{"ID":2834002,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02726","arxiv_id":"2512.02726","title":"AuditCopilot: Leveraging LLMs for Fraud Detection in Double-Entry Bookkeeping","abstract":"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.","short_abstract":"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. Benchmarkin...","url_abs":"https://arxiv.org/abs/2512.02726","url_pdf":"https://arxiv.org/pdf/2512.02726v1","authors":"[\"Md Abdul Kadir\",\"Sai Suresh Macharla Vasu\",\"Sidharth S. Nair\",\"Daniel Sonntag\"]","published":"2025-12-02T13:00:57Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}