The Effect of Artificial Intelligence on the Detection of Financial Anomalies of Auditing Firms in Nigeria

Abstract

This study examined the effect of artificial intelligence on the detection of financial anomalies in auditing firms in Nigeria. Specifically, the study investigated the influence of machine learning algorithms, deep learning techniques, and natural language processing on the detection of financial anomalies. The study was motivated by the increasing complexity of financial transactions, the growing need for audit accuracy, and the limitations of traditional auditing methods in identifying fraud and irregularities. A descriptive survey research design was adopted, and data were collected through structured questionnaires administered to selected auditing firms in Nigeria. The data were analyzed using descriptive statistics, Pearson Product Moment Correlation, multiple regression analysis, and ANOVA with the aid of SPSS version 23. Findings revealed that machine learning algorithms, deep learning techniques, and natural language processing have positive and significant effects on the detection of financial anomalies in auditing firms in Nigeria. The study further established that artificial intelligence enhances audit efficiency, improves fraud detection capabilities, and supports more accurate financial reporting. The study concludes that the adoption of artificial intelligence technologies significantly strengthens auditing practices and improves the ability of auditors to detect financial irregularities. The study therefore recommends that auditing firms should invest in artificial intelligence tools, continuous professional training, supportive technological infrastructure, and strategic collaborations to enhance audit quality and maintain global best practices in the Nigerian auditing sector.