AI随机森林模型显著提升阿塞拜疆小企业信贷风险评估准确率
Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs
- 用随机森林替代传统德尔菲法进行信贷评分
- 各项指标从0.69~0.58提升至0.83~0.79
- 适合金融风控、政策制定者参考
本研究探讨了机器学习随机森林模型在阿塞拜疆中小企业信贷风险评分中的应用效果，相较于传统德尔菲模型，其准确率、精确率、召回率和F-1分数分别从0.69、0.65、0.56和0.58提升至0.83、0.81、0.77和0.79。数据来源于本地金融机构，因缺乏公开的中小企业数据，无法独立验证。研究使用Python实现算法并进行对比分析。结果表明，采用AI模型可显著提高识别潜在违约者的能力，降低金融机构信用风险。同时，减少对小企业的不公平拒贷，有助于经济可持续增长。但需关注算法透明性及历史数据偏见等伦理问题，避免对算法的机械依赖。
原文摘要 · Abstract (English)
The research investigates how the application of a machine-learning random forest model improves the accuracy and precision of a Delphi model. The context of the research is Azerbaijani SMEs and the data for the study has been obtained from a financial institution which had gathered it from the enterprises (as there is no public data on local SMEs, it was not practical to verify the data independently). The research used accuracy, precision, recall and F-1 scores for both models to compare them and run the algorithms in Python. The findings showed that accuracy, precision, recall and F- 1 all improve considerably (from 0.69 to 0.83, from 0.65 to 0.81, from 0.56 to 0.77 and from 0.58 to 0.79, respectively). The implications are that by applying AI models in credit risk modeling, financial institutions can improve the accuracy of identifying potential defaulters which would reduce their credit risk. In addition, an unfair rejection of credit access for SMEs would also go down having a significant contribution to an economic growth in the economy. Finally, such ethical issues as transparency of algorithms and biases in historical data should be taken on board while making decisions based on AI algorithms in order to reduce mechanical dependence on algorithms that cannot be justified in practice.
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