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Integrating AI and Process Reengineering in Financial Shared Services

Yufei Chen et al.

International Journal of Information Systems in the Service Sector2026https://doi.org/10.4018/ijisss.396822article
AJG 1
Weight
0.50

What the paper says

Under digital transformation, Financial Shared Service Centers (FSSCs) are evolving from administrative units to strategic enablers. Drawing on case studies from manufacturing and internet firms (2015–2022), this paper identifies three key limitations: process inflexibility in complex scenarios, disconnected financial-business data flows, and delayed risk response. To address these, the authors propose a framework—“hierarchical processes, intelligent tools, and closed-loop risk management.” It automates routine tasks via RPA and OCR, reserves expert teams for complex cases, and embeds machine learning and big data analytics into workflows for real-time monitoring. Results show nearly 50% reduction in processing time, 70% fewer annual risk incidents, and over 25% higher cost savings. When processes and technologies are stably integrated, FSSCs enhance not only efficiency but also strategic decision support. This study offers a practical pathway for digital transformation in enterprise service systems, highlighting how information systems align operational automation with strategic agility.

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https://doi.org/https://doi.org/10.4018/ijisss.396822

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@article{yufei2026,
  title        = {{Integrating AI and Process Reengineering in Financial Shared Services}},
  author       = {Yufei Chen et al.},
  journal      = {International Journal of Information Systems in the Service Sector},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisss.396822},
}

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Integrating AI and Process Reengineering in Financial Shared Services

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.