# Financial misstatement detection: a realistic evaluation

arXiv: https://arxiv.org/abs/2305.17457 · HTML: https://mlsanity.com/paper/2305.17457/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 27 May 2023.

## Builds on (most cited first, 2 of 2)

- [EDGAR-CORPUS: Billions of Tokens Make The World Go Round](https://mlsanity.com/paper/2109.14394/) (2021)
- [A Review of Financial Accounting Fraud Detection based on Data Mining Techniques](https://mlsanity.com/paper/1309.3944/) (2013)

## Built on by (most cited first, 4 of 4)

- [Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking](https://mlsanity.com/paper/2311.06102/) (2023)
- [Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance](https://mlsanity.com/paper/2308.14634/) (2023)
- [Focused PU learning from imbalanced data](https://mlsanity.com/paper/2605.14467/) (2026)
- [VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings](https://mlsanity.com/paper/2608.28402/) (2026)

## Abstract (arXiv)

In this work, we examine the evaluation process for the task of detecting financial reports with a high risk of containing a misstatement. This task is often referred to, in the literature, as ``misstatement detection in financial reports''. We provide an extensive review of the related literature. We propose a new, realistic evaluation framework for the task which, unlike a large part of the previous work: (a) focuses on the misstatement class and its rarity, (b) considers the dimension of time when splitting data into training and test and (c) considers the fact that misstatements can take a long time to detect. Most importantly, we show that the evaluation process significantly affects system performance, and we analyze the performance of different models and feature types in the new realistic framework.