Technology and digitalization in accounting and auditing

How digital tools change what accountants and auditors do.

Team of business people working in the office

Our recent work covers machine learning for corporate environmental performance ratings, the move from business intelligence to artificial intelligence in the financial sector, and how professional auditors scrutinize digital ventures in public-sector audits.

Journal articles

Engström, J., Öhman, P. och Tagesson, T. (2026)
Scrutinising the scrutiny: How do professional auditors approach digital ventures in municipal value for money audits?
Financial Accountability & Management, Vol. 42, No 1, pp. 90-102.
Kontakt: peter.ohman@miun.se

Svanberg, J., Öhman, P., Samsten, I., Neidermeyer, P., Rana, T. och Danielson, M. (2025)
Addressing the exception prioritization problem in continuous auditing systems with thresholding,
Intelligent Systems in Accounting, Finance and Management, Vol. 32, No. 4, e70022.
Kontakt: peter.ohman@miun.se

Hedback, D. (2025)
Identifying going concern audit opinions using supervised machine learning,
Intelligent Systems in Accounting, Finance and Management, Vol. 32 No. 4, e70020.
Kontakt: dennis.hedback@miun.se

Rahman, Md. J., Rana, T., Xu, Y. och Öhman, P. (2024)
Bridging BI and AI enhancing operational efficiency in the Chinese financial sector,
Journal of Global Information Management, Vol. 32, No.1, pp. 1-27.
Kontakt: peter.ohman@miun.se

Maiga, A.S., Nilsson, A. och Jacobs, F.A. (2013)
Extent of managerial IT use, learning routines, and firm performance: A structural equation modeling of their relationship,
International Journal of Accounting Information Systems, Vol. 14, No. 4, pp. 297-320.
Kontakt: peter.ohman@miun.se

Maiga, A.S. och Jacobs, F.A. (2011)
Selection bias and endogeneity issues on the relationship between information technology and firm performance,
Advances in Management Accounting, Vol. 19, pp. 109-131.
Kontakt: peter.ohman@miun.se

Books and book chapters

Predictive machine learning in assessing materiality: The global reporting initiative standard and beyond,
In Walker, T., Wendt, S., Goubran, S. och Schwartz, T. (eds.), Artificial intelligence for sustainability innovations in business and financial services,
Cham: Palgrave MacMillan, pp. 105-131.

Kronblad, C., Rahnert, K. och Öhman, P. (2023)
Revision i en digital kontext,
I Rahnert, K. och Öhman, P. (red.), Revision – i går, i dag, i morgon,
Stockholm: Ekerlids Förlag, sid. 354-376.

Rana, T., Svanberg, J., Öhman, P. och Lowe, A. (eds.), (2023)
Handbook of big data and analytics in accounting and auditing,
Singapore: Springer.

Rana, T., Svanberg, J., Öhman, P. och Lowe, A. (2023)
Introduction,
In Rana, T., Svanberg, J., Öhman, P. och Lowe, A. (eds.), Handbook of big data and analytics in accounting and auditing,
Singapore: Springer, pp. 1-13.

Svanberg, J., Ardeshiri , T., Samsten, I., Öhman, P. och Neidermeyer, P. (2023)
Prediction of controversies and estimation of ESG performance: An experimental investigation using machine learning,
In Rana, T., Svanberg, J., Öhman, P. och Lowe, A. (eds.), Handbook of big data and analytics in accounting and auditing,
Singapore: Springer, pp. 65-88.

Debate articles

Stockenstrand, A-K. och Henriksson, H. (2018)
Vad är bra redovisning i en digital och global tid?, Organisation & Samhälle, Årgång 5, Nr 2, sid. 10-16.