基于TNF的谱嵌入在汽车保险欺诈检测中有效应用监督机器学习技术
TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection
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中文总结 AI 辅助
本研究提出一种结合MWMOTE、TNFSE2谱嵌入和随机森林的三阶段汽车保险欺诈检测方法，在65种组合中取得最优性能。
中文摘要 AI 辅助
欺诈检测因其财务影响而成为保险业务中重要的研究领域。欺诈检测模型的主要目标是高精度地识别欺诈和非欺诈案例，同时兼顾其他重要指标，如灵敏度、特异性、精确度、F1分数、假阳性率、假发现率、AUC等。为实现这一目标，我们需要探索合适的分类模型来识别欺诈和非欺诈案例。在本工作中，我们使用了汽车保险数据集，并探索了决策树（DT）、随机森林（RF）、XGBoost、LightGBM和梯度提升机（GBM）等分类模型。为克服数据不平衡问题，我们采用了MWMOTE和TGAN技术。我们使用基于拓扑节点特征（TNF）的谱嵌入进行低维数据表示，并结合了MDS、Isomaps和t-SNE等流行的嵌入方法。在研究了这些模型的全部65种可能组合后，我们提出了一种有效的汽车保险欺诈检测创新方法。对于给定数据集，我们的结果表明，使用MWMOTE作为数据不平衡处理技术（第一阶段）、TNFSE2作为数据嵌入（第二阶段）和随机森林作为分类（第三阶段）的组合，在所有其他组合中提供了最佳结果。这项工作还突显了基于TNF的谱嵌入在汽车保险数据集中的有效性。
英文摘要
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
发表机构
- Sri Sathya Sai Institute of Higher Learning（斯里萨提亚赛高等教育学院）
机构由 AI 辅助整理，请以论文原文为准。
