Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916475712831488 |
|---|---|
| author | Kadam, Prashank |
| author_facet | Kadam, Prashank |
| contents | Human-in-the-loop (HITL) feedback mechanisms can significantly enhance machine learning models, particularly in financial fraud detection, where fraud patterns change rapidly, and fraudulent nodes are sparse. Even small amounts of feedback from Subject Matter Experts (SMEs) can notably boost model performance. This paper examines the impact of HITL feedback on both traditional and advanced techniques using proprietary and publicly available datasets. Our results show that HITL feedback improves model accuracy, with graph-based techniques benefiting the most. We also introduce a novel feedback propagation method that extends feedback across the dataset, further enhancing detection accuracy. By leveraging human expertise, this approach addresses challenges related to evolving fraud patterns, data sparsity, and model interpretability, ultimately improving model robustness and streamlining the annotation process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05859 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation Kadam, Prashank Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science J.1 Human-in-the-loop (HITL) feedback mechanisms can significantly enhance machine learning models, particularly in financial fraud detection, where fraud patterns change rapidly, and fraudulent nodes are sparse. Even small amounts of feedback from Subject Matter Experts (SMEs) can notably boost model performance. This paper examines the impact of HITL feedback on both traditional and advanced techniques using proprietary and publicly available datasets. Our results show that HITL feedback improves model accuracy, with graph-based techniques benefiting the most. We also introduce a novel feedback propagation method that extends feedback across the dataset, further enhancing detection accuracy. By leveraging human expertise, this approach addresses challenges related to evolving fraud patterns, data sparsity, and model interpretability, ultimately improving model robustness and streamlining the annotation process. |
| title | Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science J.1 |
| url | https://arxiv.org/abs/2411.05859 |