The combination of finance and technology is the cornerstone of economic activity. As a sector FinTech has a huge disruptive power for any business, not only the conventional Finance sector and the verticals within it, such as PayTech.

Enterprises are constantly seeking ways to innovate faster, deliver at scale and with high impact.

This research cluster aims to bring together key stakeholders that have an interest in Fintech or seeing an opportunity in Fintech innovation providing a unique opportunity to innovate and disrupt current business practices and ecosystems, and to improve customer experiences

Research Focus

Innovative Payment Paths:
We investigate how digital technologies can assist to change and improve all aspects of payments towards a seamless customer experience. This includes the evaluation of technologies and possibilities of mobile payment solutions to optimize the checkout process.

Technologies to evaluate may include existing contactless payment functions (NFC), new technologies (instant payments, blockchain, chatbot, wearables, etc.) and also various Fintech solutions in the context of European directives. Satisfaction of customers and merchants with existing technologies will also be evaluated and possible improvements for these technologies should be presented.

Data Governance and Consent Management:
With this research we investigate aspects and practices on Data Governance, including privacy and consent management as well as automation of data governance practices and information quality aspects.

Trust and Quality
Information Quality and the trust in financial information and decisions is an ongoing challenge and subject to debate. A diversity of explanation methods for AI predictions has been developed in the literature being able to understand the predictions made by those models.

Researchers have been developing a wide range of XAI that allow explanations or human-AI interfaces with natural language processing capabilities for different stakeholders.

However, trust into those AI techniques is still an issue, especially from experience domain experts, who are knowledgeable about their domain.

With this research we aim to investigate the elements of trustworthiness in Financial information, and how a deep understanding is enabled through explanations provided by explainable artificial intelligence (XAI) techniques.


List of Publications:

Scientific Publications
  • Gabriel Hogan, Markus Helfert: Transparent Cloud Privacy: Data Provenance Expression in Blockchain. CLOSER 2019: 430-436
  • Douglas Cirqueira, Dietmar Nedbal, Markus Helfert, Marija Bezbradica: Scenario-Based Requirements Elicitation for User-Centric Explainable AI – A Case in Fraud Detection. CD-MAKE 2020: 321-341