Agentic CBR in action: empowering loan approvals through interactive, counterfactual explanations.

Large Language Models (LLMs) have demonstrated impressive conversational capabilities, yet their susceptibility to hallucinations and inconsistent recommendations poses significant risks in high-stakes domains such as finance. This paper presents an interactive chatbot for loan application guidance that leverages a case-based reasoning (CBR) approach to generate actionable counterfactual explanations within an agentic framework. Our system employs a supervisor agent, built using the LangGraph framework, to orchestrate four specialised agents: a classifier agent that provides an initial loan prediction, a causally-aware counterfactual explanation agent that proposes minimal yet feasible modifications to reverse an unfavourable decision, a Feature Actionability Taxonomy (FAT) agent that updates user-specific immutability constraints based on feedback, and a template-based natural language generation (NLG) agent that transforms counterfactual suggestions into clear, user-friendly explanations. A key strength of our design is the automated feedback loop: when users indicate that certain suggestions are unworkable, the FAT agent revises the constraints and instructs the counterfactual generation agent to produce a refined explanation. We detail the system architecture and workflow and outline an experimental plan that compares our full agentic chatbot to ablated variants and a LLM-Only Baseline. And finally we outline a planned user study to evaluate how controlled reasoning affects trust in high-stakes lending.