Special Issue: Exemplars and Criteria for Applicable Design Science Research in Risk Management

Background

In recent years, the field of risk management has undergone significant transformations with the advent of digital transformation and the increasing availability of big data analytics. The rapid growth of digital technologies has led to an explosion of data that can be leveraged to improve risk management practices. However, this increased availability of data also poses new challenges, such as managing the complexity of data analytics and ensuring the reliability and integrity of audit processes.

Key Findings

This special issue presents a collection of articles that showcase exemplary design science research in the field of risk management. The articles demonstrate how design science methodology can be applied to identify and mitigate risks, develop novel audit techniques, and create sustainable risk management systems.

Case Studies

• One article explores the application of deep neural networks to predict credit card delinquencies, highlighting the potential benefits of incorporating machine learning algorithms into traditional risk assessment models.
• Another article presents a three-layer structure for an advanced continuous data level auditing system, demonstrating how design science principles can be used to develop innovative audit solutions.

Conclusion

This special issue provides a comprehensive overview of the current state of design science research in the field of risk management. The articles presented offer valuable insights into the challenges and opportunities arising from the intersection of digital transformation and traditional risk management practices. As the field continues to evolve, it is essential to continue developing innovative solutions that integrate design science principles with practical applications.

Exemplars and Criteria for Applicable Design Science Research

To ensure that design science research contributes meaningfully to the advancement of the field of risk management, we propose the following criteria as exemplars:

Relevance

• The research should be relevant to the field of risk management, addressing specific challenges or opportunities.

Innovativeness

• The research should introduce novel concepts, methods, or solutions that have the potential to significantly impact the field.

Design Science Methodology

• The research should employ design science principles and methodology to develop innovative solutions.

Practical Applicability

• The research should demonstrate practical applicability and potential for implementation in real-world settings.

By considering these criteria, researchers can ensure that their design science research contributes meaningfully to the advancement of the field of risk management.