Paper 6 of 6 · Session 3: Enterprise Systems & Innovation
Process-Mining Maturity in Romanian Banking: A Three-Bank Comparative Study
Anca Andreescu, Constanța-Nicoleta Bodea, Radu Țițirigă · ASE Bucharest
Abstract
Process mining has attracted intense practical interest in European banking since the late 2010s, yet empirical evidence on adoption maturity in Eastern-European banking sectors is scarce. This paper reports a three-bank comparative study (universal top-three, mid-tier retail, specialist trade-finance) of process-mining maturity in Romanian commercial banking. The banks score at PM-Levels 4, 2, and 3 respectively, with three drivers of maturity identified: top-management sponsorship continuity, prior investment in data-engineering capabilities, and a stable process-owner role. We discuss implications for supervisory engagement and for curriculum design at ASE Bucharest.
1. Introduction
Process mining — the family of techniques pioneered by van der Aalst and colleagues that extracts process knowledge from operational event logs — has been an object of intense practical interest in the European banking sector since the late 2010s. The Romanian banking sector, characterised by relatively high digitisation but persistent operational inefficiencies in retail-lending and trade-finance back-offices, is a particularly interesting setting. This paper reports on a three-bank comparative study of process-mining maturity in Romanian commercial banking.
2. Background
We anchor the study in van der Aalst's (2016) PM-maturity framework, the Celonis Process-Excellence Maturity Model (2022), and the IS-capabilities literature on analytics adoption (Mikalef et al. 2019).
3. Method
Three commercial banks, anonymised as Bank A (top-three, universal), Bank B (mid-tier, retail-focused), and Bank C (specialist, trade-finance). Data: 24 semi-structured interviews, observation at 6 process-mining initiative meetings, and structured maturity-scoring against a 28-item rubric. Period: October 2023 – April 2024.
4. Findings
Bank A scores at PM-Level 4 of 5 — process-mining integrated into operations-excellence governance, with regular cross-functional review cycles. Bank B scores at Level 2 — isolated pilots, no permanent staffing. Bank C scores at Level 3 — specialist team in place, but process-mining outputs are weakly coupled to operational decision-making. We identify three drivers of maturity: top-management sponsorship continuity, prior investment in data-engineering capabilities, and a stable process-owner role.
5. Discussion
The findings echo Mikalef et al.'s general findings on analytics-capabilities heterogeneity, but identify a Romania-specific factor — the relative scarcity of process-engineering professionals — that constrains the absorptive capacity for process-mining technology.
6. Conclusion
We close with recommendations for the National Bank of Romania's supervisory engagement with operational-risk reporting and for ASE's curriculum on data-engineering for finance.
References (selected)
- Bodea, C.-N. et al. (2021). Process-mining readiness in Romanian banking. Studies in Informatics and Control, 30(4), 67–78.
- Celonis SE (2022). Process-Excellence Maturity Model (PEM-M) v3.
- Mikalef, P., Boura, M., Lekakos, G. & Krogstie, J. (2019). Big-data analytics capabilities and innovation. British Journal of Management, 30(2), 272–298.
- van der Aalst, W. (2016). Process Mining: Data Science in Action, 2nd ed. Springer.