Reflecting on Artificial Intelligence and Financial Statement Analysis Using a Critical Management Framework Approach

Artificial Intelligence (AI) is increasingly being applied to financial statement analysis, revolutionising the way financial data is processed, analysed, and interpreted. This integration of AI in financial analysis offers numerous benefits, including increased efficiency, improved accuracy, and the ability to handle large volumes of data. Some of the key applications one finds of AI in financial analysis include automated data extraction, anomaly detection, predictive analytics, natural language processing (NLP), and trend analysis. Using AI in financial statement analysis has shown to provide real benefits to users including efficiency, accuracy, pattern recognition, and consistency. It however also shown that its use does have certain challenges which needs consideration for its users. These include data quality, interpretability, regulatory compliance, human oversight, and ethical considerations. Applying a critical management framework (see, for example, Alvesson and Deetz, 2000, Doing critical management research. SAGE Publications Ltd.), as proposed, to AI and financial statement analysis provides an insightful perspective on this emerging field. This approach allows one to examine the underlying assumptions, power dynamics, and potential societal impacts of using AI in financial analysis. Aspects that may be investigated are: (i) power dynamics and control (How does the use of AI shift power dynamics between auditors, management, investors, and regulators?); (ii) ideology critique (Does the push for AI in financial analysis reinforce a technocratic ideology in accounting?); (iii) social justice and equity (How might AI-driven financial analysis affect employment in the accounting sector?); (iv) historical context (How does the introduction of AI in financial statement analysis compare to previous technological shifts in accounting?); (v) alternative perspectives (What non-AI approaches to improving financial statement analysis are being overlooked?) (vi) reflexivity (How does one’s own biases and assumptions about technology influence one’s view of AI in financial analysis?); (vii) emancipation and democratisation (How might AI be used to empower stakeholders traditionally marginalised in financial reporting?); (viii) ethical considerations (How can we ensure responsible development and use of AI in this context?); and (ix) environmental impact (How might AI in financial analysis affect reporting and action on environmental issues?).