aiqbench analysis
This research proposes a novel pipeline combining Topological Node Feature spectral embedding with Random Forest to address class imbalance in automobile insurance fraud detection. By integrating MWMOTE for resampling and TNFSE2 for dimensionality reduction, the study demonstrates superior performance metrics compared to standard baselines like t-SNE or Isomap. The work advances supervised machine learning techniques specifically tailored for high-stakes financial applications where data scarcity and imbalance are critical challenges.
TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection
arXiv:2609.33376v1 Announce Type: new Abstract: 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
Abstract: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
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2609.33376 [cs.LG]
(or arXiv:2609.33376v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.33376 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 320 words
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