用自然语言对话自动分析信贷风险，快速发现人工难察觉的高危贷款群体。
Oculi: A Conversational Agentic Platform for Automated Credit Risk Analysis
- 通过大模型驱动的智能体，将自然语言转为数据查询、统计检验和可视化
- 在200+特征的房贷数据集上识别出此前难以发现的重大风险段
- 适合金融风控人员快速探索高危客户群，兼顾可审计性与统计严谨性
金融机构传统的信贷风险分析依赖分析师手动编写SQL、执行统计计算并制作可视化仪表盘，流程耗时且受限于熟悉的数据维度。我们提出Oculi，一个对话式平台，能将自然语言问题转化为包含数据查询、统计检验和交互式可视化的完整风险分析。Oculi采用三层架构：推理（大模型智能体）、执行（模型上下文协议工具服务器）和展示（智能体界面），帮助分析师发现高风险投资组合片段。其中新提出的片段发现流程结合确定性统计方法与大模型引导的特征选择，融合语义知识与数据指标，识别出有意义且可操作的风险分组。在包含200多个特征的抵押贷款数据集上评估表明，Oculi能有效发现此前人工探索难以触及的重大风险段，显著缩短洞察时间，同时保持可审计性和统计严谨性。
原文摘要 · Abstract (English)
Credit risk analysis in financial institutions traditionally requires analysts to manually write SQL queries, run statistical computations, and build visualization dashboards. This is a time-consuming workflow that limits exploration to familiar segments. We introduce \textbf{Oculi}, a conversational platform that transforms natural language questions into comprehensive credit risk analyses, complete with data queries, statistical testing, and interactive visualizations. Oculi employs a three-layer architecture that separates reasoning (LLM-powered agent), execution (Model Context Protocol tool servers), and presentation (agentic UI), enabling analysts to discover high-risk portfolio segments. Within Oculi, a new segment discovery pipeline is proposed that combines deterministic statistical methods with LLM-guided feature selection, leveraging LLM semantic domain knowledge alongside data-driven metrics to identify meaningful, actionable portfolio segments. Evaluated on a mortgage portfolio with 200+ features, Oculi demonstrates effectiveness in discovering material risk segments previously intractable through manual exploration, reducing time-to-insight significantly while maintaining auditability and statistical rigor.
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