A Risk-Based Input–Process–Output Framework for Financial Statement Fraud Detection: Integrating Financial Red Flags, Governance Controls and Forensic Analytics

Financial statement fraud can impair investor confidence, distort capital allocation and weaken the credibility of corporate reporting. Existing detection approaches use financial-ratio screens, fraud-risk theories, governance indicators and increasingly sophisticated analytical models, but these approaches are often applied separately. This paper develops a risk-based Input–Process–Output (IPO) framework that connects observable financial and governance signals to a structured review process and measurable investigation outcomes. The proposed framework treats financial pressure, unusual accounting movements, control weaknesses and governance signals as inputs; analytical screening, corroboration, professional scepticism and escalation as processes; and prioritised risk alerts, documented evidence and improved monitoring as outputs. A transparent illustrative logistic model is specified to demonstrate how risk indicators could be combined without presenting simulated values as empirical evidence. The paper synthesises established work on earnings manipulation, fraud-risk assessment and fraud-detection model design. It argues that ratio-based scores should be used as screening indicators rather than proof of fraud, and that model performance must be validated against independently established outcomes, class imbalance and sector-specific conditions. The contribution is a practical, auditable framework for connecting detection signals to investigation decisions while preserving human oversight. The paper is conceptual and does not claim original empirical validation.