(untitled) (Journal of Global Accounting, 2024)
This study ascertained the relationship between forensic accounting techniques and fraud management of commercial banks in Awka-South Anambra State, Nigeria.The specific objective of the study was to examine the extent to which data mining technique, computer assisted auditing technique, ratio analysis technique and public documents review technique relate with artificial intelligence biometrics of listed commercial banks in Awka-South Anambra State. Descriptive survey research design. The population of this study consisted of seven hundred and fifty eighty (758) staff of the thirteen (13) commercial banks in Awka South, Anambra State Nigeria from which a sample size of 262 was selected. Primary data for the study were collected from the respondents using structured questionnaire. The tools for descriptive analysis were percentage analysis, frequency distribution and mean. Hypotheses were tested using regression analysis. The finding of the study showed that: there is a significant positive relationship between data mining techniques and artificial intelligence biometrics for fraud management in listed commercial banks (β = 0.086, p = 0.000); computer-assisted audit techniques significantly and positively relate with artificial intelligence biometrics for fraud management (β = 0.204, p = 0.002); ratio analysis techniques have a significant and positive relationship with artificial intelligence biometrics for fraud management (β = 0.148, p = 0.033); public documents review techniques have a significant and positive relationship with artificial intelligence biometrics for fraud management (β = 0.316, p = 0.000). In conclusion, integrating advanced techniques into fraud management systems significantly enhances the effectiveness of AI biometrics. The study recommends that the Information Technology and data analytics teams of listed commercial banks in Awka-South should enhance their data mining capabilities to better integrate with AI biometrics systems, thereby improving fraud detection effectiveness.