DATA

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.

One of the most significant threat to petroleum production is that of sand production resulting from the migration of formation sand caused by the flow of reservoir fluids. Conventional well Completions in soft formations commonly produce formation sands or fines with fluids. Today’s operators need access to complete production system, delivering intelligent real time information and operation alongside existing instrumentation. Hence, Acoustic Sand Monitors and Intrusive Erosion Probes are invaluable tools in detecting the presence and effect of solids production. Accurate monitoring coupled with analysis and interpretation of the real time data can guarantee improved longevity of the asset and greatly reduce cost JK of repairs, replacement and downtime. Detailed, real-time information can also help to optimise production, and in many cases adjustments can be made to individual wells to increase oil production where sand is not an issue. This study presents an analytical assessment of sand production monitoring from an offshore field in the Niger Delta region from real time oil field production data. The data used in this study have been obtained from sand signals generated from acoustic sound detectors installed along flow paths in the oil production facility. In this study, sound signals generated by solids particles (sands/fines) along the flow paths of the facility are analysed to monitor sand production from the wells in the field over a period of nine years of production. These wells were consequently categorized as high sand producers or low sand producers following the percentage deviation of the sand signal averages for the wells from the corresponding baselines for each of the wells in comparison to the established percentage deviation threshold. At the end of this study, four wells were categorized to be high sand producers while nine were categorized as low sand producers.