AI 中文总结
研究将住房担保风险预测转化为可审计报告的挑战，提出证据约束报告流程，利用韩国租房押金数据，结合预测模型与结构化证据、验证及分析师监督，提升高风险检测能力，经评估报告支持实际决策，证明该方法可靠。
AI 中文摘要
将下个月的住房担保风险预测转化为可审计的运营报告至关重要但具有挑战性，因为极端事件稀少、源记录保密且生成的叙述可能扭曲潜在证据。我们利用2015年9月至2025年12月韩国月全租房押金担保数据，引入证据约束报告流程，优先进行极端事件监测，检索相关历史先例，将可采信信息整理成类型化证据契约，并在分析师审查前验证生成的声明。在原始面板上训练和选择预测主干，报告实验使用根据经验范围和时间结构校准的合成总体情景。所选预测模型大幅提高高风险检测能力，同时保持有竞争力的平均误差。跨八个大语言模型，结构化证据持续提高报告质量、数字保真度和声明级基础。从业者评估表明报告支持实际审查和决策，多数参与者认为报告实用并认可运营试点。这些发现表明可靠的大语言模型辅助报告需要预测模型与结构化证据、明确验证和分析师监督相结合。
英文摘要
Translating next-month housing-guarantee risk forecasts into auditable operational reports is essential yet challenging because upper-tail events are sparse, source records are confidential, and generated narratives can distort the underlying evidence. Using monthly South Korean \textit{jeonse} deposit guarantee data from September 2015 to December 2025, we introduce an evidence-constrained reporting pipeline that prioritizes upper-tail monitoring, retrieves historical precedents aligned with the forecasting rationale, organizes admissible information into typed evidence contracts, and verifies generated claims before analyst review. We train and select the forecasting backbone on the original panel, whereas the reporting experiments use synthetic aggregate scenarios calibrated to its empirical ranges and temporal structure. The selected forecasting model substantially improves high-risk detection while retaining competitive average error. Across eight LLMs, structured evidence consistently increases report quality, numerical fidelity, and claim-level grounding. A practitioner evaluation involving 51 analysts and related domain professionals further indicates that the reports support real-world review and decision-making: most participants rated them as practically useful and endorsed an operational pilot. These findings demonstrate that reliable LLM-assisted reporting requires predictive models to be coupled with structured evidence, explicit verification, and analyst oversight.
