BiasMix-Finance: Post-Generation KYC Guardrails for LLM Portfolio Advice

Abstract:Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic KYC-style constraints on risk, fees, and diversification. This is especially problematic in agentic multi-turn advisory systems, where each draft recommendation can become an action unless guarded by an auditable enforcement layer. We study a model-agnostic, asset-agnostic post-generation guardrail pipeline: (i) enforce a strict JSON allocation schema, (ii) validate allocations against numeric caps, and (iii) when violations occur, deterministically project the output to the nearest feasible portfolio via a convex quadratic program (QCQP). We introduce BiasMix-Finance (Mini), a compact stress-test benchmark for constrained decision-making under biased LLM generations, with a 16-ETF universe, three investor profiles, and eight bias prompts. Across three models and three inference modes (direct, critique, self-consistency), first-pass generations violate at least one cap in 47.6-85.7% of test cases (67.2% pooled), but the convex projection layer reduces final feasibility violations to 0% while requiring only a small correction distance (test pooled median D=||w*-w0||_2=0.066), indicating that the guardrail typically preserves the intent of the original allocation. We report violation rates and correction distances with confidence intervals, and paired model comparisons with multiple-testing correction. To support reproducibility, we release the dataset, prompts, caps, and code in our public GitHub repository.

18 pages, 9 figures, 8 tables. Accepted to the ICLR 2026 Financial AI (FinAI) Workshop. Code and data: this https URL

Artificial Intelligence (cs.AI)

I.2.7; I.2.1; G.1.6

arXiv:2608.28646 [cs.AI]

(or arXiv:2608.28646v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.28646 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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