Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive models (traditional statistical and machine learning algorithms), this study aims to examine the macroeconomic determinants impact on the UK banking credit risk and assess the most accurate credit risk estimate using predictive analytics. This study found that the variance-based multi-split decision tree algorithm is the most precise predictive model with interpretable, reliable, and robust results. Our model performance achieved 95% accuracy and evidenced that unemployment and inflation rate are significant credit risk predictors in the UK banking context. Our findings provided valuable insights such as a positive association between credit risk and inflation, the unemployment rate, and national savings, as well as a negative relationship between credit risk and national debt, total trade deficit, and national income. In addition, we empirically showed the relationship between national savings and non-performing loans, thus proving the paradox of thrift. These findings benefit the credit risk management team in monitoring the macroeconomic factors thresholds and implementing critical reforms to mitigate credit risk.

翻译：宏观经济因素对银行信用风险具有关键影响，但这些因素无法由银行直接控制。因此，亟需构建基于宏观经济的早期信用风险预警系统。本研究通过对比不同预测模型（传统统计模型与机器学习算法），深入探究宏观经济决定因素对英国银行业信用风险的影响，并运用预测分析技术评估最精确的信用风险估计方法。研究发现，基于方差的多元分裂决策树算法是最准确的预测模型，其结果具有可解释性、可靠性及稳定性。该模型性能达到95%的准确率，并证实失业率和通货膨胀率是英国银行业信用风险的重要预测指标。研究结果揭示了以下关键发现：信用风险与通货膨胀率、失业率及国民储蓄呈正相关，而与国债规模、贸易逆差总额及国民收入呈负相关。此外，我们通过实证分析证明了国民储蓄与不良贷款之间的关联，从而验证了"节俭悖论"的存在。这些发现有助于信用风险管理部门监测宏观经济因素的阈值变化，并实施关键改革以降低信用风险。