Financial Risk Management in Indonesian Banking: The Integrative Role of Data Analytics and Predictive Algorithms
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Abstract
This research delves into the realm of financial risk management within the Indonesian banking sector, with a focus on leveraging Data Analytics and Predictive Algorithms. Amidst the global financial market's complexities and the evolving nature of banking risks, this study aims to provide a comprehensive understanding of how advanced technological tools can enhance risk identification, evaluation, and management. Utilizing extensive datasets from the Indonesian Banking Statistics, Central Statistics Agency, and Bank Indonesia, the research explores the intricate relationship between various banking risks and macroeconomic factors. The study employs sophisticated predictive models to analyze data, focusing on credit and operational risks. The findings highlight the significant impact of macroeconomic variables on banking risks and the effectiveness of predictive models in risk assessment. The research contributes to the existing literature by offering a detailed analysis of the integration of machine learning and big data analytics in banking risk management. It also provides strategic insights for banks to adopt more dynamic, data-driven risk management strategies in the face of economic and industrial changes. The study underlines the importance of continuous innovation in technological applications to meet the evolving demands of the banking sector.
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References
Huber, C. and Scheytt, T., 2013. The dispositif of risk management: Reconstructing risk management after the financial crisis. Management Accounting Research, 24(2), pp.88-99. DOI: https://doi.org/10.1016/j.mar.2013.04.006.
Corelli, A., 2014. Understanding financial risk management. Routledge.
Choi, T.M., Chan, H.K. and Yue, X., 2016. Recent development in big data analytics for business operations and risk management. IEEE transactions on cybernetics, 47(1), pp.81-92. DOI: https://doi.org/10.1109/TCYB.2015.2507599.
Baryannis, G., Validi, S., Dani, S. and Antoniou, G., 2019. Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), pp.2179-2202. DOI: https://doi.org/10.1080/00207543.2018.1530476.
Ahmed, Z., Mohamed, K., Zeeshan, S. and Dong, X., 2020. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, p.baaa010. DOI: https://doi.org/10.1093/database/baaa010.
Sante, Z.V., Murni, S. and Tulung, J.E., 2021. Pengaruh Risiko Kredit, Risiko Likuiditas Dan Risiko Operasional Terhadap Profitabilitas Perusahaan Perbankan Yang Terdaftar Di Lq45, Buku Iii Dan Buku Iv Periode 2017-2019. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 9(3), pp.1451-1462. DOI: https://doi.org/10.35794/emba.v9i3.35784.
Widajatun, V.W. and Ichsani, S., 2019. The Impact of Credit, Liquidity, Operational and Market Risks on Return on Asset. Global Business and Management Research, 11(1), pp.335-340.
Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: Applications, prospects and challenges. Mobile big data: A roadmap from models to technologies, pp.3-20.
Mishra, N. and Silakari, S., 2012. Predictive analytics: A survey, trends, applications, oppurtunities & challenges. International Journal of Computer Science and Information Technologies, 3(3), pp.4434-4438.
Kelleher, J.D., Mac Namee, B. and D'arcy, A., 2020. Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.
Leo, M., Sharma, S. and Maddulety, K., 2019. Machine learning in banking risk management: A literature review. Risks, 7(1), p.29. DOI: https://doi.org/10.3390/risks7010029.
Peng, Y., Wang, G., Kou, G. and Shi, Y., 2011. An empirical study of classification algorithm evaluation for financial risk prediction. Applied Soft Computing, 11(2), pp.2906-2915. DOI: https://doi.org/10.1016/j.asoc.2010.11.028.
Dicuonzo, G., Galeone, G., Zappimbulso, E. and Dell'Atti, V., 2019. Risk management 4.0: The role of big data analytics in the bank sector. International Journal of Economics and Financial Issues, 9(6), pp.40-47.
Zhou, H., Sun, G., Fu, S., Liu, J., Zhou, X. and Zhou, J., 2019. A big data mining approach of PSO-based BP neural network for financial risk management with IoT. IEEE Access, 7, pp.154035-154043. DOI: https://doi.org/10.1109/ACCESS.2019.2948949.
Van Thiel, D. and Van Raaij, W.F.F., 2019. Artificial intelligence credit risk prediction: An empirical study of analytical artificial intelligence tools for credit risk prediction in a digital era. Journal of Risk Management in Financial Institutions, 12(3), pp.268-286.
Khemakhem, S. and Boujelbene, Y., 2018. Predicting credit risk on the basis of financial and non-financial variables and data mining. Review of accounting and finance, 17(3), pp.316-340. DOI: https://doi.org/10.1108/RAF-07-2017-0143.
Liang, Y., Quan, D., Wang, F., Jia, X., Li, M. and Li, T., 2020. Financial big data analysis and early warning platform: a case study. IEEE Access, 8, pp.36515-36526. DOI: https://doi.org/10.1109/ACCESS.2020.2969039.
Mashrur, A., Luo, W., Zaidi, N.A. and Robles-Kelly, A., 2020. Machine learning for financial risk management: a survey. IEEE Access, 8, pp.203203-203223. DOI: https://doi.org/10.1109/ACCESS.2020.3036322.