Effect of Data Mining, Computer-assisted Auditing Software (CAAS) and the Use of Anonymous Communication on Financial Fraud Detection in Local Government Council of Oyo State, Nigeria
Description
This study investigated the effect of data mining and computer-assisted auditing software (CAAS) and the use of anonymous communication on financial fraud detection in local government council of Oyo State. The research adopted a descriptive research methodology to gather opinions from Local Government staff. The study populations comprised of all the Local Government in Oyo State. The study used structured questionnaire and key informant interviews was conducted in all the Local Government in Oyo State. The responses were analyzed using descriptive and inferential methods. Findings revealed a positive perception of the impact of data mining tools on financial fraud detection in the local government council and also generally positive perception of the effectiveness of CAAS in detecting and uncovering financial fraud in the local government council. In addition, it revealed a mixed perception among local government employees regarding the effectiveness of anonymous communication as a whistle blowing tool for detecting financial fraud. Based on the above findings, this study concluded that the utilization of data mining and CAAS significantly contributes to the detection of financial fraud within the local government council and also promoting a culture of anonymous reporting and communication channels can empower individuals to report suspicious activities without fear of reprisal, thus facilitating the identification and prevention of financial fraud. The study therefore recommended that the local government council should consider and prioritize the adoption and utilization of data mining techniques, computer-assisted auditing software (CAAS) and also encourage the use of anonymous communication channels which can foster a culture of reporting and whistleblowing.
Files
ISRGJEBM742024 re.pdf
Files
(710.5 kB)
Name | Size | Download all |
---|---|---|
md5:fce2db4ea2aadb913d347737d59922f8
|
710.5 kB | Preview Download |