Relationship between Data Mining and Audit Quality: Evidence from Big 4 Audit Firms in Nigeria

Abstract

This study was necessitated by the need to eliminate and minimize sampling error and fraud, to improve audit quality in the public sector of Nigeria. Consequently, a descriptive survey design was adopted to investigate the relationship between data mining and audit quality. A sample of 220 practicing auditors were drawn from the Big4 audit firms operating in Nigeria, in line with Krejcie & Morgan’s Sample Size Calculator of 1970. The reliability of the research instrument (questionnaire) was determined by Conbrach Alpha test, and questionnaire responses were coded, converted and analysed with Karl Pearson’s Moment Correlation at 0.05level of significance. The results revealed that text and web mining can effectively discover trends, patterns, errors, misrepresentations and fraud in accounting records and reports. This study therefore, concludes that data mining and audit quality are positively related, and that data mining can improve audit quality and reduce the age long audit expectation gap. On this premise, this study recommends that contemporary auditors should acquaint themselves with data mining skills, and audit firms and shareholders should apply and insist on the use of data mining in external audit.