Innovative Application of Artificial Intelligence Algorithms in Credit Scoring: Taking specific products in the field of consumer finance as the research object
DOI:
https://doi.org/10.65455/aair5612关键词:
Artificial Intelligence Algorithm, Credit Score, Consumer Finance, Deep Learning, Model Interpretability摘要
This paper focuses on specific products in consumer finance and explores the innovative application of artificial intelligence algorithms in credit scoring. By analyzing the technical foundation and current situation, this paper expounds the core technological innovations such as deep learning, tree model integration, and graph neural networks, and solves the problems of data fusion, model optimization, and interpretability. Empirical research shows that artificial intelligence algorithms outperform traditional models in multiple indicators.
参考
[1]KHALIL A M, PADMANABHAN R, HADID M, et al. AI driven transformation in trade finance: A roadmap for automating letter of credit document examination. Digital Business, 2025, 5(2): 100130.
[2]PARUL A, MANSI G, ABHIRUPA R, et al. Artificial intelligence and credit application processing: the role of embarrassment. European Journal of Marketing, 2025, 59(4): 923–950.
[3]PHILIP M M, ANINDITA P. Toward an evolving framework for responsible AI for credit scoring in the banking industry. Journal of Information, Communication and Ethics in Society, 2025, 23(1): 148–163.
[4]JEWEL K R, LÁSZLÓ V. Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management: A Systematic Literature Review. International Journal of Business Analytics (IJBAN), 2024, 11(1): 1–23.
[5]TIGGES M, MESTWERDT S, TSCHIRNER S, et al. Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data. Technological Forecasting & Social Change, 2024, 205: 123491.
[6]M. F T, ABDUSSALAM A, MAHMOUD B, et al. Toward interpretable credit scoring: integrating explainable artificial intelligence with deep learning for credit card default prediction. Neural Computing and Applications, 2023, 36(9): 4847–4865.
[7]UDO M. Is algorithmic credit scoring a ‘high risk’?. Journal of Digital Banking, 2023, 7(3): 249–265.
[8]HICHAM S, FADI S, HADI E M M E. Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance, 2022, 10(1).
[9]EL A Q, MARIA T, NATALIA R D, et al. Correction to: Feature contribution alignment with expert knowledge for artificial intelligence credit scoring. Signal, Image and Video Processing, 2022, 17(4): 1743.
[10]RAVALI K J, SRIKANTH I. Responsible AI in automated credit scoring systems. AI and Ethics, 2022, 3(2): 485–495.
[11]AYOUB Q E, MARIA T, NATALIA R D, et al. Feature contribution alignment with expert knowledge for artificial intelligence credit scoring. Signal, Image and Video Processing, 2022, 17(2): 427–434.
[12]GERALD S. Algorithms, credit scoring, and the new proposals of the EU for an AI Act and on a Consumer Credit Directive. Law and Financial Markets Review, 2021, 15(3-4): 239–261.
[13]NIKITA A. The norms of algorithmic credit scoring. The Cambridge Law Journal, 2021, 80(1): 42–73.
[14]Artificial Intelligence; Study Data from University Putra Malaysia Update Understanding of Artificial Intelligence (Hybrid Harmony Search-Artificial Intelligence Models in Credit Scoring). Robotics & Machine Learning, 2020: 2816.
[15]YING R G, SOON L L, HSINVONN S, et al. Hybrid Harmony Search-Artificial Intelligence Models in Credit Scoring. Entropy (Basel, Switzerland), 2020, 22(9).