Agentic AI for Autonomous, Explainable, and Real-Time Credit Risk Decision-Making

• URL: http://arxiv.org/abs/2601.00818v1
• Date: Mon, 22 Dec 2025 23:30:38 GMT
• Title: Agentic AI for Autonomous, Explainable, and Real-Time Credit Risk Decision-Making
• Authors: Chandra Sekhar Kubam,
• Abstract summary: This paper presents an Agentic AI framework, or a system where AI agents view the world of dynamic credit independent of human observers.<n>The research introduces a multi-agent system with reinforcing learning, natural language reasoning, explainable AI modules, and real-time data absorption pipelines.<n>Findings indicate that decision speed, transparency and responsiveness is better than traditional credit scoring models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Significant digitalization of financial services in a short period of time has led to an urgent demand to have autonomous, transparent and real-time credit risk decision making systems. The traditional machine learning models are effective in pattern recognition, but do not have the adaptive reasoning, situational awareness, and autonomy needed in modern financial operations. As a proposal, this paper presents an Agentic AI framework, or a system where AI agents view the world of dynamic credit independent of human observers, who then make actions based on their articulable decision-making paths. The research introduces a multi-agent system with reinforcing learning, natural language reasoning, explainable AI modules, and real-time data absorption pipelines as a means of assessing the risk profiles of borrowers with few humans being involved. The processes consist of agent collaboration protocol, risk-scoring engines, interpretability layers, and continuous feedback learning cycles. Findings indicate that decision speed, transparency and responsiveness is better than traditional credit scoring models. Nevertheless, there are still some practical limitations such as risks of model drift, inconsistencies in interpreting high dimensional data and regulatory uncertainties as well as infrastructure limitations in low-resource settings. The suggested system has a high prospective to transform credit analytics and future studies ought to be directed on dynamic regulatory compliance mobilizers, new agent teamwork, adversarial robustness, and large-scale implementation in cross-country credit ecosystems.

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