In What Way Does Artificial Intelligence Influences Audit Practice? Empirical Evidence from Southwest, Nigeria (Published)
AI has gained significant traction as an innovative tool for automating tasks, enhancing data analytics, and reducing the risk of errors in auditing processes. This study investigated the impact of adopting artificial intelligence (AI) on the quality of audit practice in Nigeria, focusing on data mining, machine learning, and image recognition as proxies for the independent variable. Population was 251 accounting firms in southwest Nigeria, with a sample size of 159, purposively determined. The study utilized structured questionnaires for data collection, with regression analysis, and correlation matrices adopted for the analysis. The findings revealed a significant positive relationship between data mining and image recognition with the quality of audit practice in Nigeria. Machine learning, however, showed an insignificant negative relationship. This suggests that AI, particularly data mining and image recognition, can enhance audit quality in Nigeria. As a result, the study recommended that Nigerian audit professionals and firms should consider incorporating data mining techniques into their audit processes to improve effectiveness and error detection.
Keywords: Artificial Intelligence, Data mining, Quality of audit practice., image recognition, machine learning
Data Mining Technology and Its Role in Discovering Financial Fraud (Published)
The basis of any business – the customer database, which provides information about the client relationship with the company. The increasing complexity of organizational processes and rapidly changing business environment led to strong growth in domestic corporate data companies. In this regard, the increasing interest from the point of view of fraud risk assessments are beginning to provide tools such as data mining (Forensic Data Analytics – FDA), which allows you to narrow sample of suspicious transactions while minimizing the volume of checks. For example, in the field of communication in the database stores information about the conclusion of agreements for the use of services, the time of termination of the contract, a region rate, etc. The analysis revealed 7 out of 31 dentists who deliberately overstate the value of work performed by the insurance.
K-means algorithm using the algorithm of k-means as 4 clusters formed:
- Cluster 1: specialized work using expensive additional procedures, the average age of the client – 25, the average cost of services – $ 715;
- Cluster 2: minor works without the use of additional procedures, the average age of the client – 21, the average cost of services – $ 286;
- Cluster 3: Significant work using expensive additional procedures, the average age of the client – 38, the average cost of services – $ 819;
- Cluster 4: Significant work with cheap additional procedures, the average age of the client – 27, the average cost of services – $ 551.
Keywords: Cluster Algorithms, Data Miner., Data mining, Financial Fraud, K-Means Algorithm