A Generative AI-Driven Financial Shared Service Center Model: A Task-Technology Fit Perspective

DOI

10.2991/978-94-6239-652-4_27How to use a DOI?

Keywords

Generative artificial intelligence; Financial shared service centers; Task-technology fit; Financial reporting; Intelligent finance

Abstract

Financial Shared Service Centers (FSSCs) play a central role in standardizing and centralizing corporate financial activities. However, traditional FSSCs continue to face persistent limitations in high-frequency and data-intensive environments, including delayed decision-making, fragmented information processing, and inconsistent reporting quality. This study examines how generative artificial intelligence (AI) can enhance FSSC effectiveness by improving the alignment between financial tasks and technological capabilities. Grounded in Task-Technology Fit (TTF) theory, the integration of generative AI is analyzed across three stages of financial work: task input, task processing, and task output. The analysis shows that generative AI exhibits a strong fit with FSSC tasks involving structured data ingestion, rule-based transaction processing, standardized financial reporting, and predictive analysis. This alignment enhances reporting efficiency, consistency, and audit traceability, thereby facilitating the transformation of FSSCs from transaction-oriented processing units into intelligent financial governance platforms. Key implementation challenges are also examined, including limited algorithmic interpretability, data quality risks, privacy concerns, model hallucination, and accountability ambiguities. The study outlines implications for the design and governance of AI-enabled financial reporting systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Min Liu
AU  - Yijie Huang
AU  - Chuyue Shi
AU  - Ming Chen
PY  - 2026
DA  - 2026/04/19
TI  - A Generative AI-Driven Financial Shared Service Center Model: A Task-Technology Fit Perspective
BT  - Proceedings of the  2026 5th International Conference on Engineering Management and Information Science (EMIS 2026)
PB  - Atlantis Press
SP  - 278
EP  - 287
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-652-4_27
DO  - 10.2991/978-94-6239-652-4_27
ID  - Liu2026
ER  -