Summary of Advancing Anomaly Detection: Non-semantic Financial Data Encoding with Llms, by Alexander Bakumenko (1) et al.
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
by Alexander Bakumenko, Kateřina Hlaváčková-Schindler, Claudia Plant, Nina C. Hubig
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Risk Management (q-fin.RM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to detecting anomalies in general ledger data using Large Language Models (LLMs) embeddings. The authors tested 3 pre-trained sentence-transformer models to encode categorical data from real-world financial records, achieving improved results over traditional machine learning methods. For the downstream classification task, they implemented and evaluated 5 optimized ML models, including Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks. The findings demonstrate that LLMs contribute valuable information to anomaly detection, outperforming baselines in selected settings by a large margin. This study highlights the potential of LLM embeddings for enhancing anomaly detection in financial journal entries, particularly when dealing with feature sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding unusual patterns in financial records using special computer algorithms called Machine Learning models. The researchers tested different ways to use these algorithms on real-world data and found that a new method using “Large Language Models” worked really well. They tried many different approaches, including some simple ones like looking at single transactions, and some more complex ones that combined information from multiple transactions. By comparing the results, they showed that this new approach can find anomalies in financial records more effectively than other methods. |
Keywords
» Artificial intelligence » Anomaly detection » Boosting » Classification » Logistic regression » Machine learning » Random forest » Transformer
