Abstract

The initiative (Unmasking Corruption and Bribery Using Predictive Analytics) aims to identify employees in government sectors who commit corruption and bribery by addressing issues such as the lack of transparency, fairness, equality, and public trust, as well as weakened loyalty and poor reputation. This project focuses on data analytics and predictive modeling to detect suspicious or high-risk employee behaviors related to corruption and bribery. The main objective is to reduce these unethical activities in government sectors, strengthen integrity, and promote transparency and public trust through predictive capabilities. It also seeks to raise awareness about how serious and harmful these crimes are. Since such activities are often committed in secret, it is crucial to monitor employees carefully. Data analysis and prediction using machine learning can greatly simplify the process of identifying individuals involved in these crimes. This strategy aims to eliminate such behaviors, promote honesty, and improve public trust, which will positively affect the state. It emphasizes the importance of ethical values among employees in all government sectors and how they can create a more positive and fair work environment. The predictive model findings indicate that the XGBoost model (46) outperformed other machine learning methods in detecting suspicious employee behavior, achieving the highest recall of (94%) (27). The model accurately identified strong indicators of corruption and bribery risks, such as accepting bribes or showing unusual approval patterns, as atypical and anomalous behaviors. These results highlight the success of advanced machine learning techniques in revealing hidden risks that might remain unnoticed by traditional moni- toring systems. The research concludes that predictive analytics can be an effective approach for monitoring public officials and detecting misconduct. Implementing these strategies en- ables government agencies to reduce corruption allegations, restore public trust, and promote transparency and fairness. Moreover, the findings demonstrate how integrating data-driven methods into decision-making supports ethical governance and strengthens accountability within public institutions. Keywords: Corruption, Bribery, techniques, lack of transparency, fairness, public trust, government sectors, employees, machine learning, random Forest, XGboost, logistic regrassion, neural network, LSVM, correlation, chi-square, Mann-withney, boxplot, mahalanobis.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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