SECTOR

This study examines the effect of forensic data analytics on audit quality in the Nigerian banking sector. It investigates the relationship between data collection methods, data cleaning, data mining and detection techniques, and predictive analytics, and their collective impact on audit quality. The study was motivated by the growing complexity of financial transactions and the need for auditors to employ advanced technological tools to enhance accuracy, fraud detection, and transparency in financial reporting. An ex post facto and survey research design was adopted. Data were obtained from 384 respondents across selected deposit money banks in Edo State, Nigeria. Descriptive statistics, correlation, and multiple regression analysis were used to analyze the data and test the hypotheses. The results revealed that all components of forensic data analytics—data collection methods, data cleaning, data mining and detection techniques, and predictive analytics—had significant and positive effects on audit quality. The regression model showed that these variables collectively explained 76.1% of the variation in audit quality, indicating a strong relationship between forensic data analytics and improved audit performance. The study concludes that forensic data analytics significantly enhances audit quality by improving fraud detection, predictive risk assessment, and the reliability of financial reporting. It recommends that banks integrate forensic data analytics tools into their audit systems, invest in robust data infrastructure, and provide continuous training for auditors to build capacity in analytics-driven auditing. The study contributes to knowledge by empirically validating the influence of forensic data analytics on audit quality within the context of Nigerian banks and extending Institutional Theory by demonstrating how regulatory pressures and technological advancements drive the adoption of forensic analytics in auditing.