Process Mining as a Mean of Process Optimisation

Topic

Process mining is a helpful process management technique that enables business processes to be analysed and evaluated based on data originating from IT-systems. However, due to its novelty and complexity, companies in the Swiss financial industry resist implementing process mining in their operations. Therefore, this paper aims to provide a profound understanding of process mining by discussing its functionality, benefits, and limitations. This thesis focuses on the exploration of challenges arising from introducing process mining in Swiss financial institutions.  By providing key success factors of implementing and applying process mining, managerial implications to hold up against resistance are provided. A Qualitative Analysis of the Resistance to Process Mining in Swiss Financial Institutions.

Relevance

Organisations strive to enhance their efficiency, profitability, and flexibility to remain competitive. Therefore, process-based approaches including process management practices have emerged for analysing, optimising, and controlling business processes. However, current solutions primarily offer qualitative insights based on individuals’ perception. To address this limitation, process mining gained attention for visualising, evaluating, and improving business processes. However, current literature of process mining focuses on the technical and mathematical aspects and neglects the strategic and organisational aspects of its implementation. Consequently, the practical application of process mining within organisations, particularly in Swiss financial institutions, remains under-explored, leading to resistance and challenges, which must be addressed.

Results

Process mining is perceived as a beneficial extending technique to current business process management practices by providing transparency, enabling continuous improvement, enhancing efficiency, and supporting data-driven decision-making. However, multiple challenges such as data availability, data quality, data privacy, and complex IT system landscapes in financial institutions lead to resistance. Furthermore, challenges in an organisational context and the missing expertise on data-driven business increases this resistance. Holding up against this resistance requires Swiss financial institutions to integrate business process management into their corporate strategy, to create a process-oriented and data-driven culture, to secure management support, and to modernise IT systems.

Implications for Practitioners

• Managers should actively embrace process mining as a valuable technique to enhance business process management practices, however, the discipline of process management must be well-establish and strategically aligned with the overall strategy
• Swiss financial institutions must further digitise their processes, modernise their IT infrastructure, and establish a data management plan to collect, store, process, and interpret data accurately, which will consequently lead to successful use cases of process mining
• Swiss financial institutions must foster a process-oriented and data-driven culture within the organization. Without trust in data, an organisation and people cannot benefit from its valuable information.
• Mangers must include all relevant stakeholders when implementing process mining from the beginning to ensure transparent communication, a profound understanding of the technique and to define company-wide objectives for the use of process mining

Methods

This study followed a qualitative approach, as the topic of process mining is new to research and needs to be explored. The data was first collected by means of secondary data, conducting existing literature on process mining. This helped to determine the current state of art of process mining with its benefits and limits. By applying a qualitative approach in using semi-structured expert interviews, current challenges in the integration of process mining in Swiss financial institutions were explored and analysed. With an iterative coding approach, the primary data was analysed and conclusions on the resistance to process mining initiatives could be drawn.