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Novel embedding model predicting the credit card's default using neural network optimized by harmony search algorithm

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  • 1Department of Educational Technology, Hulunbuir University, Hulunbuir, 021008, China.

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Optimizing credit card default prediction models with advanced algorithms like Multi-verse Optimization (MVO) significantly improves accuracy. This research enhances financial stability by addressing volatile credit card default data for better risk management.

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Area of Science:

  • Financial technology
  • Machine learning
  • Optimization algorithms

Background:

  • Credit card usage is integral to modern finance, driving economic growth but also increasing default risks.
  • Volatile and imbalanced credit card default data pose challenges for traditional prediction models.
  • Existing optimization algorithms struggle to provide stable and optimal solutions for credit card default prediction.

Purpose of the Study:

  • To evaluate and compare the performance of four optimization algorithms (Whale Optimization Algorithm, Harmony Search, Multi-verse Optimization, Vortex Search) for credit card default prediction.
  • To enhance the performance of Artificial Neural Network (ANN) models through parameter tuning using these optimization algorithms.
  • To identify the most effective optimization approach for mitigating credit card default risks.

Main Methods:

  • Applied four distinct optimization algorithms: Whale Optimization Algorithm (WOA), Harmony Search (HS), Multi-verse Optimization (MVO), and Vortex Search (VS) to tune ANN models.
  • Assessed twenty-three parameters for model optimization.
  • Evaluated model efficacy using Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) metrics, comparing against models trained on original data.

Main Results:

  • Multi-verse Optimization (MVO) demonstrated the highest training accuracy among the evaluated algorithms.
  • The optimized models achieved improved Area Under the Curve (AUC) values compared to baseline models.
  • Specifically, MVO-MLP achieved AUC values of 0.7469 (training) and 0.7329 (testing), outperforming WOA-MLP, HS-MLP, and VS-MLP.

Conclusions:

  • The study highlights the significant potential of advanced optimization algorithms, particularly MVO, in improving credit card default prediction accuracy.
  • The proposed methodology offers a robust solution for managing default probabilities in the credit card industry.
  • Implementing these optimized models can lead to enhanced financial stability and risk management for financial institutions.