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 reveal that approximately 91 percent of actual fraud is detected, outperforming 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 banking 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 results show that adjusting decisions by cost impact and validating across entire systems makes deployment more stable and realistic for today's digital banking platforms.

翻译：数字银行服务中的欺诈活动日益复杂，对现有防御体系构成严峻挑战。传统基于规则的方法难以应对新型欺诈模式，即便是以精确率为导向的算法在面对新型诈骗手段时也表现不足。这些工具通常忽略犯罪行为的细微变化，遗漏关键风险信号。由于未被发现的欺诈行为给金融机构造成的损失远高于被误判的正常交易，因此尽可能捕获所有潜在欺诈案例至关重要。当监管疏漏可能导致重大损失时，对真实威胁的高敏感性变得尤为关键。本研究核心目标是在不过度增加误报的前提下显著降低欺诈漏报率。本文构建了一个通过智能阈值调整的集成学习系统。基于公开的真实交易数据集——其中欺诈行为在正常活动中出现频率极低——我们在实际类别不平衡分布下进行测试。实验结果表明，该系统能检测约91%的真实欺诈交易，在处理类别不平衡样本时优于依赖固定规则的标准配置。在实时环境测试中，该欺诈检测系统直接接入在线银行交易流，可在可疑交易完成前实施拦截。此外，我们还开发了适用于Chrome浏览器的扩展程序，用于标记欺诈性网页链接并降低恶意网站威胁。这些成果表明，通过成本影响调整决策并在全系统范围内进行验证，能够为现代数字银行平台提供更稳定且实用的部署方案。