Explainable Fraud Detection with GNNExplainer and Shapley Values
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911156363329536 |
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| author | Dao, Ngoc Hieu |
| author_facet | Dao, Ngoc Hieu |
| contents | The risk of financial fraud is increasing as digital payments are used more and more frequently. Although the use of artificial intelligence systems for fraud detection is widespread, society and regulators have raised the standards for these systems' transparency for reliability verification purposes. To increase their effectiveness in conducting fraud investigations, fraud analysts also profit from having concise and understandable explanations. To solve these challenges, the paper will concentrate on developing an explainable fraud detector. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Fraud Detection with GNNExplainer and Shapley Values Dao, Ngoc Hieu Machine Learning The risk of financial fraud is increasing as digital payments are used more and more frequently. Although the use of artificial intelligence systems for fraud detection is widespread, society and regulators have raised the standards for these systems' transparency for reliability verification purposes. To increase their effectiveness in conducting fraud investigations, fraud analysts also profit from having concise and understandable explanations. To solve these challenges, the paper will concentrate on developing an explainable fraud detector. |
| title | Explainable Fraud Detection with GNNExplainer and Shapley Values |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.12262 |