International Journal of Advanced Multidisciplinary Research and Studies
Volume 4, Issue 5, 2024
Artificial Intelligent (AI) Abilities in Detecting and Preventing Financial Fraud in Federal Neuropsychiatric Hospital Enugu
Author(s): Onyemachi Chinedu Okechukwu, Nnadi Hillary Sunday, Ugwu Patricia Ogechi
Abstract:
The study examined influence of Artificial Intelligent (AI) abilities in detecting and preventing financial fraud in Federal Neuropsychiatric Hospital Enugu. Specifically, the study sought to: Examine the influence of webcam video camera on theft identification in Federal Neuropsychiatric Hospital Enugu and ascertain the influence of electronic governance on fraud detection in Federal Neuropsychiatric Hospital Enugu. The descriptive survey method was the research design. The sample size of 125 respondents was taken from 202 staff of Federal Neuropsychiatric Hospital Enugu. Research questions of the study were answered using mean score and standard deviation. The hypotheses stated were tested using single regression analysis. The empirical result shows that webcam video camera has significant influence on theft identification in Federal Neuropsychiatric Hospital Enugu (t-statistic =3.692; P-value (0.000) < Sig-value (0.05) and electronic governance has significant influence on fraud detection in Federal Neuropsychiatric Hospital Enugu (t-statistic = 5.748; P-value (0.000) < Sig-value (0.05). The study concludes that there is positive and significant influence of Artificial Intelligent (AI) abilities in detecting and preventing financial fraud in Federal Neuropsychiatric Hospital Enugu. The study recommended that Artificial Intelligent (AI) should be implemented in every Public Service at the Federal, state and local levels. The use of BVN along with e-administration should be mandatorily encouraged. Also, the use of multiple biometric recognition should be introduced to effectively curb the issue of ghost workers.
Keywords: Artificial Intelligent (AI), Webcam Video Camera, Theft Identification, Electronic Governance and Fraud Detection
Pages: 953-960
Download Full Article: Click Here