AI-Augmented Financial Ratio Analysis for Early Warning, Monitoring, and Assurance

Financial ratio analysis has long been a method for assessing organizational health, but it faces challenges like reporting delays, limited use of unstructured data, and manual interpretation. This paper reviews how artificial intelligence (AI)-including machine learning (ML), natural language processing (NLP), and automated analytics-advances ratio-based assessment for early warning, monitoring, and assurance. We compile recent evidence on (i) automating ratio calculation and validation, (ii) predicting defaults or fraud using ratio time series and external signals, (iii) enhancing analysis with textual disclosures and news, and (iv) implementing governance mechanisms such as explainability and algorithm auditing. Based on these insights, we propose an AI-Augmented Financial Ratio Analysis (AFRA) framework with six modules: secure data collection, ratio and temporal feature engineering, multimodal enrichment from text and news, predictive and anomaly detection models, explainability and auditability, and privacy-preserving deployment. We align AFRA with evaluation metrics (AUROC, calibration, Mean Absolute Error/Mean Absolute Percentage Error (MAE/MAPE), precision) and provide an implementation roadmap for information systems teams working within confidentiality and compliance constraints. The paper concludes with a research agenda focusing on drift-resistant models, causal explanations, and standardized algorithm audits for finance analytics.