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

Jiaming Ma 1*
1 Xi’an Jiaotong University
*Corresponding author: molecule@stu.xjtu.edu.cn
Published on 24 September 2025
Volume Cover
TNS Vol.132
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-80590-305-5
ISBN (Online): 978-1-80590-306-2
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Abstract

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.

Keywords:

Credit risk modeling, Macroeconomic indicators, Machine learning, Credit risk modeling, Predictive analytic

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Ma,J. (2025). Equipping AI with an "Economic Sensor": Enabling Loan Prediction Models to Perceive Macroeconomic Changes. Theoretical and Natural Science,132,7-16.

References

[1]. Altman, E.I. (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.

[2]. Hand, D.J., and Henley, W.E. (1997) Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523-541.

[3]. Daniele, F., et al. (2022) The myopia of machine learning models in finance: A case for macroeconomic integration. Journal of Financial Data Science, 4(2), 45-62.

[4]. Jiménez, G., and Mencia, J. (2009) Modelling the distribution of credit losses with observable and latent factors. Journal of Empirical Finance, 16(2), 235-253.

[5]. Rajan, R.G. (2005) Has financial development made the world riskier? NBER Working Paper No. 11728.

[6]. Brunnermeier, M.K. (2009) Deciphering the liquidity and credit crunch 2007-2008. Journal of Economic Perspectives, 23(1), 77-100.

[7]. Gourinchas, P.O., et al. (2021) COVID-19 and SME failures. American Economic Review: Insights, 3(4), 465-482.

[8]. Hastie, T., Tibshirani, R., and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York.

[9]. Figini, S., and Fantazzini, D. (2019) Macroeconomic credit risk: A Bayesian model averaging approach. Journal of Banking & Finance, 106, 367-378.

[10]. Breeden, J.L. (2007) Reinventing retail credit modelling: The emergence of scenario-based modelling. Journal of Risk Model Validation, 1(3), 1-26.

[11]. Koopman, S.J., and Lucas, A. (2005) Business and default cycles for credit risk. Journal of Applied Econometrics, 20(2), 311-323.

[12]. Breiman, L. (2001) Random forests. Machine Learning, 45(1), 5-32.

[13]. Bergmeir, C., and Benítez, J.M. (2012) On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213.

[14]. Niculescu-Mizil, A., and Caruana, R. (2005) Predicting good probabilities with supervised learning. Proceedings of the 22nd International Conference on Machine Learning (ICML '05), 625-632.

[15]. Fawcett, T. (2006) An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

[16]. Basel Committee on Banking Supervision (2019) Stress testing principles. Bank for International Settlements.

[17]. Merton, R.C. (1974) On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449-470.

[18]. Beck, T., Demirgüç-Kunt, A., and Levine, R. (2005) SMEs, growth, and poverty: Cross-country evidence. Journal of Economic Growth, 10(3), 199-229.

References

[1]. Altman, E.I. (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.

[2]. Hand, D.J., and Henley, W.E. (1997) Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523-541.

[3]. Daniele, F., et al. (2022) The myopia of machine learning models in finance: A case for macroeconomic integration. Journal of Financial Data Science, 4(2), 45-62.

[4]. Jiménez, G., and Mencia, J. (2009) Modelling the distribution of credit losses with observable and latent factors. Journal of Empirical Finance, 16(2), 235-253.

[5]. Rajan, R.G. (2005) Has financial development made the world riskier? NBER Working Paper No. 11728.

[6]. Brunnermeier, M.K. (2009) Deciphering the liquidity and credit crunch 2007-2008. Journal of Economic Perspectives, 23(1), 77-100.

[7]. Gourinchas, P.O., et al. (2021) COVID-19 and SME failures. American Economic Review: Insights, 3(4), 465-482.

[8]. Hastie, T., Tibshirani, R., and Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York.

[9]. Figini, S., and Fantazzini, D. (2019) Macroeconomic credit risk: A Bayesian model averaging approach. Journal of Banking & Finance, 106, 367-378.

[10]. Breeden, J.L. (2007) Reinventing retail credit modelling: The emergence of scenario-based modelling. Journal of Risk Model Validation, 1(3), 1-26.

[11]. Koopman, S.J., and Lucas, A. (2005) Business and default cycles for credit risk. Journal of Applied Econometrics, 20(2), 311-323.

[12]. Breiman, L. (2001) Random forests. Machine Learning, 45(1), 5-32.

[13]. Bergmeir, C., and Benítez, J.M. (2012) On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192-213.

[14]. Niculescu-Mizil, A., and Caruana, R. (2005) Predicting good probabilities with supervised learning. Proceedings of the 22nd International Conference on Machine Learning (ICML '05), 625-632.

[15]. Fawcett, T. (2006) An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

[16]. Basel Committee on Banking Supervision (2019) Stress testing principles. Bank for International Settlements.

[17]. Merton, R.C. (1974) On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449-470.

[18]. Beck, T., Demirgüç-Kunt, A., and Levine, R. (2005) SMEs, growth, and poverty: Cross-country evidence. Journal of Economic Growth, 10(3), 199-229.

Cite this article

Ma,J. (2025). Equipping AI with an "Economic Sensor": Enabling Loan Prediction Models to Perceive Macroeconomic Changes. Theoretical and Natural Science,132,7-16.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-APMM 2025 Symposium: Simulation and Theory of Differential-Integral Equation in Applied Physics

ISBN: 978-1-80590-305-5(Print) / 978-1-80590-306-2(Online)
Editor: Marwan Omar, Shuxia Zhao
Conference website: https://www.confapmm.org/dalian.html
Conference date: 27 September 2025
Series: Theoretical and Natural Science
Volume number: Vol.132
ISSN: 2753-8818(Print) / 2753-8826(Online)

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