Why do LLMs Hallucinate, and how do we avoid them?
AI agents powered by LLMs can sometimes generate outputs that seem plausible but are factually incorrect or nonsensical – a phenomenon known as "hallucinations."These hallucinations can arise from biases in the training data, lack of real-world grounding, or the inherent probabilistic nature of these models. In lending, such hallucinations could lead to inaccurate loan approvals, flawed risk assessments, or even misleading communication with borrowers. That’s why RAG is used to mitigate the hallucination risk, grounding the AI's responses in verifiable data from reliable sources. Still, that may not be sufficient, so we must incorporate a human-in-the-loop system. While AI agents automate much of the lending process, a human-in-the-loop approach ensures responsible and ethical decision-making. In this setup, human experts can oversee the AI's actions, review complex cases, and handle exceptions that require human judgment or intervention. This collaboration allows lenders to leverage the efficiency and speed of AI while retaining human oversight for sensitive decisions that impact overall final loan approval or managing edge cases that fall outside the AI's capabilities.
