Artificial Intelligence in Supply Chain Financial Planning: Applications, Governance, and Decision Accountability, a Review of Forecasting, Cash Planning, Financing Decisions, and Human Oversight
Keywords:
artificial intelligence; supply chain finance; demand forecasting; cash flow forecasting; working capital; model risk management; AI governance; human oversight; decision accountabilityAbstract
Artificial intelligence (AI) is moving from operational supply chain tasks, such as routing and warehouse automation, into the financial planning layer that links physical flows to cash, working capital, and funding. This review synthesises research and practice published up to 2024 on four connected domains: demand and cost forecasting, cash and working capital planning, financing decisions (including supply chain finance and credit assessment), and the human oversight arrangements that govern AI-supported decisions. The evidence shows that machine learning methods can improve forecast accuracy where data are rich and hierarchical, but gains are uneven and depend heavily on data quality, horizon, and the presence of structural breaks. In cash planning and financing, AI is most valuable when it is coupled with optimisation and scenario analysis rather than used as a stand-alone predictor. The review argues that the binding constraint on value is increasingly governance rather than algorithmic capability. Drawing on established model risk practice, AI governance standards, and emerging regulation, it proposes a decision accountability framework that assigns decision rights according to financial materiality and reversibility, specifies documentation and explainability requirements, and defines escalation paths for human override. The paper closes with a research agenda covering prescriptive analytics under uncertainty, measurement of human and AI complementarity, and the auditability of generative AI in financial planning workflows.
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