Abstract
Occupational fraud is a widespread fraud in the Nigerian construction sector which takes the form of misappropriation of assets, overbilling, fraud of procurements and financial records falsification. This paper focuses on the implications of applying forensic accounting methods namely; data mining, fraud risk measurement, and financial statement analysis on occupational fraud in construction selective companies in Nigeria. The descriptive survey research approach was taken with the selected companies being listed on the Nigerian Exchange Group as the target. Purposive sampling was applied to select two companies Julius Berger Nigeria Plc founded in 1970 and Ronchess Global Resources incorporated 2021 due to their size of operations, track records, and the differences in the length of existence. A structured questionnaire was employed in gathering data on 137 respondents who were project managers, financial officers, accountants and internal auditors. The data was analysed using descriptive statistics, Pearson correlation, and multiple regression analysis. The results showed that all the dimensions of occupational fraud have strong statistically significant negative relationships with the forensic accounting techniques. Data mining became the most appropriate method especially in identifying anomalous trends and overcharging. The regression models were found to be good predictors, and the adjustment R-squared values are between 0.549 and 0.596, which showed that the predictive ability of the forensic accounting methods is significant, and it predicts fraudulent behaviours reduction. The research findings are that forensic accounting methods are essential to prevent and detect fraud in Nigerian construction sector. It suggests further internalization of these methods into internal control systems, the investing in the data analytics infrastructure, and ongoing capacity building of accounting and audit practitioners.
Keywords: Data mining, Forensic accounting, Fraud risk assessment, Financial statement analysis, Occupational fraud.