Neurofinancial Artificial Intelligence Model for Early Detection of Corruptive Behavior in Educational Environments

Abstract

Corruption remains a serious problem that weakens trust, damages institutions, and slows down sustainable progress. This issue does not begin only in politics or organizations, but often takes root during the early years of education. Small acts such as cheating, manipulating data, or misusing small responsibilities can gradually develop into deeply rooted corrupt practices later in life. This study introduces a new model that uses artificial intelligence combined with insights from human behavior and financial decision-making to detect early signs of corruption in schools. The research applies a mixed approach, collecting data from surveys on financial habits, learning abilities, and self-control from both students and teachers. The information is then analyzed using advanced computer-based methods to identify patterns that may indicate risky behavior. The goal is to create a predictive system that can provide early warnings and offer schools practical guidance for designing more effective anti-corruption education. The study contributes to building integrity, promoting ethical values, and supporting sustainable development by preventing corruption before it takes root.