@inproceedings{yu-sun-2026-cognitive,
title = "Cognitive Echoes: Deployable Conversational Risk Screening in Automotive Finance on Constrained {LLM}s",
author = "Yu, Zewei and
Sun, Yuting",
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.87/",
pages = "1456--1465",
ISBN = "979-8-89176-460-6",
abstract = "Automotive-finance screening can fail when individually valid documents are paired with an internally inconsistent applicant narrative. We present the Adversarial Cognitive Probing Framework (ACPF), a theory-motivated staged protocol that scores four ordinal behavioral dimensions and delegates the final decision to a deterministic backend predicate. We evaluate ACPF on a risk-enriched retrospective corpus of 340 officer{--}applicant dialogues: 72 institutional adjudications of suspected misrepresentation, 138 cases of significant early delinquency, 110 good cases, and 20 non-fraud rejections excluded from the primary metrics. On identical fixed transcripts and the same QWEN3.5-122B carrier, structured scoring raises combined-risk capture from 5.7{\%} to 61.0{\%} while reducing the false-rejection rate from 14.5{\%} to 12.7{\%}. With OmniMAX, the selected low-FPR carrier, ACPF captures 58/138 (42.0{\%}) delinquency cases at 6/110 (5.5{\%}) false rejections; current origination review flags none of these delinquency cases. On the institutionally adjudicated stratum, ACPF captures 56/72 (77.8{\%}) versus 72/72 for current review, but we make no comparative claim because the label and reference decision share institutional provenance. A shadow deployment has no adverse-action authority; prospective outcome validation is not yet available."
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<abstract>Automotive-finance screening can fail when individually valid documents are paired with an internally inconsistent applicant narrative. We present the Adversarial Cognitive Probing Framework (ACPF), a theory-motivated staged protocol that scores four ordinal behavioral dimensions and delegates the final decision to a deterministic backend predicate. We evaluate ACPF on a risk-enriched retrospective corpus of 340 officer–applicant dialogues: 72 institutional adjudications of suspected misrepresentation, 138 cases of significant early delinquency, 110 good cases, and 20 non-fraud rejections excluded from the primary metrics. On identical fixed transcripts and the same QWEN3.5-122B carrier, structured scoring raises combined-risk capture from 5.7% to 61.0% while reducing the false-rejection rate from 14.5% to 12.7%. With OmniMAX, the selected low-FPR carrier, ACPF captures 58/138 (42.0%) delinquency cases at 6/110 (5.5%) false rejections; current origination review flags none of these delinquency cases. On the institutionally adjudicated stratum, ACPF captures 56/72 (77.8%) versus 72/72 for current review, but we make no comparative claim because the label and reference decision share institutional provenance. A shadow deployment has no adverse-action authority; prospective outcome validation is not yet available.</abstract>
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%0 Conference Proceedings
%T Cognitive Echoes: Deployable Conversational Risk Screening in Automotive Finance on Constrained LLMs
%A Yu, Zewei
%A Sun, Yuting
%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 yu-sun-2026-cognitive
%X Automotive-finance screening can fail when individually valid documents are paired with an internally inconsistent applicant narrative. We present the Adversarial Cognitive Probing Framework (ACPF), a theory-motivated staged protocol that scores four ordinal behavioral dimensions and delegates the final decision to a deterministic backend predicate. We evaluate ACPF on a risk-enriched retrospective corpus of 340 officer–applicant dialogues: 72 institutional adjudications of suspected misrepresentation, 138 cases of significant early delinquency, 110 good cases, and 20 non-fraud rejections excluded from the primary metrics. On identical fixed transcripts and the same QWEN3.5-122B carrier, structured scoring raises combined-risk capture from 5.7% to 61.0% while reducing the false-rejection rate from 14.5% to 12.7%. With OmniMAX, the selected low-FPR carrier, ACPF captures 58/138 (42.0%) delinquency cases at 6/110 (5.5%) false rejections; current origination review flags none of these delinquency cases. On the institutionally adjudicated stratum, ACPF captures 56/72 (77.8%) versus 72/72 for current review, but we make no comparative claim because the label and reference decision share institutional provenance. A shadow deployment has no adverse-action authority; prospective outcome validation is not yet available.
%U https://aclanthology.org/2026.emnlp-industry.87/
%P 1456-1465
Markdown (Informal)
[Cognitive Echoes: Deployable Conversational Risk Screening in Automotive Finance on Constrained LLMs](https://aclanthology.org/2026.emnlp-industry.87/) (Yu & Sun, EMNLP 2026)
ACL