A High-Recall Cost-Sensitive Machine Learning Framework for Real-Time Online Banking Transaction Fraud Detection

Fraudulent activities on digital banking services are becoming more intricate by the day, challenging existing defenses. While older rule-driven methods struggle to keep pace, even precision-focused algorithms fall short when new scams are introduced. These tools typically overlook subtle shifts in criminal behavior, missing crucial signals. Because silent breaches cost institutions far more than flagged but legitimate actions, catching every possible case is crucial. High sensitivity to actual threats becomes essential when oversight leads to heavy losses. One key aim here involves reducing missed fraud cases without spiking incorrect alerts too much. This study builds a system using group learning methods adjusted through smart threshold choices. Using real-world transaction records shared openly, where cheating acts rarely appear among normal activities, tests are run under practical skewed distributions. The outcomes revealed that approximately 91% of actual fraud was caught, beating standard setups that rely on unchanging rules when dealing with uneven examples across classes. When tested in live settings, the fraud detection system connects directly to an online bank's transaction flow, stopping questionable activities before they are completed. Alongside this setup, a browser add-on built for Chrome is designed to flag deceptive web links and reduce threats from harmful sites. These tests show one insight: adjusting decisions by cost impact and validating across entire systems makes deployment more stable and realistic for today’s digital banking platforms.