Machine learning prediction of petty corruption intention among law enforcement officers

Abstract

Law enforcement agencies face a widespread problem of corruption, which jeopardizes their credibility and institutional integrity. Thus, the primary goal of this study is to develop a machine learning prediction model for petty corruption intentions as an early warning system for law enforcement officials who fail to perform their duties and obligations with integrity. Using a questionnaire survey of two hundred twenty-five participants, from senior officers to rank and file police officers, this study presents the fundamental knowledge on the design and implementation of machine learning model based on six selected algorithms; generalized linear model, fast last margin, decision tree, random forest, gradient boosted trees, and support vector machine. In addition to demographic factors, the efficacy of each machine learning algorithm on petty corruption was evaluated using general strain theory (GST) attributes: financial stress, work stress, leadership pressure, and peer pressure. The findings indicated that peer pressure has given the highest weight of contributions to most of the machine learning algorithms. The most outperformed machine learning in terms of the classification accuracy is gradient boosted trees with accuracy above 90%. This paper presents useful knowledge to enhance the realization of implementing intelligent corruption detection tools.