Artificial Intelligence and Audit Quality of Deposit Money Banks in Nigeria

The incorporation of Artificial Intelligence (AI) in the audit process, particularly in the banking sector, has been recognized as a potential innovation to enhance audit quality. However, while AI technologies such as Machine Learning (ML), Natural Language Processing (NLP), and data analytics hold potential to improve accuracy and efficiency, there is a significant gap in understanding how these technologies specifically affect audit quality in banks. Therefore, this study was carried out to examine the influence of artificial intelligence on audit quality of deposit money banks in Nigeria. The specific objectives were to evaluate the influence of machine learning, natural language processing and data analytics on audit quality of deposit money banks in south-south, Nigeria. Survey research was adopted for the study. The population for the study consisted of 8095 employees and sample size was 385 respondents which were determined using Taro Yamane formula for sample size determination. Data were collected using questionnaire which was distributed to the staff of the five selected deposit money banks used for the study out of which 367 copies of questionnaire were filled and returned. Data collected were analyzed using mean, standard deviation and multiple regression analyses. Findings indicated that machine learning, natural language processing and data analytics have significant influence on audit quality of deposit money banks in south-south, Nigeria (Adjusted R2 -.845, F-value 663.633, Durbin Watson-1.907, Beta-.169, 1.079, .259 and P-.000 (P<0.05)). It was concluded that machine learning, natural language processing and data analytics significantly and positively influence audit quality of deposit money banks in south-south, Nigeria. Therefore, it was recommended that deposit money banks should invest in training their audit professionals to effectively utilize machine learning. Banks should prioritize the integration of advanced AI tools, particularly those for NLP and data into their auditing processes. Banks should focus on improving data quality and ensuring robust data security protocols.