2024 | Two Contributions in the Journal of Machine Learning and Knowledge Extraction 2024

In 2024, the Chair of Application Systems and E-Business published two scientific articles in the prestigious Journal of Machine Learning and Knowledge Extraction, authored by Mustafa Pamuk.



On July 27, 2024, the following article was published:
"Towards AI Dashboards in Financial Services: Design and Implementation of an AI Development Dashboard for Credit Assessment"



Abstract: Financial institutions are increasingly turning to artificial intelligence (AI) to improve their decision-making processes and gain a competitive edge. Due to the iterative process of AI development, it is mandatory to have a structured process in place, from the design to the deployment of AI-based services in the finance industry. This process must include the required validation and coordination with regulatory authorities. An appropriate dashboard can help to shape and structure the process of model development, e.g., for credit assessment in the finance industry. In addition, the analysis of datasets must be included as an important part of the dashboard to understand the reasons for changes in model performance. Furthermore, a dashboard can undertake documentation tasks to make the process of model development traceable, explainable, and transparent, as required by regulatory authorities in the finance industry. This can offer a comprehensive solution for financial companies to optimize their models, improve regulatory compliance, and ultimately foster sustainable growth in an increasingly competitive market. In this study, we investigate the requirements and provide a prototypical dashboard to create, manage, compare, and validate AI models to be used in the credit assessment of private customers.



On January 11, 2024, the following article was published:
"What Do the Regulators Mean? A Taxonomy of Regulatory Principles for the Use of AI in Financial Services"



Abstract: The intended automation in the financial industry creates a proper area for artificial intelligence usage. However, complex and high regulatory standards and rapid technological developments pose significant challenges in developing and deploying AI-based services in the finance industry. The regulatory principles defined by financial authorities in Europe need to be structured in a fine-granular way to promote understanding and ensure customer safety and the quality of AI-based services in the financial industry. This will lead to a better understanding of regulators’ priorities and guide how AI-based services are built. This paper provides a classification pattern with a taxonomy that clarifies the existing European regulatory principles for researchers, regulatory authorities, and financial services companies. Our study can pave the way for developing compliant AI-based services by bringing out the thematic focus of regulatory principles.