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首页> 外文会议>IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development >Implementation of a Predictive Model for Fraud Detection in Motor Insurance using Gradient Boosting Method and Validation with Actuarial Models
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Implementation of a Predictive Model for Fraud Detection in Motor Insurance using Gradient Boosting Method and Validation with Actuarial Models

机译：基于梯度提升法和精算模型验证的车险欺诈检测预测模型的实现

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摘要

Machine Learning provides greater ability to identify in-depth patterns in the data that are normally invisible or difficult to identify using other methods. One of the major application of it is seen in insurance claims fraud detection, which is a classification problem. In this work, Gradient Boosting Method (GBM) was used to create a predictive model which was applied to motor insurance claims data. The dataset was highly imbalanced; this problem was addressed using Synthetic Minority Oversampling Technique (SMOTE). The results achieved were remarkable with F1 score around 98% and the accuracy 99%. This was cross-validated by industry experts using extreme value theory (EVT), an actuarial model. The predictive model presented in this paper can be customized, tested and extended to other lines of business.
机译：机器学习提供了更高的能力，可以识别数据中的深度模式，这些模式通常是不可见的或难以使用其他方法识别。它的主要应用之一是在保险索赔欺诈检测中，这是一个分类问题。在这项工作中，使用梯度提升方法（GBM）创建了预测模型，该模型已应用于汽车保险索赔数据。数据集高度不平衡;使用综合少数族裔过采样技术（SMOTE）解决了此问题。 F1分数约为98％，准确性为99％，所获得的结果非常出色。行业专家使用精算模型极值理论（EVT）对此进行了交叉验证。本文中介绍的预测模型可以定制，测试并扩展到其他业务领域。

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