Paper Caution Ahead: Numerical Reasoning and Look-ahead Bias in AI Models
Recent literature in accounting and finance has documented that modern AI systems achieve superhuman performance across a range of foundational tasks in these fields. However, existing studies rarely provide economic rationale for this outperformance, in part because the internal mechanisms of these models remain largely opaque. This study aims to open that black box, offering direct evidence on how and why AI models appear to perform so well on accounting and finance-related tasks. The findings suggest that much of the apparent superiority of AI models stems from artifacts of the modeling process itself, rather than from economically meaningful mechanisms. The study focuses on two key sources of potential bias that may distort inferences in research relying on these models. First, large language models (LLMs) are shown to exhibit substantially poor numerical reasoning, raising important concerns about their suitability for quantitative applications in these domains. Second, commercial LLMs are found to suffer from significant look-ahead bias — access to information that would not have been available at the time of a real-world decision — which may account for a considerable share of their apparent predictive power across various settings. Despite these limitations, the study concludes on a constructive note, identifying numerous opportunities where AI systems can meaningfully advance research in accounting and finance. Together, these findings urge greater scrutiny of AI benchmarking practices in these fields while also pointing toward productive directions for responsible integration of AI tools in future work.
- Authored by
- 2023
- CAAI - Finance