MortarBench: Evaluating Mortgage Loan Origination Agents

Abstract:Loan origination is the process by which a lender creates a new loan, from application and underwriting through approval and funding. This process serves a critical role in evaluating the eligibility and level of risk posed by an applicant. Recently, firms have begun using mortgage loan agents to augment human loan officers, despite a lack of any public benchmark. To fill this gap, we present MortarBench, a loan origination agent benchmark. MortarBench uses a financial data synthesis and mutation pipeline to generate examples with broad edge case coverage that match real-world distributions and questions. We find that state-of-the-art large language models (LLMs) perform poorly, with closed-source models achieving at most 77.1\% exact match accuracy. We also discover systematic biases in LLM perception of foreignness related to non-English names. Noting these weaknesses, we introduce CRIT, a confidence calibration framework. Our method increases accuracy to 80.5\% while improving risk management steering and reducing bias.

Machine Learning (cs.LG)

arXiv:2606.19416 [cs.LG]

(or arXiv:2606.19416v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.19416 Focus to learn more arXiv-issued DOI via DataCite

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