Optimizing bank credit risk assessment models using big data analytics

[1] Burns, C., Clifton, J., & Quaglia, L. (2018). Explaining policy change in the EU: financial reform after the crisis. Journal of European Public Policy, 25(5), 728-746. Search in Google Scholar

[2] Song, Z., & Xiong, W. (2018). Risks in China’s financial system. Annual review of financial economics, 10, 261-286. Search in Google Scholar

[3] Kharabsheh, B. (2019). Determinants of bank credit risk: Empirical evidence from Jordanian commercial banks. Academy of Accounting and Financial Studies Journal, 23(3), 1-12. Search in Google Scholar

[4] Markov, A., Seleznyova, Z., & Lapshin, V. (2022). Credit scoring methods: Latest trends and points to consider. The Journal of Finance and Data Science, 8, 180-201. Search in Google Scholar

[5] Lara-Rubio, J., Rayo-Cantón, S., Navarro-Galera, A., & Buendia-Carrillo, D. (2017). Analysing credit risk in large local governments: an empirical study in Spain. Local Government Studies, 43(2), 194-217. Search in Google Scholar

[6] Mukid, M. A., Widiharih, T., Rusgiyono, A., & Prahutama, A. (2018, May). Credit scoring analysis using weighted k nearest neighbor. In Journal of Physics: Conference Series (Vol. 1025, No. 1, p. 012114). IOP Publishing. Search in Google Scholar

[7] Ubarhande, P., & Chandani, A. (2021). Elements of credit rating: a hybrid review and future research Agenda. Cogent Business & Management, 8(1), 1878977. Search in Google Scholar

[8] Smuts, M., & Allison, J. (2020). An overview of survival analysis with an application in the credit risk environment. ORiON, 36(2). Search in Google Scholar

[9] Arsic, V. B. (2021). Challenges of financial risk management: AI applications. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, 26(3), 27-34. Search in Google Scholar

[10] Guerra, P., & Castelli, M. (2021). Machine learning applied to banking supervision a literature review. Risks, 9(7), 136. Search in Google Scholar

[11] Aslam, U., Tariq Aziz, H. I., Sohail, A., & Batcha, N. K. (2019). An empirical study on loan default prediction models. Journal of Computational and Theoretical Nanoscience, 16(8), 3483-3488. Search in Google Scholar

[12] Hossain, M. J. (2023). Implementation of Big Data Analytics in Credit Risk Management in the Banking and Financial Services Sector: A Contemporary Literature Review. Available at SSRN 4441658. Search in Google Scholar

[13] Medeiros Assef, F., & Arns Steiner, M. T. (2020). Ten-year evolution on credit risk research: a Systematic Literature Review approach and discussion. Ingeniería e investigación, 40(2), 50-71. Search in Google Scholar

[14] Gulsoy, N., & Kulluk, S. (2019). A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), e1299. Search in Google Scholar

[15] Ghenimi, A., Chaibi, H., & Omri, M. A. B. (2017). The effects of liquidity risk and credit risk on bank stability: Evidence from the MENA region. Borsa Istanbul Review, 17(4), 238-248. Search in Google Scholar

[16] Breeden, J. (2021). A survey of machine learning in credit risk. Journal of Credit Risk, 17(3). Search in Google Scholar

[17] Branzoli, N., & Supino, I. (2020). Fintech credit: A critical review of empirical research literature. Bank of Italy Occasional Paper, (549). Search in Google Scholar

[18] Tariq, H. I., Sohail, A., Aslam, U., & Batcha, N. K. (2019). Loan default prediction model using sample, explore, modify, model, and assess (SEMMA). Journal of Computational and Theoretical Nanoscience, 16(8), 3489-3503. Search in Google Scholar

[19] Lassoued, M. (2018). Comparative study on credit risk in Islamic banking institutions: The case of Malaysia. The Quarterly Review of Economics and Finance, 70, 267-278. Search in Google Scholar

[20] Bhatore, S., Mohan, L., & Reddy, Y. R. (2020). Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1), 111-138. Search in Google Scholar

[21] Win, S. (2018). What are the possible future research directions for bank’s credit risk assessment research? A systematic review of literature. International Economics and Economic Policy, 15(4), 743-759. Search in Google Scholar

[22] Zamore, S., Ohene Djan, K., Alon, I., & Hobdari, B. (2018). Credit risk research: Review and agenda. Emerging Markets Finance and Trade, 54(4), 811-835. Search in Google Scholar

[23] Çallı, B. A., & Coşkun, E. (2021). A longitudinal systematic review of credit risk assessment and credit default predictors. Sage Open, 11(4), 21582440211061333. Search in Google Scholar

[24] Naili, M., & Lahrichi, Y. (2022). The determinants of banks’ credit risk: Review of the literature and future research agenda. International Journal of Finance & Economics, 27(1), 334-360. Search in Google Scholar

[25] Sadok, H., Sakka, F., & El Maknouzi, M. E. H. (2022). Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance, 10(1), 2023262. Search in Google Scholar

[26] Addy, W. A., Ugochukwu, C. E., Oyewole, A. T., Ofodile, O. C., Adeoye, O. B., & Okoye, C. C. (2024). Predictive analytics in credit risk management for banks: A comprehensive review. GSC Advanced Research and Reviews, 18(2), 434-449. Search in Google Scholar

[27] Aduda, J., & Obondy, S. (2021). Credit risk management and efficiency of savings and credit cooperative societies: A review of literature. Journal of Applied Finance and Banking, 11(1), 99-120. Search in Google Scholar

[28] Twum, A. K., ZhongMing, T., Agyemang, A. O., Ayamba, E. C., & Chibsah, R. (2021). The impact of internal and external factors of credit risk on businesses: An empirical study of Chinese commercial banks. Journal of Corporate Accounting & Finance, 32(1), 115-128. Search in Google Scholar

[29] Wu, W. (2022). Credit risk measurement, decision analysis, transformation and upgrading for financial big data. Complexity, 2022(1), 8942773. Search in Google Scholar

[30] Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1), 29. Search in Google Scholar

[31] Cai Qianqian,Sun Yong,Huang Youpeng,Zhao Jingming,Li Jingru & Yi Shiqi.(2023).Malfunction diagnosis of main station of power metering system using LSTM-ResNet with SMOTE method.Journal of Computational Methods in Sciences and Engineering(5),2621-2633. Search in Google Scholar

[32] Syakiylla Sayed Daud Syarifah Noor,Sudirman Rubita & Wee Shing Tee.(2023).Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state.Biomedical Signal Processing and Control Search in Google Scholar

[33] Yue Yang Zhang,Jian Jun Zhao & Wei Liang.(2014).The Fault Diagnosis of Electric Power Metering System Based on Momentum BP Neural Network.Applied Mechanics and Materials(668-669),724-728. Search in Google Scholar

[34] Han Gong.(2024).Research on Enterprise Financial Risk based on BP Neural Network -- Taking Listed Manufacturing Companies in China as an Example.Scientific Journal of Economics and Management Research(4). Search in Google Scholar