User authentication and fraud detection face growing challenges as digital systems expand and adversaries adopt increasingly sophisticated tactics. Traditional knowledge-based authentication remains rigid, requiring exact word-for-word string matches that fail to accommodate natural human memory and linguistic variation. Meanwhile, fraud-detection pipelines struggle to keep pace with rapidly evolving scam behaviors, leading to high false-positive rates and frequent retraining cycles required. This work introduces two complementary LLM-enabled solutions, namely, an LLM-assisted authentication mechanism that evaluates semantic correctness rather than exact wording, supported by document segmentation and a hybrid scoring method combining LLM judgement with cosine-similarity metrics and a RAG-based fraud-detection pipeline that grounds LLM reasoning in curated evidence to reduce hallucinations and adapt to emerging scam patterns without model retraining. Experiments show that the authentication system accepts 99.5% of legitimate non-exact answers while maintaining a 0.1% false-acceptance rate, and that the RAG-enhanced fraud detection reduces false positives from 17.2% to 3.5%. Together, these findings demonstrate that LLMs can significantly improve both usability and robustness in security workflows, offering a more adaptive , explainable, and human-aligned approach to authentication and fraud detection.

翻译：随着数字系统规模的扩展以及攻击者采用日益复杂的手段，用户身份验证与欺诈检测正面临日益严峻的挑战。传统的基于知识的身份验证方式过于僵化，要求精确的逐字字符串匹配，无法适应人类记忆的自然差异与语言变体。与此同时，欺诈检测流水线难以跟上快速演变的诈骗行为，导致高误报率以及频繁的重新训练需求。本文提出了两种互补的、基于大语言模型的解决方案：其一是一种LLM辅助的身份验证机制，它通过文档分割以及一种融合LLM判断与余弦相似度度量的混合评分方法，评估语义正确性而非精确措辞；其二是基于RAG的欺诈检测流水线，该流水线将LLM推理根植于经过筛选的证据中，以减少幻觉并适应新兴诈骗模式，而无需重新训练模型。实验表明，该身份验证系统对合法的非精确答案接受率达99.5%，同时维持0.1%的误接受率；而RAG增强的欺诈检测将误报率从17.2%降至3.5%。这些发现共同表明，LLM能够显著提升安全工作流的可用性与鲁棒性，为身份验证与欺诈检测提供了一种更具自适应性、可解释性且更契合人类习惯的解决方案。