IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v52y2018i2d10.1007_s10614-017-9683-7.html
   My bibliography  Save this article

Multi Criteria Decision Making in Financial Risk Management with a Multi-objective Genetic Algorithm

Author

Listed:
  • Sujatha Srinivasan

    (VELS University)

  • T. Kamalakannan

    (VELS University)

Abstract

A huge amount of data is being collected and stored by financial institutions like banks during their operations. These data contain the most important facts about the institutions and its customers. A good and efficient data analytics system can find patterns in this huge data source that can be used in actionable knowledge creation. Actionable knowledge is the knowledge that can be put to decision making and take some positive action towards better performance of organizations. This actionable knowledge is termed Business Intelligence by data scientists. Business Intelligence and Analytics is the process of applying data mining techniques to organizational or corporate data to discover patterns. Business Intelligence and Business Analytics are emerging as important and essential fields both for data scientists and organizations. Risk analysis, fraud detection, customer retention, customer satisfaction analysis and actuarial analysis are some of the areas of application of business intelligence and analytics. Credit risk analysis is an important part of a successful financial institution particularly in the banking sector. The current study takes this risk analysis in financial institutions and reviews the state of the art in using data analytics or data mining techniques for financial risk analysis. The analysis of risk from financial data depends on several factors that are both objective and subjective. Hence it is a multi-criteria decision problem. The study also proposes a multi-objective genetic algorithm (MOGA) for analyzing financial data for risk analysis and prediction. The proposed MOGA is different from other evolutionary systems in that a memory component to hold the rules is added to the system while other systems in the literature are memory less. The algorithm is applied to bench mark data sets for predicting the decision on credit card and credit applications. The preliminary results are encouraging and show light towards better decision making in reducing risks.

Suggested Citation

  • Sujatha Srinivasan & T. Kamalakannan, 2018. "Multi Criteria Decision Making in Financial Risk Management with a Multi-objective Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 443-457, August.
    • Handle: RePEc:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9683-7
    DOI: 10.1007/s10614-017-9683-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-017-9683-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-017-9683-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rajkamal Iyer & Manju Puri & Nicholas Ryan, 2016. "A Tale of Two Runs: Depositor Responses to Bank Solvency Risk," Journal of Finance, American Finance Association, vol. 71(6), pages 2687-2726, December.
    2. Desheng Wu, 2016. "Risk management and operations research: a review and introduction to the special volume," Annals of Operations Research, Springer, vol. 237(1), pages 1-5, February.
    3. Sarlin, Peter, 2016. "Macroprudential oversight, risk communication and visualization," Journal of Financial Stability, Elsevier, vol. 27(C), pages 160-179.
    4. Aida Krichene Abdelmoula, 2015. "Bank Credit Risk Analysis with K-Nearest-Neighbor Classifier: Case of Tunisian Banks," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 14(1), pages 79-106, March.
    5. Desheng Wu, 2016. "Risk management and operations research: a review and introduction to the special volume," Annals of Operations Research, Springer, vol. 237(1), pages 1-5, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haelim Anderson & Daniel Barth & Dong Beom Choi, 2018. "Reducing Moral Hazard at the Expense of Market Discipline: The Effectiveness of Double Liability Before and During the Great Depression," Working Papers 18-06, Office of Financial Research, US Department of the Treasury.
    2. Atmaca, Sümeyra & Schoors, Koen & Verschelde, Marijn, 2020. "Bank loyalty, social networks and crisis," Journal of Banking & Finance, Elsevier, vol. 112(C).
    3. Danisewicz, Piotr & Lee, Chun Hei & Schaeck, Klaus, 2022. "Private deposit insurance, deposit flows, bank lending, and moral hazard," Journal of Financial Intermediation, Elsevier, vol. 52(C).
    4. International Association of Deposit Insurers, 2021. "Deposit Insurance Coverage Level and Scope," IADI Research Papers 21-12, International Association of Deposit Insurers.
    5. König-Kersting, Christian & Trautmann, Stefan T. & Vlahu, Razvan, 2022. "Bank instability: Interbank linkages and the role of disclosure," Journal of Banking & Finance, Elsevier, vol. 134(C).
    6. Hubert János Kiss, 2018. "Depositors’ Behaviour in Times of Mass Deposit Withdrawals," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 17(4), pages 95-111.
    7. Hubert Janos Kiss & Ismael Rodriguez-Lara & Alfonso Rosa-Garcia, 2018. "Who runs first to the bank?," CERS-IE WORKING PAPERS 1826, Institute of Economics, Centre for Economic and Regional Studies.
    8. Todd Messer, 2022. "Financial Failure and Depositor Quality: Evidence from Building and Loan Associations in California," International Finance Discussion Papers 1354, Board of Governors of the Federal Reserve System (U.S.).
    9. Carletti, Elena & Leonello, Agnese & Marquez, Robert, 2023. "Loan guarantees, bank underwriting policies and financial stability," Journal of Financial Economics, Elsevier, vol. 149(2), pages 260-295.
    10. Martin Brown & Stefan T. Trautmann & Razvan Vlahu, 2017. "Understanding Bank-Run Contagion," Management Science, INFORMS, vol. 63(7), pages 2272-2282, July.
    11. Roy Havemann, 2018. "Can creditor bail-in trigger contagion? The experience of an emerging market," Working Papers 755, Economic Research Southern Africa.
    12. Nicola Limodio & Francesco Strobbe, 2017. "Bank Deposits and Liquidity Regulation: Evidence from Ethiopia," Working Papers 612, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    13. Bonfim, Diana & Santos, João A.C., 2023. "The importance of deposit insurance credibility," Journal of Banking & Finance, Elsevier, vol. 154(C).
    14. Sune Ferreira & Zandri Dickason-Koekemoer, 2019. "The Relationship Between Depositor Behaviour and Risk Tolerance in a South African Context," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(3), pages 36-55, September.
    15. Markus Kinateder & Hubert János Kiss & Ágnes Pintér, 2020. "Would depositors pay to show that they do not withdraw? Theory and experiment," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 873-894, September.
    16. Kristian Blickle & Markus K. Brunnermeier & Stephan Luck, 2022. "Who Can Tell Which Banks Will Fail?," NBER Working Papers 29753, National Bureau of Economic Research, Inc.
    17. Nicolás de Roux & Nicola Limodio, 2023. "Deposit Insurance and Depositor Behavior: Evidence from Colombia," The Review of Financial Studies, Society for Financial Studies, vol. 36(7), pages 2721-2755.
    18. Schilling, Linda, 2017. "Optimal Forbearance of Bank Resolution," MPRA Paper 112409, University Library of Munich, Germany.
    19. Anderson, Haelim & Copeland, Adam, 2023. "Information management in times of crisis," Journal of Monetary Economics, Elsevier, vol. 136(C), pages 35-49.
    20. Haelim Anderson & Gary Richardson & Brian Yang, 2023. "Deposit Insurance and Depositor Monitoring: Quasi‐Experimental Evidence from the Creation of the Federal Deposit Insurance Corporation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 441-464, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9683-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.