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 98 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，即便是以精确率为核心的算法在面对新型骗局时也显不足。这些工具通常忽略犯罪行为的细微变化，遗漏关键信号。由于未被发现的违规行为给金融机构造成的损失远高于被标记的合法交易，因此尽可能识别所有潜在欺诈案例至关重要。当监管疏漏导致重大损失时，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最大限度降低欺诈漏报率。本研究构建了一个通过智能阈值选择调整的集成学习系统。利用公开的真实交易记录数据集（其中欺诈行为在正常活动中出现频率极低），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98%的实际欺诈交易，在处理类别不平衡样本时优于依赖固定规则的标准配置。在真实场景测试中，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时拦截。此外，本研究还开发了适用于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。这些结果表明：基于成本影响调整决策策略，并通过全系统验证，可使部署方案更稳定可靠，符合当前数字银行平台的现实需求。