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Journal of Computational Social Dynamics

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Published

2021-08-13

Issue

Vol. 6 No. 3 (2021): Journal of Computational Social Dynamics-3rd-Q

Section

Articles

How to Cite

Xiaoli , W., & Nong, N. B. (2021). Evaluating Big Data Strategies for Risk Management in Financial Institutions. Journal of Computational Social Dynamics, 6(3), 34–45. Retrieved from https://vectoral.org/index.php/JCSD/article/view/44

Evaluating Big Data Strategies for Risk Management in Financial Institutions

Wu Xiaoli 

School of Finance, Renmin University of China, China 

Nguyen Bang Nong

Vietnam Academy of Social Sciences

Keywords: Risk Management, Big Data, Financial Institutions, Credit Risk, Market Risk, Operational Risk

Abstract

Financial institutions face numerous risks that can impact their operations and profitability. Managing these risks effectively is crucial for success. Big data strategies that leverage advanced analytics on large, diverse datasets can enhance risk management capabilities. This paper evaluates various big data approaches for improving risk management in financial institutions. It examines how big data strategies can be applied to major risk categories, including credit risk, market risk, liquidity risk, and operational risk. The paper discusses how predictive modelling, data mining, text analytics, and network analysis can provide deeper insights for credit risk management. Big data techniques like sentiment analysis, machine learning algorithms, and pattern detection are shown to strengthen capabilities for market risk measurement and monitoring. For liquidity risk, applications like granular cash flow forecasting, scenario modeling, text mining, and social media monitoring are assessed. Operational risk management can benefit from process mining, anomaly detection, external third-party data, and responsible employee misconduct surveillance. The advantages and limitations of different big data techniques are analysed through examples and data. The paper provides best practises for successfully implementing big data analytics for risk management. Strong executive sponsorship, specialised talent, technological infrastructure, developing a data-driven risk culture, ensuring model transparency, and aligning with risk strategy are critical success factors. Big data strategies must conform to privacy and ethical standards.  Big data can significantly enhance risk identification, measurement, and mitigation capabilities if deployed thoughtfully. This can help financial institutions make better risk decisions, avoid losses, comply with regulations, and gain a competitive advantage. But care must be taken to implement big data analytics responsibly and prudently.

Author Biography

Nguyen Bang Nong, Vietnam Academy of Social Sciences

 

 

 

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Submission Guidelines for the Journal of Computational Social Dynamics General Guidelines:

  1. Article Types: We accept a variety of article types including Original Research Articles, Review Papers, Short Reports, and Opinion Pieces.

  2. Language: Manuscripts must be written in English, and should be clear, concise, and free of jargon.

  3. Length: There is no strict word limit, but Original Research Articles should generally be between 4,000 and 10,000 words, including references.

  4. Formatting: Manuscripts should be submitted as a single Word or LaTeX document, formatted in double-spaced 12-point font.

  5. Abstract: An abstract of 250-300 words is required for all submissions, summarizing the key objectives, methods, findings, and implications of the research.

Manuscript Structure:

  • Title Page: Must include the title, author names, affiliations, and contact information for the corresponding author.

  • Introduction: Provide background, state the problem, and outline objectives.

  • Methods: Detail the methods and tools used in your research.

  • Results: Present the findings of your study.

  • Discussion: Interpret results, discuss their impact, and suggest future research directions.

  • Conclusion: Summarize the main points of the paper.

  • References: Citations should be formatted according to the APA/IEEE styles.

Ethical Guidelines:

Authors must ensure that their research adheres to ethical standards and that informed consent has been obtained for any human or animal subjects involved.

Plagiarism:

The Journal of Computational Social Dynamics uses plagiarism detection software. Any manuscript found to include plagiarized content will be rejected.

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All submissions undergo a double-blind peer review process. Authors can expect initial feedback within six to eight weeks.

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For any questions or concerns, please contact the editorial office at editorial@vectoral.org. Thank you for considering the Journal of Computational Social Dynamics as a venue for your research.

Published by Vectoral Publishing, the Journal of Computational Social Dynamics maintains high standards in peer-review and scientific inquiry. Authors who wish to submit manuscripts should adhere to the guidelines detailed on our website, www.vectoral.org/jcsd. Vectoral Publishing does not bear any responsibility for the accuracy of information or for any outcomes related to the use of the research published. For editorial correspondence and submissions, please reach out to editorial@vectoral.org.