of technological architecture and their actions for spreading culture internally, enabling the valorisation of data for the customer and the company, as well as data centrality. These players have the advantage of being able to use data of high quality and an extensive historical series to push forward their strategy, but must work to put it into effect.

The third approach relates to the cluster of “committed” banks (FinTechs F and G). It shows extreme consistency between the technological choices and corporate culture, giving them the foundations for strong further potential, in some cases postponing their push towards analytics and sophisticated analysis models to a time when more data are available. Their performance management also evolves transversally, evolving their approach towards an increasing emphasis on customer-oriented metrics. The challenge for these players is now related to their ability to innovate and invest continuously, and to leverage on the data to undertake further advanced analyses.

It also emerged that it is not enough for a bank to be a FinTech (FinTechs E, F and G) to gain a better position and exploit the market to its advantage. While some FinTechs apply both the more considered and committed approaches, some traditional banks use the considered approach more associated to a FinTech (e.g. Banks C and D to FinTech E). Similarly, while it emerged that the positioning of Banks A and B is not yet in line with market development, it is not enough to be traditional to be labelled as cautious, as shown by the aforementioned case of traditional banks that share the same approach as some FinTechs.

Main contributions

Adopting a value proposition centred on customers and value for customers is a must, but it is also rather complex for any firm (Weinstein 2020), especially in a sector that has been lagging for decades. While customer value has been studied extensively in previous literature, the role of technology as an enabler of customer value has received little attention so far. The financial industry was a suitable context for this investigation, having changed radically over the last few years through technological developments and shifts in consumer needs and perceptions (Chen et al. 2021; Ferm and Thaichon 2021; Pousttchi and Dehnert 2018; Xu et al. 2021), as well as being pushed towards the new frontiers of Open Finance by the European Commission (2020).

Overall, it emerges that customisation enabled by data gives a competitive advantage in an industry flooded with nearly standardised products and services, which differ mainly in terms of pricing schemes. Designing new tailored products, customising their pricing, offering them through the right channels via targeted communication are data-driven initiatives that stem from a cross- or up-selling potential, core to the retail banking industry for turning a client into a cash flow. Loyalty and retention emerged as well, enabled by the banks proactively identifying their customers’ aspirations and dreams. Real-time systems open up the possibility of sending customers offers in real time, but generate within the same customers a sense that they are being controlled, rather than giving them a feeling of satisfaction.

Value for customers has become increasingly subtle and fine-tuned, enabled by data and segmentation forms that overcome static dichotomies about demographics, income, transactions, product usage and channel data (transactional data). This wider scope extends towards relational data (lifestyle, habits, preferences, future spending objectives) and alternative data (PSD2 transactions, website navigation, e-mails, in-apps, in-chats, geolocation and third-party data), with a trend signalling a high level of alternative data within FinTechs. The information required by the Authorities presented a great opportunity to collect information that can improve the customer’s profile significantly. Interestingly, the words used by our informants to refer to customers, and which are used in their own documentation, were varied, ranging from “counterpart”, “number” and “vector” to “human”, which already denotes the culture of the company. Omnichannel logics have introduced online and offline touchpoints with customers, with the added complexity of being consistent with the customer whatever the interaction interface, as well as anticipating channel management, in order to steer each customer towards the most correct channel.

Additionally, we found that a completely siloed architecture with customer profiles fragmented across several databases is no longer the dominant model in the industry. The evidence suggests that all banks, including the more traditional ones, are undergoing a process of integration which is moving them away from their legacy systems to data warehouses that serve a data integration purpose by aggregating multiple strategic silos. While FinTechs opted to eliminate the integration layer and its underlying silos, enabling them to combine unstructured data, the integration layer is still a part of the set up in traditional banks. While, in most cases, the CRM unit plays a central role in the data orchestration of data flows, we found cases where there was no central entity and the banks organised their access to data independently. Banks were thus developing a need-to-know policy for information, educating their employees on how to use tools to self-consume the data and giving them the skills and know-how for data processing as well as providing programming tools.

This study contributes to the literature on information asymmetry in banking, which currently is mainly concerned with financial inclusion and intermediation (Grassi et al. 2022; Baek et al. 2020; Demir et al. 2020; Feyen et al. 2021; Mhlanga 2020) or with the potential signals for success (Farag and Johan 2021; in ICO, Chen 2019, Chen and Chen 2020, Šapkauskienė and Višinskaitė 2020; in peer-to-peer funding and crowdfunding, Chava et al. 2021; Lin et al. 2013; Yeh and Chen 2020).

It emerged that the centricity of customer value and data strategy helps to reduce information asymmetry from the external and internal points of view, because of the increased amount and quality of information (Alford and Jones 1998) enabled through Open Finance. Customer orientation, effective processes and horizontal solutions where information can reach all the otherwise distinct teams smoothly and promptly (Moorman and Rust 1999) are ways to reduce this asymmetry, both internally and externally.

The concept of how information asymmetry is linked to Open Finance can be explored further through co-design experiences (culture), customer-oriented metrics (performance management), richer data collection (data) and the exploitation of data value (data value), all of which reduce external information asymmetry between customer and bank, injecting trust into the relationship, leading to greater inclusion and lowering the need for signals linked to trust (see, for example, in lending). Similarly, cross-functional integration and heterogeneous teams (organisation), data visibility and self-consumption (data), and integrated architecture (technological architecture) can all reduce internal asymmetry between the various bank departments, which arises because each will see a different set of information for the same customer. Internal asymmetry thus results in additional requests or less socially desirable outcomes for the cost and time to (re)acquire such information.

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