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Credit score model training method, credit score calculation method, apparatus and server

机译:信用评分模型训练方法,信用评分计算方法,装置及服务器

摘要

Embodiments of the present invention relate to the field of credit assessment, and disclose a credit score model training method, and a credit score calculation method, apparatus, and server. The method includes: obtaining an i th credit score, an i th relational credit score, and default annotation information of a sampled user; training a credit score model according to the i th credit score, the i th relational credit score, and the default annotation information of the sampled user, and calculating an (i+1) th credit score of the sampled user; and obtaining the completely trained credit score model when a training termination condition is satisfied, so that a problem that when personal information of a user is missing or incorrect, it is very difficult to accurately calculate a credit score of the user is resolved; and a target credit score of the user is comprehensively calculated by using the credit score of the user and a relational credit score that is calculated based on a social relation of the user, thereby achieving effects of expanding coverage of the credit score model, and improving accuracy of calculating the credit score of the user.
机译:本发明实施例涉及信用评估领域,公开了一种信用评分模型训练方法,信用评分计算方法,装置和服务器。该方法包括:获取被采样用户的第i个信用评分,第i个关系信用评分和默认注释信息;根据第i个信用评分,第i个关系信用评分和被采样用户的默认注释信息,训练一个信用评分模型,并计算被采样用户的第(i + 1)个信用评分;当满足训练终止条件时,获得完全训练的信用评分模型,从而解决了当用户的个人信息丢失或不正确时,很难准确计算出用户的信用评分的问题。通过使用用户的信用分数和基于用户的社会关系计算出的关系信用分数,综合计算出用户的目标信用分数,从而达到扩大信用分数模型覆盖面,提高信用度的效果。计算用户信用评分的准确性。

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