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Credit Default Prediction Based on Improved Smote Algorithm and GA_LightGBM Algorithm

Published: 27 January 2023 Publication History

Abstract

In the big data environment, customer information is lengthy and complex, and lenders need to connect with users on the Internet. Therefore, higher requirements are required for the identification of customers. In view of the above problems, this paper proposes a credit default detection model based on improved smote algorithm and GA_LightGBM algorithm to detect customer credit default behavior. The experimental results show that the model has a good prediction effect in the aspect of user credit default.

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ICIIP '22: Proceedings of the 7th International Conference on Intelligent Information Processing
September 2022
367 pages
ISBN:9781450396714
DOI:10.1145/3570236
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 27 January 2023

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Author Tags

  1. Detection Model
  2. GA_LightGBM Algorithm
  3. Improve SMOTE
  4. User Credit Default

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ICIIP '22

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Overall Acceptance Rate 87 of 367 submissions, 24%

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