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Research on financial consumer behavior based on deep Learning

机译:基于深度学习的金融消费者行为研究

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At present, the judgment of users' financial literacy is mainly based on users' default records and loan information, but the research on reflecting users' financial literacy through users' consumption behavior is limited. Based on the transaction data of an internet financial company, a fusion model is established for a variety of machine learning algorithms, and the relationship between the consumer behavior and the credit risk of the user is established. At the same time, based on the characteristics of artificial feature selection and information gain of tree model, the main consumption behaviors that affect users' credit risk are obtained, which provides a new idea for judging users' financial literacy through studying and judging users' consumption behaviors.
机译:目前,用户的金融扫盲判断主要基于用户的默认记录和贷款信息,但通过用户消费行为反映用户的金融扫盲的研究有限。 基于互联网金融公司的交易数据,为各种机器学习算法建立了融合模型,建立了消费者行为与用户的信用风险之间的关系。 同时,基于人工特征选择的特征和树模型的信息增益,获得影响用户信用风险的主要消费行为,通过学习和判断用户提供了评估用户的金融扫盲的新想法 消费行为。

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