Problem
Research questions and friction points this paper is trying to address.
Credit Risk Analysis
Manual SQL Queries
Statistical Computations
Visualization Dashboards
Time-Consuming Workflow
Oculi: A Conversational Agentic Platform for Automated Credit Risk Analysis
🤖 AI Summary
本文介绍Oculi平台，通过自然语言处理和三层架构自动完成信用风险分析，结合统计方法与LLM指导特征选择，以减少手动操作时间和提高风险识别效率。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
conversational platform
credit risk analysis
three-layer architecture
LLM-guided feature selection
segment discovery
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