人工智能在开发银行风险指数中的作用:基于自适应神经网络的模糊推理系统 (ANFIS) 的应用
Ibrahim Elsiddig Ahmed1, Riyadh Mehdi2, Elfadil A Mohamed2
1College of Business Administration, Member of AI and DT Research Centers, Ajman University, Ajman, UAE.
Artificial intelligence review
|June 26, 2023
概括
本研究介绍了用于银行风险评估的Mahalanobis距离 (MD) 指数,并确定了影响银行风险的关键金融比率. 调查结果显示,净利息利率显著影响风险,大多数海湾银行显示出良好的风险状况.
科学领域:
- 银行和金融风险管理
- 量化金融 量化金融
- 计量经济学 计量经济学
背景情况:
- 有效的银行风险测量和管理对于金融稳定至关重要.
- 现有的方法可能无法充分捕捉银行风险的多维性质.
- 政策制定者和管理者需要强大的风险评估工具.
研究的目的:
- 提出使用马哈拉诺比斯距离 (MD) 的新风险排名指数.
- 确定各种金融比率在评估银行风险方面的相对重要性.
- 分析45家海湾银行在2016-2020年间的风险概况.
主要方法:
- 利用Mahalanobis距离 (MD) 来创建一个多维风险指数.
- 采用了自适应神经模糊推理系统 (ANFIS) 来评估比重的重要性.
- 分析了五个风险领域的十个金融比率:资本充足性,信贷,流动性,收入质量和运营风险.
主要成果:
- 建立了MD的信任水平门:4.82 (99%),4.28 (95%) 和4.0 (90%).
- 在分析的45家海湾银行中,大多数银行被发现处于稳定的风险地位.
- 净利率率被确定为最重要的风险因素,其次是资本充足率,普通股一级资本和一级股本.
结论:
- 拟议的MD指数为银行风险排名提供了有价值的工具.
- 净利息保证金是银行风险的主要驱动因素.
- 该研究为加强银行监管和风险管理策略提供了见解.
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