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False Invoicing Feature Identification and Risk Prediction

机译：虚假发票特征识别和风险预测

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With the rapid development of economy, the behavior of false invoicing by enterprises disturbs the tax order and even harms the national interests, which has become a hot issue of social concern. Tax authorities can crack down on enterprises' false invoicing according to risk characteristics. In this paper, we analyze these behavioral characteristics. By comparing the prediction performance of each algorithm e.g. Logistic Regression, Support Vector Machine, Decision Tree, BP neural network, Random Forest, Gradient Boosting Decision Tree and GBoost classification that based on historical case data, we select that the Random Forest which is the one with the highest prediction accuracy as the final model. We use extended data to deal with complex features, and propose a high-precision prediction model based on Random Forest, which is more intelligent and efficient than traditional ones, so as to provide accurate decision-making basis for the prediction of enterprises false invoicing.
机译：随着经济的快速发展，企业的错误发票的行为扰乱了税收令，甚至损害了国家利益，这已成为社会问题的热点问题。 根据风险特征，税务机关可以打击企业的虚假发票。 在本文中，我们分析了这些行为特征。 通过比较每种算法的预测性能。 Logistic回归，支持向量机，决策树，BP神经网络，随机森林，梯度提升决策树和GBoost分类，基于历史案例数据，我们选择随机森林，这是最高预测精度作为最终模型的森林 。 我们使用扩展数据来处理复杂的功能，并提出基于随机森林的高精度预测模型，这比传统森林更智能，更有效，以便为预测企业虚假发票提供准确的决策基础。

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