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Credit and Loan Approval Classification Using a Bio-Inspired Neural Network.

Spyridon D Mourtas1,2, Vasilios N Katsikis1, Predrag S Stanimirović2,3

  • 1Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece.

Biomimetics (Basel, Switzerland)
|February 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bio-inspired algorithm (BWASD) to improve bank loan and credit approval processes. It enhances machine learning efficiency, reducing risks and saving bank resources.

Keywords:
Moore-Penrose inversebeetle antennae searchloan approval classificationneural networksweights and structure determination

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Finance

Background:

  • The banking industry faces challenges in loan and credit approval due to increasing applications and limited assets.
  • Accurate risk assessment is crucial for efficient resource allocation and minimizing financial losses.
  • Traditional methods struggle with the complexity and scale of modern credit scoring.

Purpose of the Study:

  • To develop an advanced machine learning model for efficient and accurate credit and loan approval.
  • To address the limitations of conventional neural networks, such as slow training and local minima.
  • To introduce a novel bio-inspired algorithm that enhances the learning process for binary classification problems in finance.

Main Methods:

  • Development of a novel weights and structure determination (WASD) neural network.
  • Creation of a bio-inspired WASD algorithm for binary classification problems (BWASD).
  • Integration of the metaheuristic beetle antennae search (BAS) algorithm to optimize the WASD learning procedure.

Main Results:

  • The BWASD algorithm demonstrates superior performance and adaptability compared to conventional back-propagation neural networks.
  • The proposed model effectively handles the unique characteristics of credit and loan approval tasks.
  • Theoretical and experimental studies confirm the enhanced efficiency and reduced risk in applicant selection.

Conclusions:

  • The BWASD algorithm offers a significant advancement in machine learning for financial risk assessment.
  • This bio-inspired approach provides a more robust and efficient solution for credit and loan approval systems.
  • A comprehensive MATLAB package is available to support the implementation and further research.