IIARD International Institute of Academic Research and Development

WORLD JOURNAL OF FINANCE AND INVESTMENT RESEARCH (WJFIR )

E-ISSN 2550-7125
P-ISSN 2682-5902
VOL. 8 NO. 4 2024
DOI: 10.56201/wjfir.v8.no4.2024.pg69.88

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Enhancing Risk Management in Financial Institutions Through Big Data Analytics

Oloto, Ngozi U.

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Abstract

This study is on enhancing risk management in financial institutions through big data analytics. It delves into the impact of big data analytics on risk management practices within financial institutions, focusing on how these advanced techniques contribute to improving both the accuracy and timeliness of risk prediction models. By employing a comprehensive survey methodology, the research collected insights from 217 industry professionals to assess the effectiveness of big data analytics in enhancing risk management. The study reveals that a substantial proportion of respondents recognize big data analytics as a powerful tool for refining the precision of risk prediction models. Among the tools assessed, Apache Hadoop and Apache Spark are highlighted as particularly effective in processing and analyzing large datasets. Despite the overall positive assessment, the study identifies several persistent challenges, including issues related to data quality, integration complexities, and the high costs of implementation. To address these challenges, the study recommends several strategies: enhancing data quality and integration processes, investing significantly in employee training and development, and implementing robust data security measures to protect sensitive information. These findings offer actionable insights for financial institutions aiming to leverage big data analytics to optimize their risk management practices. They underscore both the significant benefits of these technologies and the critical strategies required for overcoming implementation challenges and achieving successful outcomes in risk management.

keywords:

Big Data Analytics (BDA), Risk Management, Credit Risk Modeling, Fraud Detection,

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References:

Berg, T., & Meggison, W. L. (2021). Machine learning in financial risk management: A survey.
Journal of Financial Data Science.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big
data to big impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
Chen, J., Zhou, X., & Li, Y. (2021). Real-time data analytics and risk management. Financial
Analysts Journal.
Chen, L., & Wang, M. (2024). Survey of risk management practices: The role of big data analytics
in financial institutions. International Journal of Financial Risk, 29(1), 98-115.
Cheng, H., Zhang, J., & Wang, X. (2022). Incorporating alternative data for credit risk assessment.
Journal of Risk Finance.
Gomez, A., & Rodriguez, T. (2023). The impact of real-time big data analysis on market risk
management. Financial Data Science Quarterly, 7(4), 112-130.
Ghosh, D., & Reilly, S. (2023). Anomaly detection and risk management with big data.
International Journal of Financial Engineering.
Gupta, R., & Kumar, S. (2022). The impact of big data analytics on operational risk management:
A quantitative analysis. International Journal of Financial Studies, 10(4), 112-129.
Guan, Y., Wang, H., & Zhang, M. (2023). Adaptive risk assessment models in big data
environments. Computational Economics.
Janssen, M., van der Voort, H., & Wahyudi, A. (2012). Factors influencing the use of data analytics
in public sector organizations. Journal of Information Technology & Politics, 9(1), 68-84.
https://doi.org/10.1080/19331681.2012.636176
Kim, S., & Choi, J. (2023). Longitudinal analysis of big data analytics in financial risk
management. Risk Management Journal, 22(2), 167-184.
Kara, A., & Petrescu, M. (2018). Self-employment and its relationship to subjective well-being.
International Review of Entrepreneurship, 16(1).
Ko?tuniuk, A., Witczak, I., M?ynarska, A., Czajor, K., & Uchmanowicz, I. (2021). Satisfaction
with life, satisfaction with job, and the level of care rationing among Polish nurses—A
cross-sectional
study.
Frontiers
in
Psychology,
https://doi.org/10.3389/fpsyg.2021.634814
Lapa, T. Y. (2013). Life satisfaction, leisure satisfaction, and perceived freedom of park recreation
participants. Procedia-Social and Behavioral Sciences, 93, 1985-1993.
Larsson, J. P., & Thulin, P. (2019). Independent by necessity? The life satisfaction of necessity
and opportunity entrepreneurs in 70 countries. Small Business Economics, 53(4), 921-934.
Lee, B., & Kim, Y. E. (2021). Factor structure and validation of the 12-item Korean version of the
General Health Questionnaire in a sample of early childhood teachers. Education Sciences,
11(5), 243. https://doi.org/10.3390/educsci11050243
Li, J., & Chen, Z. (2023). Enhancing risk management in financial institutions through big data
analytics: A case study approach. Journal of Financial Risk Management, 15(2), 234-250.
Park, M., & Lee, H. (2021). Machine learning in risk prediction: Insights from financial
institutions. Journal of Financial Technology, 9(3), 98-115.
Patel, R., & Kumar, S. (2022). Comparative analysis of risk management strategies: Traditional
vs. big data-driven approaches. Risk Management Review, 18(2), 77-89.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: A literature review and application
framework.
Computers
&
Operations
Research,
64,
87-97.
https://doi.org/10.1016/j.cor.2015.05.005
Zhang, Y., & Li, J. (2023). Machine learning models for enhancing credit risk assessment using
financial transaction data. Journal of Financial Analytics, 12(3), 45-62.

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