Auditors rely on Journal Entry Tests (JETs) to detect anomalies in tax-related ledger records, but rule-based methods generate overwhelming false positives and struggle with subtle irregularities. We investigate whether large language models (LLMs) can serve as anomaly detectors in double-entry bookkeeping. Benchmarking SoTA LLMs such as LLaMA and Gemma on both synthetic and real-world anonymized ledgers, we compare them against JETs and machine learning baselines. Our results show that LLMs consistently outperform traditional rule-based JETs and classical ML baselines, while also providing natural-language explanations that enhance interpretability. These results highlight the potential of \textbf{AI-augmented auditing}, where human auditors collaborate with foundation models to strengthen financial integrity.
@article{arxiv.2512.02726,
title = {AuditCopilot: Leveraging LLMs for Fraud Detection in Double-Entry Bookkeeping},
author = {Md Abdul Kadir and Sai Suresh Macharla Vasu and Sidharth S. Nair and Daniel Sonntag},
journal= {arXiv preprint arXiv:2512.02726},
year = {2025}
}