The digital business ecosystem GAIA-X – A session of the FINDER inclusive digital innovation week

At the second day of our Inclusive Digital Innovation in Financial Services & Insurance (FSI) event, we had a look on a European moon-shot project GAIA-X. For this topic we were happy to welcome Hubert Tardieu, chairman of the Board of GAIA-X, highlighting how the project will shape the future of the financial services and insurance market in Europe by “creating a next generation data ecosystem for Europe with a global aspiration”.

The kick-off summit of GAIA-X in 2020 consequentially focused on two major foundations for a project of such scale. First, the overall key concepts to achieve the envisioned cloud penetration of and in the European market were presented. These depict the five pillars of GAIA-X:

  1. Supporting policy rules derived from requirements of a European single market
  2. Support federal data infrastructures (methodology to synthesize different frameworks)
  3. Ensuring interoperability, sovereignty, portability of data
  4. Providing testable compliance to GAIA-X Architecture of standards
  5. Acknowledging open standard setting processes laid out in the internal GAI-X rules of the GAIA-X

Second, the projects governance structure was outlined. Both points reveal what is at the heart of GAIA-X: creating a digital business ecosystem for open innovation.

GAIA-X – digital business ecosystem by design

The professional literature[1][2][3] highlights a couple of design principles to achieve a successful setup of a healthy digital business ecosystem. The summit therefore was existential to growing legitimacy by presenting that GAIA-X is shaping its governance based on these, interdependent, design principles:

  • Demand orientation; stating a mission allows enthusiastic actors to push into the ecosystem instead of pulling them in securing pro-active, responsive behaviour for the joint value creation
  • Openness; in form of a transparent environment enabling an easy access
  • Self-organization; enabling participants to act autonomously to increase commitment
  • Loose coupling; so that participants can join freely and engage in open relationships so that there are no heavy dependencies determining the success of conducted projects
  • Domain clustering; enabling the grouping of participants in projects based on shared interests

Advantages of GAIA-X as a digital business ecosystems

Mr. Tardieu sees a major benefit of GAIA-X in the enabled Europe-wide collaboration between private and public sector. First, the initiative allows to jumpstart the facilitation of digital competence of European companies and thus of Europe as well. Second, it enables European-centred research hand-in-hand with practitioners which in return enables more precise policy-making. Third, the collaboration allows for a holistic approach since it is the only project that is addressing all the necessary elements together from root-to-tip. The initiative captures the alignment of technical standards and services for interoperability and portability (the roots) through the federated trust and sovereignty services (trunk) up to the definition of ontologies, APIs and technology standards for compliance (leaves).

“Gaia-X may seem gigantic but we don’t think that the big issues we are trying to tackle can be ‘sliced’ into smaller parts. In Financial Services and Insurance, this is especially true at a time when cloud adoption needs to accelerate and there is so much change within the industry.”

Based on these the concept of open strategy, nested in a digital business ecosystem, offers advantages for both affiliated producers and consumers.[4] Since for the GAIA-X participants are often both – provider and user of data – the approach taken by GAIA-X is of particularly high functionality. Benefits for data providers are lowered development and launch costs, quality improvement due to a joined development environment and increased speed to market. This, in turn, translates into the benefits of data users since the reduced costs are reflected in the price (up to being open source) as well as a direct incorporation of feedback and implementation of specifications in the development cycle.[5]

Challenges identified, faced and tackled

Nevertheless, open strategizing also presents those involved with the challenge of finding of losing established business models of value appropriation. In the case of GAIA-X, there are two factors that endanger common business models of participants:

  1. The lower costs (should costs be charged) of developed services or products are passed on directly to the user of the data. This significantly reduces the achieved profit.
  2. Differing ownership of input data, managed through data sharing agreements and data use statements, impedes the distribution of the benefits achieved.

Hence, participants have to find new ways to appropriate value within the value chain and thus generate profit from their engagement in the digital business ecosystem.[6][7] According to Mr. Tardieu the participants at GAIA-X are fully aware of that challenge:

Of course, our concern is how do we create data spaces in which those involved will be able to further their own interests too. We also think about how those who put the most effort in from the start don’t lose out to those who might join later when the hard work is done. We reject the idea of selling data. It is an old-fashioned way of thinking.

Part of this process is the integration of researchers as they are developing novel ideas to tackle this challenge. A recently idea explored is the approach of ‘Tickenomics’, originating at the University of Toulouse. The underlying mechanism is illustrated by Mr. Tardieu through an analogy:One example [of Tickenomics] would be to suppose you are in a place with no transportation system. You are selling tickets (or lots) to travellers, to towns and to whole regions. At some point, you will have enough money to create the transportation system. And that is when the tickets become valuable. It might be slow getting started but as soon as everything is in place, it takes off quickly. So we are looking at ways we might introduce ‘tickets’ without the possibility of these leading to monopolies.”

Furthermore, open strategizing[8] allowed the participants of GAIA-X to identify the following barriers which hinder the successful realisation of the visionary mission:

  1. The absence of portability (also known as vendor lock-in or the risk of ‘mainframe syndrome’) – preventing companies from committing to cloud due to future risk.
  2. The potential lack of interoperability – whereby differences in the technical infrastructure may hamper or even render data sharing impossible
  3. The importance of data sovereignty – as otherwise companies would refrain from moving to the cloud due to the risk of misappropriation of shared data.

At the same time the mission-driven initiative also produced a mode of operation to tackle these barriers according to Mr. Tardieu: “The challenges of data portability, interoperability, and common commercial and legal frameworks have different implications for different industries. That is where GAIA-X is helping participants come together to define use cases for their industries and to share information that can make industry data spaces possible. This collaboration is important.”

GAIA-X enables mission-oriented work in industry-specific projects

With the mission, design and barriers of the digital business ecosystem being fleshed out, naturally, the question occurs how GAIA-X will manifest itself through the realisation of projects. Mr. Tardieu pointed out that therefore the domain clustering is of importance as it defines groups to create “data spaces” on an industrial level. Therefore, the FSI industry is a prime pilot since, due to the high level of regulation, collaboration between participants of the public and private sector is required when tackling the challenges of data portability, interoperability, and common commercial as well as legal frameworks.

By working together, they (public & private actors) can increase the chance of success. And this isn’t just about sharing data, remember. It’s also about infrastructure too. Especially where regulations dictate compliance at a local level. You can’t just do it at the application level. This is something GAIA-X is working on.

Furthermore, he lined out that the FSI industry “is ‘ahead of the pack’ because of PSD2 (for a brief overview of PSD2 see this blogpost). We wouldn’t have seen the huge development of FinTechs without it. But this is only half of the work. Data ontologies are key and you will soon see the priority use cases from the financial services and insurance sector start to emerge based on GAIA-X projects.”

The first pilot project – the safe Financial Big Data Cluster – investigating the use case of a joint platform to fuel artificial intelligence services, is currently developed by participants of the private and public sector with involvement of FINDER (for an introduction see this blogpost).

Added benefit to the FSI industry through GAIA-X

While there are different initiatives (for instance, the EU Alliance for Industrial Data and Cloud) Mr. Tardieu sees the benefit of GAIA-X in its holistic approach since it is the only initiative that is addressing all the necessary elements together from root-to-tip. The initiative captures the alignment of technical standards and services for interoperability and portability (the roots) through the federated trust and sovereignty services (trunk) up to the definition of ontologies, APIs and technology standards for compliance (leaves).

“Gaia-X may seem gigantic but we don’t think that the big issues we are trying to tackle can be ‘sliced’ into smaller parts. In Financial Services and Insurance, this is especially true at a time when cloud adoption needs to accelerate and there is so much change within the industry.”

We will be looking out in the future to see how GAIA-X and its pilots will develop thereby changing the European Financial Service & Insurance industry. Stay tuned for more in the future.

Jonas Geisen, ESR

[1] Boley, H., & Chang, E. (2007, February). Digital ecosystems: Principles and semantics. In 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference (pp. 398-403). IEEE.

[2] Adner, R. (2017). Ecosystem as structure: An actionable construct for strategy. Journal of management, 43(1), 39-58.

[3] Tan, F. T., Ondrus, J., Tan, B., & Oh, J. (2020). Digital transformation of business ecosystems: Evidence from the Korean pop industry. Information Systems Journal, 30(5), 866-898.

[4] Appleyard, M. M., & Chesbrough, H. W. (2017). The dynamics of open strategy: from adoption to reversion. Long Range Planning, 50(3), 310-321.

[5] Chesbrough, H. W., & Appleyard, M. M. (2007). Open innovation and strategy. California management review, 50(1), 57-76.

[6] Hautz, J., Seidl, D., & Whittington, R. (2017). Open strategy: Dimensions, dilemmas, dynamics Long Range Planning, 50(3):298-309

[7] Chesbrough, H., Heaton, S., & Mei, L. (2020). Open innovation with Chinese characteristics: a dynamic capabilities perspective. R&D Management.

[8] Gooyert, V. D., Rouwette, E. A. J. A., & van Kranenburg, H. L. (2019). Interorganizational strategizing.

Enabling next-generation customer insights interactions in insurance through explainable AI – A session of the FINDER inclusive digital innovation week

In the last session of the FINDER inclusive digital innovation week, Jeremie Abiteboul, Chief Technology Advisor at DreamQuark, explained how explainable AI works, its benefits, and how DreamQuark is implementing it with customers.

What is explainable AI?

Explainable AI refers to making the decision-making process of a machine-learning model transparent and understandable for a human observer. This includes which data has been used as an input and which variables are proportionally contributing to a model’s decision.

Why do we need explainable AI?

There are multiple reasons why explainable is needed. Firstly, we need to know if input data is biased because that leads to bias-reproducing AI. Secondly, we need to know which variables the model is attributing the most weight to since these could be variables that discriminate against particular groups of people. Thirdly, having an explainable AI model enables companies to address accountability and to be prepared for regulatory reporting.

How to implement explainable AI in insurance?

The prominent business cases that AI in insurance addresses are cross-selling and up-selling, targeted recommendations, and churn prevention. Explainable AI in insurance (compared to non-explainable AI) enables customers to have increased trust in the AI system, validate the business relevance of the model, discover new insights in the data, check for variables that should be excluded, and use it for regulatory purposes.


If you would like to learn more about explainable AI in insurance, please reach out to Jeremie Abiteboul.

Inclusive Digital Innovation Event – Session 3 Summary

On Wednesday, March 17th, S. James Ellis gave a talk concerning ecosystem dominance at the weeklong Inclusive Digital Innovation event hosted by Atos. This discussion comes in the wake of a white paper currently in development centered around the same topic.

The paper views dominance through three different lenses in order to prescribe what incumbent and startups should focus on to gain a dominant edge in digital, data-driven ecosystems. “Dominance,” in this sense, is given a fair amount of room for interpretation, but it hinges on the idea that in an ecosystem where a business’ stakeholders seek sustainable revenue going forward, there exists the possibility to adapt to ecosystem changes while simultaneously gaining some measure of influence over how a company’s peers in an ecosystem engage with each other. This all centers on a core tenet of ecosystems being the variety of interactions between members.

Customer Access

The first argument asserts that customer access – distinct from customer engagements – is a path to focus on when seeking a dominant position in ecosystems. While many companies do indeed prioritize interaction with their customers as a general objective, this point of view suggests that building the material or conceptual infrastructure to own engagement with the customer is key to gaining a dominant advantage. This could be actualized, for instance, through building “vessel offerings,” where the focal company bundles its own offerings alongside complementary companies’ offerings. The example James gave was that of Internet companies that bundle television companies’ offerings in with their own services, thereby owning access to the Internet and television customer. As the customer, in this perspective, is assumed to be the leading force in ecosystem innovation, this begets an advantage in seizing customer-led innovation opportunities – and thus, a sense of dominance concerning this.


Similar to hallmark resource-based approaches, this viewpoint asserts that access to key resources is the key to finding a dominant position in ecosystems. However and somewhat particular to data-driven ecosystems, these key resources are interrelated proportionately. That is, a company must achieve an interlinked balance of capital, talent, and data in order to most effectively advance its position in its ecosystem. This viewpoint further posits that an overage of any of these resources without a correlated gain in the other two will result in an inefficient operating position, which could slow the company down enough to jeopardize its dominant advantage.

Tri-axis model of key resources, with the cone representing an optimal balance through growth and time.

Ecosystem Centrality

The final viewpoint asserts that a company that systematically pursues the most ecosystem connections, thus centralizing itself among participants, stands to gain a dominant edge among peers. By establishing material linkages with other companies, such as supply chain redundancies, formalized partnerships in joint offerings, and the like, this central and centralizing company begins to insulate itself from the inevitable failures and disruptions that occur in ecosystems, and especially those experiencing the turbulence of broadscale innovation.

The white paper will be available through Atos’ Thought Leadership publications later this year.

Other sessions in the event were given on de-risking corporate startups by Josemaria Siota, GAIA-X by Hubert Tardieu, life-fulfillment services as can be offered by retail banks by Eddy Claessens, and enabling next generation customer insights and interactions through explainable AI by Jeremie Abiteboul.

‘How to de-risk corporate startup innovations, while improving speed and cost?’

FINDER and Atos joined with practitioners, academics, and policy-makers to discuss how to yield benefits from these developments by re-positioning banks in the ecosystem, using Artificial Intelligence in insurance, mitigating risks in new venture collaborations and exploring the opportunities of the European GAIA-X project.

On Monday 15th March, the first day’s sessions took place as part of FINDER and Atos’ ‘Inclusive Digital Innovation in Financial Services & Insurance Event Week’. Atos CTO Remco Neuteboom ( and Rick Aalbers (, Associate Professor Strategy and Innovation at Radboud University hosted the sessions. They were joined by Josemaria Siota – Executive Director of the IESE Business School – who presented findings from a new corporate venturing report. The discussion was moderated by Nikhil Chouguley, the Global Head of Product Governance & ESG Oversight at Deutsche Bank.

The first half of the session was focus on new research on corporate venturing. Josemaria Siota presented the latest research on the new role of corporate venturing as an ‘enabler’. The research findings showed the importance of a corporate venturing ‘enabler’. The enabler is “An institution or individual, within an innovation ecosystem, that facilitates a resource or activity in the collaboration between an established corporation and a startup, in order for the corporation to attract and adopt innovation.” There are many types of enabler, including private accelerators and incubators, research institutions, venture capital firms or investors, governments and even other corporations. The enabler role is to help determine the innovation gap, explore the options for building the innovation capacity or partnering with others, and facilitate any partnership. Then Josemaria Siota answered about the research and its implications in the market.

The second half of the session was open to the audience. Host Nikhil Chouguley introduced himself and explained how he was interested in both sides of the relationship. He is responsible for governance at a major corporate in his day job, but he also operates his own fintech startup. Nikhil was particularly interested in the role of enablers and the relatively new concept of corporate venturing squads. The 25% of collaborations that had succeeded still represented a huge positive as he invited audience questions for Josemaria Siota to answer the research and its implications in the market.

Josemaria Siota explained how the research points to five crucial conclusions for corporates:

1) Protect your company’s core business when running corporate venturing through an enabler

2) Choose capabilities rather than ‘packaging’ to filter potential enablers. For example, working with partners via a local enabler that has a deep understanding of a specific sector in a specific country

3) Remember that enablers are not just consulting firms – the reality is far richer as they bring databases, events and other ways to connect organizations

4) These opportunities offer you a completely new revenue stream: enabling other partnerships through corporate venturing ‘squads’

5) Every day, the company is becoming less and less unique and enablers can improve your value proposition

There was also time for one key conclusion for potential enablers: A proven capability is the most frequent aspect considered by partners. So always under-sell and over-deliver.

The full research findings are available to download for free. (

About the event:

FINDER is a Marie Curie Research and Training Program funded by the European Committee and has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 813095. This session took place as part of Atos’s ‘Inclusive Digital Innovation in Financial Services & Insurance Event Week’ (15th to the 18th March 2021). This is part of Atos and Radboud University’s joint initiative FINDER (, funded by the European Commission.

Retail banking transforms into life-fulfillment services – A session of the FINDER inclusive digital innovation week

On Thursday, our event week on inclusive digital innovation in FS&I reached its final day. It was exciting to bring together perspectives from academia and practice. On our last day, we discussed how banks could transform into life-fulfillment services platforms. Eddy Claessens, Industry director at Atos, gave us insight into his hyper customer-centric retail banking vision, so-called life-fulfillment platforms. The session was hosted and facilitated by Jonas Röttger, a Ph.D. Candidate on the FINDER project.

What are life-fulfillment services platforms?

Life-fulfillment platforms refer to a vision on retail banking. It represents a business model in which the customer interacts through the banking platform with various ecosystems to fulfill diverse real-world needs. For instance, purchasing train tickets, filing insurance claims, and organizing a move. Of course, the platform also offers core financial services provided by almost all retail banks today. The general idea is that life-fulfillment platforms cover all of the customer’s needs that are related to financial transactions. These customer needs can be summarized into four cornerstones of the vision.

  1. Pay and spend
  2. Save and borrow
  3. Invest and protect
  4. Receive and earn

Which roles can banks play in this model?

The overall vision is to create a one-stop-shop solution, meaning one bank becomes the exclusive entry point for customers to fulfill various needs (see above for categories of customer needs). The short-term and mid-term perspective requires banks to position themselves concerning their function as links between the customer and various ecosystems. For instance, banks can decide to act as advisors for ecosystems where they have expertise but do not want to get directly involved. Or they can aggregate services and products in their offerings. The role banks can play hinges on their competencies and prospects. The lfie-fulfillment services vision sees four different roles mentioned below as the most promising options for banks:

  1. Advisor: the bank consults the customer on what to do, when to do it, how to do it, and with whom to do it.
  2. Facilitator: the bank provides, orchestrates, and curates a platform for different stakeholder groups to not only find each other but also interact and transact,
  3. Aggregator: the bank will package and integrate homegrown and third-party solutions.
  4. Initiator: the bank offers direct access through bank distribution channelsto specifically supported externally sourced services or products

What did we learn in our discussion with academia and practitioners on the topic of life-fulfillment services?

  • Banks are already working towards the implementation of similar models. The risk of disintermediation and the lower margins in various fields of retail banking requires banks to shift their business models. Hence, becoming a provider of new services is an appealing vision for banks.
  • Banks do not need to fulfill all roles proclaimed in the model. It strongly depends on their customers and their position in the ecosystems. Which relationships can be leveraged? Also, the customer journey for individual use cases shows which role to play.
  • Banks are well-equipped for the data-driven operational model that is needed to become life-fulfillment service platforms. They have the customers’ trust and access to rich financial data. However, the analytic capability might be something that needs further development, and that can be achieved through partnerships.

If you would like to learn more about life-fulfillment services, please reach out to Eddy Claessens for further dialog on this topic.

Atos and FINDER to host online event week on digital innovation in financial services (15th until 18th of March)

Atos and the FINDER team are hosting an online event week on Inclusive Digital Innovation in Financial Services & Insurance from the 15th until the 18th of March, everyday at 16:00 CET (Thursday already at 15:00 CET). To see the agenda and register for the event go to

The event consists of five sessions with presentations by world-leading speakers:

  • GAIA-X: The future of the European datacloud (Hubert Tardieu, Chairman of the Board of GAIX-X)
  • How to de-risk corporate-startup innovations, while improving speed and cost? (Josemaria Siota, Executive Director of IESE Business School’s Entrepreneurship and Innovation Center)
  • Ecoystem dominance (Ivo Luijendijk, Group Industry Director at Atos and S. James Ellis, FINDER PhD candidate)
  • Retail Banking transforms into Life-fulfilment services (Eddy Claessens, Group Industry Director at Atos and Jonas Röttger, FINDER PhD candidate)
  • Enabling next generation customer insights & interactions in insurance through explainable AI (Jérémie Abiteboul, Chief Technology Advisor at DreamQuark)

About the event

The COVID-19 pandemic has been a catalyst for digital adoption across various aspects of our private and professional life. In the financial services and insurance industry, processes are increasingly tackled by leveraging data, machine-learning, and Fintechs/InsurTechs. Atos has joined forces with practitioners, academics, and policy-makers to discuss how to yield benefits from these developments by re-positioning banks in the ecosystem, using Artificial Intelligence in insurance, mitigating risks in new venture collaborations and exploring the opportunities of the European GAIA-X project. This event week is part of Atos and Radboud University’s joint initiative FINDER (, funded by the European Commission. Five independent sessions will allow you to listen to expert presentations and discuss with the presenters and your peers your thoughts, ideas and questions. Please see below for our world-leading speakers.

M&A announcements: How much confidence to convey if you are considered overconfident?

Photo by Sharon McCutcheon on Unsplash

CEOs helming the next acquisition are commonly expected to convey confidence in the outcome of their recent strategic decision to pair up with others for the future. However, too much confidence by the CEO, also known as CEO overconfidence, can jeopardize the value-creation of deals due to a higher likelihood of overpayment: CEOs who are overconfident believe to possess superior capabilities in deriving synergy from acquisitions leading them to make higher bids than more rational CEOs.

Overconfidence is a widely spread human phenomenon. It affects humans’ belief in their capabilities and the precision of their judgment. For instance, people often believe to be better-than-average car drivers, which violates a rational conception of an average. People in powerful positions are even more prone to fall victim to overconfidence since their assignment indicates superiority by nature. Hence, it is not surprising to find overconfidence among CEOs.

In the context of mergers and acquisitions, overconfident CEOs represent a risk to shareholders. While it is common to observe the acquirer stock plummed upon acquisition announcement, this reaction is especially true for acquisitions that will be helmed by overconfident acquirer CEOs. So how do firms helmed by more overconfident CEOs communicate acquisition announcements so that investors do not start selling their shares?

We conducted a study on acquisitions by S&P500 constituents between 2014 and 2020. Using an automated linguistic analysis on acquirer press statements, we found that investors react more positively to acquisitions by overconfident CEOs if the firm’s announcement press release conveys less confidence in the deal. That represents an exciting finding since usually conveying confidence in a strategic decision represents a positive signal for investors to draw on. However, it seems that the effect depends on who is signaling the confidence. In the case of an overconfident CEO, it appears investors prefer a bit less confidence, maybe because that shows a more realistic view of a given deal, which evokes confidence in investors that the acquirer is on the right track.

While the linguistic analysis of firm communication does not represent a novelty for business analysts or researchers, the interaction of CEO characteristics (i.e., CEO overconfidence) and firm communication is currently not undergoing scrutiny. Hence, also something to be considered by marketing and public relation departments when announcing deals to the public. Considering the past performance and press portrayal of the CEO might be valuable when writing press releases.

– Jonas Röttger, ESR

Collaboration FINDER and TechQuartier for the project ’Financial Big Data Cluster’

On the 1st January 2021 the initiative Financial Big Data Cluster was launched with the research project “safeFBDC” as a solution for a technological driven development of the European Financial Sector. To do so the safeFBDC congregates a consortium of public and private collaborating partners managed by TechQuartier – to leverage knowledge in the areas of artificial intelligence, machine learning and business model development.

Therefore, the initiative is a response to increasingly structural change, fuelled by technological innovation. Participants are reacting to the challenge of adaptation with increasing speed.[1] As “banking is unbreakably connected with the use of information technology”[2] the financial sector is a prime representative of the importance of technological innovation. While US and Chinese actors have been predominant in the adaptation of technological innovation in the financial sector, European actors have to step up their game. Their engagement is of importance to secure data sovereignty and thus obtain a competitive position when it comes to data-driven financial services. To achieve this collaboration of the private and public sector is of utmost necessity. For this applicable, european-centristic research is needed to understand and thus enable innovations and their necessary environment. Providing such research will in turn enable the proactive engagement of practitioners.

Thus the safeFBDC project is set up to deliver on these necessities by aligning three major goals:

  1. Increasing research output through the development of new AI systems and analysis of new, information-rich data sets.
  2. Enhancing financial stability by facilitating the exercise of oversight and supervisory functions by public authorities.
  3. Promoting the development of new data-based products, services and business models, and to increase the transfer of knowledge from research to business.

Collaboration on the research of new business models driven by technology

To facilitate applicable, european-centristic research of the financial sector TechQuartier and FINDER, have decided to join forces. Together we want to utilize the opportunity the safeFBDC is providing to study the collaboration driven by technological change. To do so Luisa Kruse from TechQuartier and me will work together on this project. Aim of our collaboration is to study the underlying organizational mechanisms driving this flagship project. By doing so we generate value in three important ways. First, we facilitate applicable research to enable practitioners. Second, we gain a better understanding of how technology affects opportunities of innovation. Third, we establish a new venture of research of the European financial sector. The progress of our collaboration will subsequentially covered within my blogposts culminating in a collaborative whitepaper.

Jonas Geisen, ESR

[1] Schwab, K. (2017). The fourth industrial revolution. Currency.

[2] Thalassinos, E. (2008). Trends and Developments in the European Financial Sector. European Financial and Accounting Journal, 3(3), p. 58

How banks should harvest their internal data

Data fuels decision-making. Banks are well-equipped with the financial data of their customers. Experts often point out that consolidating internal financial data with other data sources (e.g. behavioral data, macro-economic data, etc.) will unfold data’s full potential. Yet, banks’ rich internal data is regularly overlooked as an opportunity that can be used to fuel decision-making. Banks need a solid data-gathering strategy and advanced data analytic skills to leverage their internal data.

How should banks approach internal data?

Data needs to be gathered with a clear purpose. Hence, the journey towards a data-fueled operating model starts with defining clear use cases. Subsequently, the use cases have to be checked against reality. Therefore, banks’ internal data should first be inventoried and categorized. It is crucial to define a timeframe for which data collection is performed (depending on the use case, data collection for the last three to ten years could be most suitable). Subsequently, the data can be put to work through e.g. model-building. While harvesting data with the goal to implement use cases is crucial, the strategy should also entail how to manage data in the future. Harvesting data from legacy architectures demonstrates the potential of data in general but is very inefficient for future endeavors. Here, breaking down data silos and building data lakes represents a robust solution. Currently, banks are still struggling with small projects that only reach the proof of concept stage and large projects that are abandoned due to overwhelming complexity. Incremental progress on mid-complex level projects represents the largest potential to strive.

Too much of a good thing: why data frugality is important

Occam’s razor is the idea that in problem-solving, the simplest solution is usually the right one. This approach is well-adapted in data science for several reasons. Firstly, a model’s appetite for data increases the risk of having unobserved data points which negatively affect the predictive power of a model. Secondly, more data increases the training time for models. More training time means more energy and consequently higher costs. Thirdly, more data can lead to impaired explicability of a model as a complex model’s results are harder to interpret. This is especially the case if deep learning methods are applied (which remain to a large extent black boxes). The low explicability of models prevents their application as part of automated decision-making due to GDPR regulations. Moreover, low explicability could make the model unstable in times of new hitherto unseen data. Users will have difficulties to explain why and with what accuracy the model is adapting to the new circumstances. In general, striving for parsimony is an important criterion for which banks have to optimize when using their data.

Keeping data in the loop

Oftentimes, it is argued that data evolves from simple data to information to knowledge. While that is true for many use cases, it should be pointed out that data-fueled decision-making does not always require intense computation to become knowledge. Depending on the level of human-in-the-loop or the affordances of a decision, very simple data points can be highly informative. However, if data is processed in a time-consuming and complicated manner to derive knowledge (e.g. in form of a report), this knowledge should be kept in the loop. Hence, the results of data processing should become part of the data storage.

– Jonas Röttger, ESR

Open Banking – an opportunity within grasp

The content of the FINDER blog is not an expression of Commerzbank AG, nor created on behalf of Commerzbank AG. The content is created and contributed by private persons.

On 05.11.2020 we tuned in into the Open Banking Summit held by the Commerzbank in cooperation with the Business Engineering Institute St. Gallen. We will have a look into the Summits key notes to see how the realisation of Open Banking is progressing based on this use case  –  which opportunities may arise and which challenges are still to face.

Open Banking became more prominent in 2016 when the United Kingdom announced its Open Banking Standard and the European Union published its Revised Payment Services Directive (PSD2). However, it only gained momentum in 2018 when these drafted legislations came into effect. Simplified these laws require banks to open up their IT infrastructure. Technological this is done through application programming interfaces (APIs) which allow different IT infrastructures to communicate with each other. In the case of Open Banking, APIs enable third parties to connect to banks existing IT infrastructure and thereby access and usage of the data gathered– say bye to data silos guarded by banks.

The backbone of the Summit was a whitepaper The Future of Collaboration in Corporate Banking in which Joerg Hessenmueller (Commerzbank AG) defined

API [as] a crucial technology that enables communication between IT-systems with enough flexibility to address the complexity of today’s world [based on] closer collaboration among different parties leveraging on their different capabilities to create value for the customer”.

Resulting from that one can draw the conclusion of David Kauer (PostFiannce AG) that

“Open Banking is a fundamental strategic and architectural question. Banks do not just do Open Banking – Open Banking is a framework that requires a 360-degree view of business and corporate clients and their needs. Banks, thus, have to decide wisely about the order of actions they take to follow such an approach.”

So what is achieved so far?

As the use case of Commerzbank depicts cooperation is key to identify and leverage the options available. Slowly, new networks are emerging. First attempts of opening up are made. So far these are still in their infancy. An example is the developer portal. This sandbox provides developers the documentation and option to play around and get used to the APIs provided by the Commerzbank. When having a look at the opportunities and challenges it is, however, clear that this is only a small first step in the right direction.

What are the outstanding opportunities?

The approach envisioned by the PSD2 is to fundamentally change banking in the European Union. Its implementation is aimed to enhance the value proposition of financial organisations. The basic framework is set to achieve a higher degree of cooperation and co-innovation between banks and third parties for example FinTechs. This is highly dependent on the abilities of banks to think beyond their organisational borders. If this outward-opening is happening the most valuable opportunity can be realised:

Building a new digital ecosystem marked by new business models and driven by customer expectations.

Technological enabled would such an ecosystem be through the opening of banks APIs. Cooperation, innovative ideas could facilitate user value by enhancing consumer protection and security of internet payments as well as account access within the EU and EEA. Accordingly, the opportunity for customers is access to enhanced services within one digital ecosystem. Such services would greatly enhance banks attractiveness by increasing their value proposition. At the same time, FinTechs have the opportunity to grow by getting access to a greater market reach or even provide the B2C of banking. Another actor in such an ecosystem would be BigTechs which, according to David Kauer, could take a role as technological orchestrators. In that case banks would probably occupy the B2B in such an B2B2C banking ecosystem. To not be pressured into the role of an anonymous backoffice service provider banks have to seek an pro-active role. So in general Open Banking should not be understood as a threat or zero sum game by banks but instead as an opportunity. In that sense all actors would profit in the banking B2B2C ecosystem.

Which challenges is the industry still facing?

However, the transformation is still facing challenges that need to be tackled for a digital ecosystem to emerge. As the banking sector will open up for everyone offering financial services a mind-set of collaboration is of importance. Customer centricity should be the focus flanked by provisioning of the necessary infrastructure –for example in innovation labs. An optimal setup is completed by a bank’s readiness to identify partnerships and then leverage resources to seize the presented opportunities.

Technological there are still some hurdles that hinder the facilitation of a collaborative approach to adapt to structural change. Technological readiness is one challenge to face. The adaptation of key technologies across the industry differs strongly and may, in the current state, make collaboration more difficult. Tightly connected to this is the missing standardization of APIs. Heterogeneous architectures for the same services are making a fast and approachable cooperation across organisations fairly difficult.

Future will show of all potential actors can overcome these challenges and thus provide the necessary prerequisites to foster an ecosystem marked by innovative ideas combined with industry-specific know-how

–  Jonas Geisen, ESR