Credit business recommendation method based on federal learning
A business recommendation and credit technology, applied in the information field, can solve problems such as the inability of enterprises to recommend the best credit business products, and achieve the effect of recommending credit business products accurately, improving accuracy, and improving privacy and security.
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Embodiment 1
[0047] A federated learning-based credit business recommendation method, please refer to the appendix figure 1 ,include:
[0048]Step A01) Establish a trusted server, and the trusted server establishes a credit business display page to display credit business products of several financial institutions for the enterprise to choose. The types of credit business products include credit loans, mortgage loans, letters of guarantee and discounted bills. There are long-term loans, medium and long-term loans and short-term loans in time. In addition, the interest rate calculation methods and repayment methods of credit products are different from each other. In this regard, enterprises need to compare among many credit products and choose the most suitable credit product for their own situation. Enterprises need to spend a lot of time to obtain and compare credit products, which is very time-consuming and labor-intensive. A trusted server is established to centrally display credit ...
Embodiment 2
[0082] A method for recommending a credit business based on federated learning, this embodiment provides specific protection for enterprise information on the basis of the first embodiment. In this embodiment, when the enterprise requests the trusted server to view the credit service display page, the trusted server generates a temporary asymmetric encryption key pair for the enterprise, which is recorded as a public key and a private key respectively. The public key is sent to the enterprise, and the enterprise encrypts the enterprise information with the public key and sends it to the trusted server. The trusted server decrypts the enterprise information with the private key. Then, the enterprise information and the credit business classification information of each credit business product are input into the final neural network model after preprocessing to obtain the recommendation level.
[0083] Please see attached Figure 5 , the process of the trusted server generatin...
Embodiment 3
[0108] A method for recommending a credit business based on federated learning. On the basis of the first embodiment, this embodiment further provides a specific method for exchanging obfuscation values with higher security. Please see attached Figure 8 , several financial institutions generate mutually offset obfuscated values including:
[0109] Step F01) The financial institution generates a random positive odd number, calculates the cosine value of the random positive odd number, and discloses the cosine value;
[0110] Step F02) Each financial institution is paired with other financial institutions in turn, and the cosine value of the product of random positive and odd numbers generated by the paired two financial institutions is calculated using the double angle formula of the cosine function;
[0111] Step F03) Take the first preset decimal of the cosine value as the absolute value of the confusion value, and determine the positive and negative signs of the confus...
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