Can AI Follow the Money?

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Author: Denis Avetisyan

A new benchmark assesses language models’ ability to handle complex financial instructions, revealing surprising strengths in open-weight systems.

The Financial Instruction Following Evaluation (FIFE) benchmark measures language model performance on multi-step financial tasks with strict compliance requirements, using regex verification and constraint satisfaction.

Despite advances in language models, reliably executing complex, multi-step instructions-particularly in high-stakes domains-remains a significant challenge. To address this, we introduce Financial Instruction Following Evaluation (FIFE), a novel benchmark designed to rigorously assess LM performance on financial analysis tasks requiring precise instruction following. Our evaluation of 53 models reveals a surprising hierarchy, with leading open-weight models surpassing proprietary systems, though all struggle to achieve perfect compliance with FIFE’s stringent constraints. Can these findings catalyze the development of more robust and trustworthy language models for critical financial applications and, ultimately, unlock the full potential of Reinforcement Learning in this domain?

The Inherent Fragility of Financial Logic

Despite the burgeoning potential of Large Language Models (LLMs) in automating and enhancing financial processes, a core challenge lies in their inherent unreliability when applied to complex workflows. These models, while proficient at generating human-like text, often exhibit inconsistencies and errors in tasks demanding precision, such as risk assessment, fraud detection, and algorithmic trading. The stochastic nature of LLM outputs means that identical inputs can yield varied results, making it difficult to guarantee consistent and trustworthy performance – a non-negotiable requirement within the highly regulated financial sector. This unpredictability stems from the models’ reliance on statistical correlations rather than genuine understanding of financial principles, raising concerns about the validity of their outputs and the potential for costly mistakes. Consequently, widespread adoption hinges on addressing these reliability issues and establishing robust mechanisms for verifying the accuracy and consistency of LLM-driven financial applications.

Contemporary Large Language Models, despite advancements in natural language processing, exhibit a notable fragility when tasked with adhering to precise instructions, a critical flaw impacting their viability in financial applications. This isn’t simply a matter of occasional misinterpretation; the models frequently struggle with nuances in phrasing, logical constraints, and the consistent application of rules, leading to demonstrable errors in calculations, data retrieval, and report generation. The consequence is a potential for significant financial miscalculations or inaccurate risk assessments, particularly in complex workflows requiring multi-step reasoning and the reliable execution of specific procedures. While capable of generating fluent text, these models often prioritize plausibility over accuracy, making verification and error detection a substantial challenge in environments where even minor deviations can have substantial consequences.

The stringent requirements of regulated financial environments pose a considerable barrier to the widespread adoption of Large Language Models. Unlike applications where occasional inaccuracies are tolerable, financial workflows demand unwavering consistency and verifiable adherence to established constraints – rules governing transactions, risk assessment, and reporting. Current LLMs, while powerful, often lack the demonstrable reliability needed to satisfy these demands; their outputs, even when seemingly plausible, may not be consistently traceable to defined parameters or provably free from error. This absence of verifiable constraints and consistent performance creates substantial legal and fiduciary risks, hindering their use in areas like algorithmic trading, fraud detection, and regulatory compliance, where even minor deviations can have significant financial repercussions and erode public trust.

FIFE: A Crucible for Financial Integrity

The Financial Integrity and Faithful Execution (FIFE) benchmark distinguishes itself from general language model evaluations by focusing exclusively on the reliability of instruction following within the domain of financial applications. Existing benchmarks often prioritize broad language understanding and generation capabilities; however, these do not adequately address the critical need for precision and accuracy in financial contexts. FIFE’s design specifically targets scenarios where even minor deviations from instructions can lead to significant financial consequences, necessitating a granular assessment of a model’s ability to consistently adhere to given directives. This specialized focus allows for a more relevant and rigorous evaluation of Large Language Models (LLMs) intended for use in financial services, such as fraud detection, risk management, and algorithmic trading.

The FIFE benchmark incorporates a task suite developed in direct collaboration with financial Subject Matter Experts (SMEs). This curation process prioritizes tasks reflecting real-world financial operations and decision-making scenarios, encompassing areas such as fraud detection, risk assessment, and regulatory compliance. SME involvement extends to task definition, data creation, and the establishment of expected outputs, ensuring the benchmark’s relevance to practical applications and moving beyond purely linguistic evaluations. The resulting tasks are designed to assess a model’s ability to accurately process and interpret financial information, rather than simply demonstrating general language proficiency.

The FIFE benchmark incorporates Verifiable Constraints as a method for automated evaluation of model outputs in financial tasks. These constraints are formally defined rules, expressed as logical statements or numerical ranges, that a correct response must satisfy. Rather than relying on human judgment for correctness, FIFE automatically checks if a model’s generated output adheres to these predefined constraints. This approach enables objective scoring and removes ambiguity, providing a more reliable and consistent assessment of a model’s performance in areas such as regulatory compliance, fraud detection, and accurate transaction processing. The use of verifiable constraints allows for large-scale, automated benchmarking and facilitates the identification of models with superior reliability in financial applications.

Assessing Fidelity: Methods for Rigorous Evaluation

FIFE utilizes both Strict and Loose Evaluation methodologies to provide a comprehensive assessment of model fidelity. Strict Evaluation demands exact matches to predefined criteria, flagging any deviation as a failure; this approach is suited for tasks requiring precise adherence to instructions. Conversely, Loose Evaluation permits partial matches or semantic equivalence, acknowledging that variations can still represent successful task completion. This method is beneficial when evaluating more flexible or creative outputs where multiple valid responses exist. By employing both approaches, FIFE generates a more nuanced performance profile, identifying not only whether a model meets requirements, but also the degree to which it does so, and offering insights into the model’s robustness and adaptability.

Regex-based verification within the FIFE benchmark utilizes regular expressions to programmatically evaluate model outputs against predefined criteria. This automated process replaces manual inspection, thereby increasing evaluation throughput and minimizing subjective bias. The system defines expected patterns in the generated text, and outputs are flagged as compliant or non-compliant based on whether they match these expressions. This approach is particularly effective for assessing adherence to specific formatting requirements, keyword inclusion, or the presence of particular syntactic structures, providing a quantitative and reproducible measure of instruction-following capability.

The benchmark incorporates constraint types beyond simple condition checks, enabling detailed fidelity assessments. Structural Constraints verify adherence to specified formatting requirements, such as the presence of bullet points, specific heading structures, or character limits within text blocks. Compositional Constraints extend this by requiring the fulfillment of multiple, interconnected conditions simultaneously; for example, a response might need to both include a specific keyword and be formatted as a numbered list to satisfy this constraint type. This allows for the evaluation of complex instructions that demand both content and presentational accuracy.

The Echo of Constraints: Implications for Model Development

The FIFE benchmark underscores a critical, yet often overlooked, aspect of language model evaluation: the necessity of meticulous data normalization and consistent formatting, specifically within domain-specific contexts. Reliable results aren’t simply a matter of model architecture or training data volume; rather, they are fundamentally dependent on the standardization of input data. Variations in formatting – such as differing date formats, inconsistent units of measurement, or ambiguous terminology – can introduce noise and bias, leading to inaccurate assessments of a model’s true capabilities. The benchmark’s design actively tests a model’s ability to handle such nuances, demanding that systems not only understand the semantic content of information, but also consistently interpret its presentation. This focus on data hygiene pushes developers to prioritize data preprocessing and validation, ultimately fostering the creation of more robust and trustworthy language models capable of real-world application.

The FIFE benchmark isn’t simply measuring a language model’s ability to generate text; it actively encourages the development of systems built on verifiable foundations. By prioritizing constraints – logical rules and factual correctness – the benchmark compels researchers to move beyond superficial fluency and focus on building models that understand information, not just mimic its patterns. This emphasis on constraint satisfaction steers innovation toward more robust architectures and training methodologies, ultimately leading to language models that are demonstrably more trustworthy and less prone to generating illogical or factually incorrect outputs. The demand for verifiable reasoning fosters a shift in development, prioritizing internal consistency and alignment with real-world knowledge – a crucial step towards deploying AI systems with greater reliability and accountability.

Recent evaluations utilizing the FIFE benchmark reveal a noteworthy shift in language model capabilities, as the open-weight model Llama-4 Maverick 18B demonstrably outperformed leading proprietary models. Achieving a strict compliance rate of 76.1% and a loose compliance of 79.5%, this model exceeded the proprietary benchmark’s scores of 65.9% and 70.5% respectively. This outcome isn’t simply a marginal improvement; it signifies a substantial leap forward for openly available language models, indicating that advancements in model architecture and training methodologies are effectively closing the performance gap with traditionally dominant, closed-source systems. The results suggest that open-weight models are no longer trailing behind, but actively competing – and in this instance, surpassing – their proprietary counterparts in adhering to verifiable constraints and demonstrating reliable performance.

Current open-source language models, while rapidly evolving, demonstrate a considerable performance disparity when subjected to rigorous factual consistency evaluations. Analyses of the FIFE benchmark reveal that leading open-source models achieve only 45.5% strict compliance and 48.9% loose compliance with verifiable constraints. This substantial gap, when contrasted with the performance of both proprietary models and the top-performing open-weight alternative, underscores the need for continued development focused on enhancing factual grounding and constraint satisfaction within the open-source community. These findings suggest that significant advancements are still required to bridge the performance gap and build language models that consistently adhere to established truths and logical boundaries.

The pursuit of perfectly compliant systems, as highlighted by FIFE’s evaluation of instruction following in financial applications, reveals a fundamental truth about complex constructions. It echoes Ken Thompson’s observation: “There’s no such thing as a finished program.” The benchmark demonstrates that even open-weight models, capable of surprising performance, consistently falter when faced with rigorous constraint satisfaction. This isn’t a failure of the models themselves, but rather an acknowledgement that every attempt to formalize a system introduces new vectors for eventual breakdown – dependencies that will, inevitably, lead to systemic fragility. The drive toward verifiable rewards, while laudable, merely exposes the inherent limitations of building, rather than growing, these intricate ecosystems.

The Turning of the Wheel

The benchmark introduced here, FIFE, does not so much solve a problem as illuminate the shape of the difficulty. Every instruction followed is a constraint accepted, and every constraint is a future point of failure. The observed performance, even with open-weight models exceeding expectations, reveals not a triumph of engineering, but the inherent limits of formalizing financial reasoning. One builds a system to seem compliant; the market will always find the edge cases. Every dependency is a promise made to the past, and the past is rarely so accommodating.

The focus now shifts, inevitably, to the mechanics of failure. Regex verification and constraint satisfaction are merely diagnostic tools; they highlight where the system breaks, not why it was always destined to. The true work lies in understanding the systemic properties of these models-how errors propagate, how biases accumulate, and how the illusion of control is maintained. Control is an illusion that demands SLAs.

Perhaps the most telling observation is the persistent struggle with strict compliance. This suggests that the goal isn’t perfect execution, but resilient adaptation. Everything built will one day start fixing itself. The future of financial language models won’t be about building better rule-followers, but cultivating systems that can learn from their own transgressions, and rewrite their own constraints. The wheel turns, and the accounts reconcile…eventually.

Original article: https://arxiv.org/pdf/2512.08965.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-11 17:54