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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
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    References listed on IDEAS

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    1. 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.
    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. 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.
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    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).

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