Oculi: A Conversational Agentic Platform for Automated Credit Risk Analysis

Researchers introduced Oculi, a conversational agentic platform that automates credit risk analysis by converting natural language questions into SQL queries, statistical tests, and visualizations, reducing time-to-insight while maintaining auditability. Evaluated on a mortgage portfolio with 200+ features, Oculi's three-layer architecture (LLM-powered reasoning, Model Context Protocol tool servers, and agentic UI) enabled discovery of material risk segments previously intractable through manual exploration.

arXiv:2608.28944v1 Announce Type: new 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.