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

Autonomous systems require explainability safeguards: The paper demonstrates that while agentic AI can make faster credit decisions than traditional models, the system includes "interpretability layers" and "articulable decision-making paths" as essential components—suggesting that autonomy without transparency is insufficient for responsible financial decision-making.

Model drift and data inconsistencies remain unresolved challenges: The authors acknowledge "risks of model drift" and "inconsistencies in interpreting high dimensional data" as practical limitations, indicating that even advanced AI systems can produce unreliable decisions over time without proper monitoring and human oversight mechanisms.

Regulatory frameworks lag behind technology deployment: The paper identifies "regulatory uncertainties" as a key limitation, highlighting a critical gap where financial institutions may deploy sophisticated AI systems before legal and compliance standards catch up—underscoring the need for proactive regulation rather than reactive oversight.

Human involvement shouldn't be eliminated entirely: Despite the system's goal of "few humans being involved," the paper's acknowledgment of practical limitations suggests that human judgment remains necessary for high-stakes credit decisions, particularly in complex or edge-case scenarios.

Infrastructure inequality could worsen financial access: The authors note "infrastructure limitations in low-resource settings," implying that advanced agentic AI credit systems may only be accessible to well-funded institutions, potentially deepening disparities in lending practices across different markets and communities.