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The Role of Big Data, Data Science and Data Analytics in Financial Engineering

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Published:11 June 2019Publication History

ABSTRACT

Financial engineering is the process of creating innovative solutions for the existing financial problems of a company by using applications of mathematical methods. Financial engineering uses tools and knowledge from the fields of computer science, big data, data science, data analytics, statistics, economics and applied mathematics to address current financial issues as well as to devise new and innovative financial products. Financial Engineering is helpful in derivative pricing, financial regulation, execution, corporate finance, portfolio management, risk management, trading of structured products. Therefore, financial engineering is used by Commercial Banks, Investment Banks, Insurance companies and other fund hedging agencies. The present study focus on the role of big data, data science and data analytics in financial engineering as a successful tool at all stages of insurance business management practices. How these insurance companies are using said three data tools effectively as fasteners of financial engineering for the successful design, development and implementation of innovative business processes and products in this competitive and ever-changing insurance market with innovative product features and strategies.

References

  1. Kumar, N. (2015, March 13). New age fraud analytics: Machine learning on Ha-doop. Retrieved from https://www.mapr.com/blog/new-age-fraud-analytics-machine-learning-hadoop#.Va1Iv_kQjm4Google ScholarGoogle Scholar
  2. Fairless, T. (2015, April 16). Big Data Looms as Next Battle in Europe - WSJ. Retrieved from http://www.wsj.com/articles/big-data-looms-as-next-battle-in-europe-1429217668Google ScholarGoogle Scholar
  3. SAS. (n.d.). What Is Big Data? Retrieved from http://www.sas.com/en_us/insights/big-data/what-is-big-data.htmlGoogle ScholarGoogle Scholar
  4. Derrig R. A., "Insurance fraud," Journal of Risk and Insurance, vol. 69.3, pp. 271--287, 2002Google ScholarGoogle ScholarCross RefCross Ref
  5. H.Lookman Sithic, T. Balasubramanian, "Survey of Insurance Fraud Detection Using Data Mining Techniques", International Journal of Innovative Technology and Exploring Engineering, Vol-2, Issue-3, February 2013.Google ScholarGoogle Scholar
  6. "Insurance Industry, challenges reforms and alignment", Confederation of Indian Industry.Google ScholarGoogle Scholar
  7. Nanthawadee Sucharittham, Thanaruk heeramunkong Choochart Haruechaiyasak, Bao Tu Ho, Dam Hieu Chi, "Data Mining for Life Insurance Knowledge Extraction: A Survey".Google ScholarGoogle Scholar
  8. Dietrich, D. (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. EMC Education Services Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Gartner, I. (n.d.). IT Glossary: Big Data. Retrieved from http://www.gartner.com/it-glossary/big-data/Google ScholarGoogle Scholar
  10. IBM. (2013). Bringing big data to the enterprise. Retrieved from What is big data?: https://www-01.ibm.com/software/data/bigdata/what-is-big-data.htmlGoogle ScholarGoogle Scholar
  11. Leenen, L. and Meyer, T., (2019). Artificial Intelligence and Big Data Analytics in Support of Cyber Defense. In Developments in Information Security and Cybernetic Wars (pp. 42--63). IGI Global.Google ScholarGoogle ScholarCross RefCross Ref
  12. Koti, M. S., & Alamma, B. H. (2019). Predictive Analytics Techniques Using Big Data for Healthcare Databases. In Smart Intelligent Computing and Applications (pp. 679--686). Springer, Singapore.Google ScholarGoogle ScholarCross RefCross Ref
  13. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data--evolution, challenges and research agenda. International Journal of Information Management, 48, 63--71.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Ahmed, M., Choudhury, S., & Al-Turjman, F. (2019). Big Data Analytics for Intelligent Internet of Things. In Artificial Intelligence in IoT (pp. 107--127). Springer, Cham.Google ScholarGoogle ScholarCross RefCross Ref

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        cover image ACM Other conferences
        BDE '19: Proceedings of the 2019 International Conference on Big Data Engineering
        June 2019
        137 pages
        ISBN:9781450360913
        DOI:10.1145/3341620

        Copyright © 2019 ACM

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        Publication History

        • Published: 11 June 2019

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