TECHNOLOGY DRIVEN THEFT MANAGEMENT: EVALUATING THE EFFECTIVENESS OF AI AND MACHINE LEARNING ON IDENTIFYING AND PREVENTING THEFT

Abstract

This study investigates the impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on theft identification and prevention in Nigeria. Given the increasing concern over theft in various sectors, the research aims to assess whether AI and ML can significantly enhance the detection and prevention of theft. A sample of 400 respondents was surveyed using a structured Likert-scale questionnaire to gather data on the effectiveness of AI and ML in identifying theft patterns, predicting theft, analyzing large datasets, detecting unusual behaviors, and their cost-effectiveness. A multiple linear regression model was employed to analyze the relationship between the adoption of AI/ML technologies and theft prevention. The findings reveal that AI and ML technologies have a significant positive impact on theft identification and prevention in Nigeria. Specifically, AI's ability to identify theft patterns, predict potential theft, and analyze large datasets significantly enhances theft detection. Additionally, Machine Learning's capacity to detect unusual behaviors further strengthens the prevention mechanisms. While the cost-effectiveness of these technologies is perceived as a challenge, its influence on theft prevention remains significant, albeit with a smaller effect size. The study concludes that AI and ML technologies can be powerful tools in combating theft, offering promising solutions for the Nigerian context. The results underscore the importance of integrating AI and ML in theft prevention strategies and highlight the need for continued investment in these technologies to ensure their effectiveness, particularly in a developing country like Nigeria.