信用和贷款批准分类使用生物灵感的神经网络
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
概括
本研究介绍了一种新的生物灵感算法 (BWASD),用于改进银行贷款和信贷审批流程. 它提高了机器学习的效率,降低了风险,节省了银行资源.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算金融是指计算金融.
背景情况:
- 银行业在贷款和信贷审批方面面临挑战,原因是申请人数增加和资产有限.
- 准确的风险评估对于有效地分配资源和最大限度地减少财务损失至关重要.
- 传统的方法难以应对现代信用评分的复杂性和规模.
研究的目的:
- 开发一个先进的机器学习模型,以实现高效准确的信贷和贷款批准.
- 解决传统神经网络的局限性,例如缓慢的训练和局部最小值.
- 引入一种新的生物灵感算法,增强金融二元分类问题的学习过程.
主要方法:
- 开发一种新的重量和结构确定 (WASD) 神经网络.
- 为二进制分类问题 (BWASD) 创建一个生物启发的WASD算法.
- 整合元启发式虫天线搜索 (BAS) 算法,以优化WASD学习过程.
主要成果:
- 与传统的反向传播神经网络相比,BWASD算法表现出卓越的性能和适应性.
- 拟议的模型有效地处理信用和贷款批准任务的独特特征.
- 理论和实验研究证实了申请人选择的提高效率和降低风险.
结论:
- 根据BWASD算法,机器学习在金融风险评估方面取得了重大进展.
- 这种生物灵感的方法为信贷和贷款批准系统提供了更强大,更有效的解决方案.
- 一个全面的 MATLAB 包可用于支持实施和进一步研究.
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