Exploring Financial Literacy's Impact on Preventing Economic Crimes: A Random Forest Analysis Naveen Pol, Anand Guled, T. Manikumar, R Suyam Praba, E. K. Arulkarthick, et al. Proceedings of International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI Icscai 2024, 2024 Financial crime poses a substantial menace to individuals, businesses, and economies on a global scale. In spite of attempts to hinder such unlawful acts, the intricate characteristics of deceitful activities require inventive methods for identification and reduction. This study aims to investigate the correlation between financial literacy and the prevention of economic crime by utilising sophisticated machine learning techniques, notably Random Forest Analysis. The approach we present combines data collection, preprocessing, feature selection, model construction, and Random Forest Analysis to forecast economic crime using financial literacy levels. In contrast to previous studies, our methodology provides several benefits, such as thorough feature selection, resilient model training, and exceptional predicted accuracy. The assessment of the suggested system reveals outstanding outcomes, with an accuracy of 0.94, precision of 0.92, recall of 0.90, F1-score of 0.95, and AUC of 0.92. These findings emphasise the efficacy of utilising financial literacy to reduce the dangers of economic crime and showcase the promise of modern machine learning approaches in tackling intricate social issues.
