Today, many organizations are beginning to rely on a combination of Business Process Management (BPM), Robot Automation (RPA), and Artificial Intelligence (AI) to facilitate smart processes. This transformation will require the implementation of new technologies and, above all, the inclusion of data experts to create data products. Why is this so important?

While RPA and BPM focus on standardizing and automating processes, both disciplines do not learn from their activities’s reiteration. A robot might conduct the same activities corresponding to its rule-based framework, eternally, and it will not be able to learn. The same applies to automated workflows. Optimization is done by a human being who configures and readjusts the setting in a better way. The lemma here is: “just do it and repeat”.

In contrast to traditional process automation, AI is more about learning. The learning aspect is possible because AI algorithms are capable of learning from historical and current data. They automatically deliver new ways of doing things- faster and more effectively. The result is, in the best case, end-to-end automatization.

To get the full potential of AI, organizations should create data products to make their processes smarter.  A data product solves a business use case, combining (public) cloud technology, custom-developed business logic, and data. The relevance of data in business goes beyond buzzy trends and it manifests its potential with superior business models that we encounter in our daily lives. For instance, Netflix, Uber, Facebook, and Amazon obtained a sustainable competitive advantage by introducing business models that allow them to become data-driven companies.

New business models are emerging data-driven neobanks. Suppose you are a customer of N26, RevoluT or Openbank. In that case, you might notice that financial data such as balance account and transaction is updated in real time and that you can obtain a prompt response to your questions via chat. If you are not aware of this way of digital banking, you will hear from them very soon.

The example: Intelligent Processes at a Neobank

To illustrate the value of AI, we will apply the principles of the Business Model Canvas ( Osterwalder, AlexanderPigneur, Yves 2010) for a fictive neobank.


Key Partners

Neobanks need partnerships to access crucial resources not available within the organisation. The first on the list are investors willing to proportionate financial resources for something relatively new and with certain risks. Investors might be established banks self, angel investors, accelerators and so on. Additionally, they are required to partner with competent technology organizations capable of creating (AI) applications that satisfy the unique needs of such a venture.

Key Resources 

On the journey of becoming a data driven neobank, they need the speed and flexibility of a cloud platform to survive in the world of bank regulation. For example N26 used AWS as a platform. When a business model is based on intelligent processes, then the obvious consequence is that a team of data practitioners should exist. Those data experts might -in most situations- obtain expertise in devops, mlops and agile practices from the experts outside the organisation (Key Partners).

Value proposition

One difference between traditional banking and neobanks is that customers do their transactions for free online on a platform. Digital banks´s value proposition is based, basically, on new technologies and it is free of legacy systems stopping innovation. This transformation leads to innovative banking services such cryptocurrency exchange, personal financial management, peer to peer payments, improved customized credits and more.

Customer relationships 

The impact of AI can be huge in building customer relationships. AI personals bots available 24/7 using voice and interacting with customers with Alexa, Google Home or similar is not science fiction anymore. In contrast to rule-based chat, the customer is with AI able to have meaningful interactions in a wider domain of topics before ringing the hotline. 

Another field is Natural Language Understanding. Through advanced techniques it is possible to detect feelings, entities, and intentions in calls to the call center. Voice data get analyzed, changes are made and the quality of service improves with each new interaction. The customer feels as time passes by that the bank understands him or her even better.


For a modern bank, social media channels play a very important role to reach young people for example that might be interested in opening a new account. Also, monitoring social media and integrating it with other channels is of high relevance to guarantee consistency and real-time communication with the customers: anytime, anywhere. 

Again, AI can bring omnichannel communication to the next level. Newsfeeds, interesting updates, appropriate and not appropriate information can be sorted out through machine learning. Relevant touchpoints with customers on Facebook, Twitter, or Instagram can be unified in a dashboard and indexed by priority level. For example, if a customer has a complaint online a smart and learning algorithm (see Launching workflows) could monitor and route that case to the available support employee with the best skills for that situation.

Customer Segments 

The obvious segments are young people, digital nomads, and early innovators. However, through the usage of AI new hidden segmentation can be made explicit beyond the understanding of the traditional rule logic framework, which very often seems more understandable for human beings but can be limiting. 

Cost Structure and Revenue Streams

AI can help reduce costs by automating image reading. A human eye is not always necessary to verify incoming emails with images, for example, to verify mobile photos done by a customer for a travel insurance claim (a common additional service offered by neobanks).

On the other hand, AI can help to match the right pricing dynamically for a certain customer who might be able to pay more for an extendable insurance service or a premium customized package. 

All in all, Neobanks are transforming the way we do banking. Independently, in which sector you are, data products will unleash new ways to create great customer experiences. Maybe, the most suitable question, at all, is not if data products are important but how they are going to influence your business soon.

Image: Rawpixel


  • Daniel Ávila

    Business Development Manager at Keepler. "I am a data enthusiast with business background. My lemma is business first, technology second."