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TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection — arXiv:2609.33376

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33376
License (as archived): CC0-1.0
Archived: 2026-10-07T22:37:23.435Z
SHA-256 of the archived text: 7c46ab61667d9d9add7aeb80ff01f1204dedbc6dfa88bf2376af392b29a88984
Stable URL of this page: https://metraipolis.com/documento/dc_7bfbc1f61ace

How to cite

TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection — arXiv:2609.33376 — arXiv (en), https://arxiv.org/abs/2609.33376 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7bfbc1f61ace (Metraipolis archive dc_7bfbc1f61ace, sha256 7c46ab61667d9d9add7aeb80ff01f1204dedbc6dfa88bf2376af392b29a88984)

BibTeX

@misc{dc7bfbc1f61ace,
  title = {TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection — arXiv:2609.33376},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33376},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7bfbc1f61ace; sha256 7c46ab61667d9d9add7aeb80ff01f1204dedbc6dfa88bf2376af392b29a88984},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33376. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Fraud detection is an important area of research in the insurance business due to its financial implications. The primary aim of a fraud detection model is to identify fraud and non-fraud cases with high accuracy along with other important metrics such as Sensitivity, Specificity, Precision, F1-score, False Positive Rate, False Discovery Rate, AUC etc. To achieve this, we need to explore a suitable classification model to identify fraud and non-fraud cases. In this work, we have used auto insurance data set and explored classification models such as Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM and Gradient Boosting Machine (GBM). To overcome the problem of data imbalance, we have employed MWMOTE and TGAN techniques. We have used Topological Node Feature(TNF) based spectral embedding for low dimensional data representation along with some popular embedding methods like MDS, Isomaps and t-SNE. After studying all the 65 possible combinations of these models, we have proposed an innovative method for effective automobile insurance fraud detection. For the given dataset, our results show that using a combination of MWMOTE as a data imbalance handling technique (Phase I), TNFSE2 as data embedding (Phase II) and Random Forest as classification (Phase III) provides the best result in comparison to all other combinations. This work also highlights the efficacy of TNF based spectral embedding in automobile insurance dataset

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.33376 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:14:27.932Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).