Artificial Intelligence as a Tool for Reducing Errors in Financial Budget Preparation

Abstract

Financial budget preparation remains vulnerable to errors arising from data extraction and mapping, spreadsheet manipulation, forecast assumptions, version control, judgemental adjustment and weak reconciliation. Artificial intelligence (AI) is increasingly proposed as a means of reducing these errors, yet the evidence base is fragmented across management accounting, accounting information systems, forecasting, robotic process automation, machine learning, anomaly detection and human-AI decision research. This critical narrative review evaluates how far current evidence supports AI-enabled error reduction in organisational budgeting and where new sources of error emerge. Literature published principally from 2015 to 16 June 2026 was identified through multidisciplinary and business-focused scholarly indexes, supplemented by citation searching and verification against authoritative bibliographic records. The synthesis distinguishes deterministic automation from predictive machine learning, anomaly detection and generative AI because these technologies address different failure modes and carry different assurance requirements. Evidence is strongest for reducing repetitive transfer and processing errors, improving selected accounting estimates and forecasts, and widening exception screening. Direct causal evidence that AI improves end-to-end corporate budget accuracy remains limited, while field evidence increasingly shows that benefits depend on data quality, process standardisation, confidence-aware human intervention and effective internal control. Important countervailing risks include data leakage, model drift, false precision, brittle automation, automation bias and unreliable numerical reasoning by large language models. The review therefore argues that AI should be treated as a layered control and decision-support architecture rather than an autonomous budget preparer. The most defensible design combines governed source data, deterministic calculations, validated forecasting models, exception detection, logged human overrides and continuous performance monitoring. Future research should test these arrangements in real budgeting cycles using common error taxonomies and outcome measures that capture accuracy, rework, reconciliation failures, uncertainty and control effectiveness.