Credit score value determination method and device, electronic equipment and storage medium
A determination method and credit scoring technology, applied in the field of machine learning, can solve problems such as the inability to guarantee the security of customer data privacy, and achieve the effect of improving the accuracy rate, improving the risk level, and ensuring privacy and security
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Embodiment 1
[0046] According to an embodiment of the present invention, an embodiment of a method for determining a credit score value is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and , although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
[0047] figure 1 is a flow chart of an optional method for determining a credit score value according to an embodiment of the present invention, such as figure 1 As shown, the method includes the following steps:
[0048] Step S101, based on the account ID of the target account, multiple account features of the target account are obtained to obtain a feature set, wherein each account feature corresponds to a feature value, and multiple feature values are binned to obtain multiple feature bins ...
Embodiment 2
[0125] figure 2 is a schematic diagram of an optional longitudinal federal credit scoring modeling process according to an embodiment of the present invention, such as figure 2 As shown, the modeling process of longitudinal federal credit scoring includes three parts: sample identity encryption alignment, federated learning modeling and federated scoring, as follows:
[0126] The first part is data set preparation and sample identity encryption alignment. Before starting to build a federated scoring model, each federated participant, that is, each scoring agency, needs to prepare its own data set, and then seek intersection through the private set (PrivateSet Intersection, PSI) to complete the encrypted alignment of the sample identification of both parties (the original data cannot be directly transmitted), and each participant forms a data set for model training and prediction. In this embodiment, it is assumed uniformly that the rating agency A provides the binary classi...
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