Crime Risk Intelligence and Forecasting (CRIF): A Comprehensive Machine Learning and Deep Learning Framework for Proactive Policing

Crime prevention is a significant issue for law enforcement agencies around the world. Traditional policing methods are reactive. They focus on investigating crimes after they happen instead of predicting them. This paper presents Crime Risk Intelligence and Forecasting (CRIF), a hybrid AI framework that combines Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) to forecast possible crime events in specific areas. CRIF uses data from multiple sources, including historical crime records, demographics, social media activity, weather conditions, and local events. The framework applies Random Forest for structured data, ConvLSTM for spatio-temporal modeling, and BERT for social media analysis. The outputs of the models are combined into a Crime Risk Index (CRI), which classifies areas as Low, Medium, or High risk. A Python-based prototype that uses synthetic datasets and a Streamlit web application showcases real-time, interactive predictions. Experimental results indicate high predictive accuracy, clear risk levels, and a strong potential for proactive policing. Future efforts will focus on real-world deployment with IoT surveillance, live social media feeds, and geospatial visualization for smart cities.