A leading airport solution provider aimed to improve the accuracy of their risk profiling by predicting valid hits (rule-in hits) using historical traveler patterns and profiles. Coforge implemented a machine learning-based approach, leveraging R and Python for modeling, which significantly reduced false positives and enhanced hit accuracy.