Equipping AI with an "Economic Sensor": Enabling Loan Prediction Models to Perceive Macroeconomic Changes

Traditional credit risk models, focusing primarily on borrower-specific features, often fail to capture the impact of macroeconomic fluctuations, leading to systemic misjudgment during downturns. This paper proposes enhancing loan prediction models by integrating an "economic sensor" a module incorporating key macroeconomic indicators (GDP growth, unemployment rate, interest rate, Housing Price Index, Industry Index) processed via a temporal sliding window mechanism. We develop an economic shock simulator for stress testing. Using a synthetically collected 5-year data set of loan applications and macroeconomic conditions, we train Random Forest models. Results show the enhanced model (individual and macro features) outperforms the baseline (individual features only), with accuracy increasing from 83.13% to 85.21% and AUC from 0.8993 to 0.9217. The model demonstrates heightened sensitivity to economic shocks, evidenced by a rightward shift in the predicted default probability distribution and an increase in the mean predicted default rate from 21.5% to 31.8% post a simulated 20% housing price crash. Crucially, it provides early warnings, identifying 2,873 clients (75% SMEs) with significantly increased risk 3-6 months post-shock. This approach enables more robust, economically-aware credit risk assessment.