An integrated method for detecting financial statement fraud

Abstract

Corporate transparency, investor trust, and the integrity of capital markets are all jeopardised by financial statement fraud, which calls for better detection methods. By merging narrative disclosure-based behavioural signals with standardised financial indicators and governance monitoring factors, this study suggests an integrated strategy for identifying financial statement fraud. The analysis begins by standardising all variables using z-scores to guarantee comparability across scales. The variables in the dataset are company-year observations that have been labelled as High Risk or Low Risk based on composite scoring. There is proper normalisation of the financial and governance variables, according to descriptive statistics; nonetheless, early comparisons show that High Risk observations show larger financial pressure signals and lower governance monitoring. Financial pressure has a modest association with governance opportunity and narrative behaviour indicators, and a significant association with financial pressure, according to correlation analysis, demonstrating that fraud risk is multifaceted. Highlighting accruals as a key predictor of high-risk categorisation, interpretable insights are provided by logistic regression utilising just underlying z-score variables. Stratified training and holdout testing are used to incorporate several machine learning models, such as SVM, Random Forest, and Decision Tree, in order to further increase prediction performance. Including designed sub-scores Financial Pressure Score (FPS), Governance Opportunity Score (GOS), and Narrative Behaviour Score (NBS) with the z-score indications gives SVM and Random Forest the best accuracy, according to the results. The results show that auditors, regulators, and forensic practitioners may greatly benefit from an integrated, multi-layered approach to fraud detection since it increases accuracy and dependability.