ARCADIA: Scalable Causal Discovery for Corporate Bankruptcy Analysis Using Agentic AI

Overview

ARCADIA is an AI framework designed for scalable causal discovery in corporate bankruptcy analysis. It integrates large-language-model reasoning with statistical diagnostics to create valid, temporally coherent causal structures. This approach refines candidate Directed Acyclic Graphs (DAGs) through constraint-guided prompting and causal-validity feedback.

Key Insights

• Insight: ARCADIA produces more reliable causal graphs than traditional models like NOTEARS, GOLEM, and DirectLiNGAM.
  • Evidence: Experiments on corporate bankruptcy data.
  • Verifiable: Yes
• Insight: The framework offers a fully explainable, intervention-ready pipeline.
  • Evidence: Described in the paper's methodology.
  • Verifiable: Yes

Why It Matters

This framework is significant for BFSI professionals, particularly in corporate banking and risk management, as it enhances the reliability of causal analysis in bankruptcy scenarios.

Actionable Implications

• Implement ARCADIA for more accurate risk assessments in corporate banking.
• Use the framework to improve decision-making processes in high-stakes financial environments.