# AuditCopilot: Leveraging LLMs for Fraud Detection in Double-Entry Bookkeeping

arXiv: https://arxiv.org/abs/2512.02726 · HTML: https://mlsanity.com/paper/2512.02726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [AD-LLM: Benchmarking Large Language Models for Anomaly Detection](https://mlsanity.com/paper/2412.11142/) (2024)
- [AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach](https://mlsanity.com/paper/2402.09334/) (2024)
- [LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop](https://mlsanity.com/paper/2402.09346/) (2024)
- [Automating Financial Statement Audits with Large Language Models](https://mlsanity.com/paper/2506.17282/) (2025)

## Abstract (arXiv)

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 AI-augmented auditing, where human auditors collaborate with foundation models to strengthen financial integrity.