@inproceedings{toles-etal-2026-mortarbench-evaluating,
title = "{M}ortar{B}ench: Evaluating Mortgage Loan Origination Agents",
author = "Toles, Matthew and
Lu, Yunan and
Munjal, Manav and
Liu, Bojun and
Deng, Yuanhao and
Selig, Stephanie and
Rindner, Derek and
Li, Cheng and
Yu, Zhou",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.72/",
pages = "1222--1233",
ISBN = "979-8-89176-460-6",
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 80.8{\%} F1 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 peak F1 accuracy to 83.6{\%} while improving risk management steering and reducing bias."
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<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 80.8% F1 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 peak F1 accuracy to 83.6% while improving risk management steering and reducing bias.</abstract>
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%0 Conference Proceedings
%T MortarBench: Evaluating Mortgage Loan Origination Agents
%A Toles, Matthew
%A Lu, Yunan
%A Munjal, Manav
%A Liu, Bojun
%A Deng, Yuanhao
%A Selig, Stephanie
%A Rindner, Derek
%A Li, Cheng
%A Yu, Zhou
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F toles-etal-2026-mortarbench-evaluating
%X 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 80.8% F1 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 peak F1 accuracy to 83.6% while improving risk management steering and reducing bias.
%U https://aclanthology.org/2026.emnlp-industry.72/
%P 1222-1233
Markdown (Informal)
[MortarBench: Evaluating Mortgage Loan Origination Agents](https://aclanthology.org/2026.emnlp-industry.72/) (Toles et al., EMNLP 2026)
ACL
- Matthew Toles, Yunan Lu, Manav Munjal, Bojun Liu, Yuanhao Deng, Stephanie Selig, Derek Rindner, Cheng Li, and Zhou Yu. 2026. MortarBench: Evaluating Mortgage Loan Origination Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1222–1233, Budapest, Hungary. Association for Computational Linguistics.