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Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment

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  • Yuan Hong

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Shaojian Qu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Operational risk assessment has received considerable attention in bank risk management. However, current assessment methods are primarily designed to assess the risk profile of individual banks. To enable cross-bank operational risk assessment, we propose an integrated AHP-DEA (analytic hierarchy process–data envelopment analysis) method. This method determines the importance of assessment criteria by calculating the weighted sum of rank votes after obtaining the importance values for specific rankings with DEA. This procedure replaces the pairwise comparisons in AHP and addresses the challenge of traditional AHPs in determining appropriate importance values when dealing with a large number of indicators. We applied this method to assess the operational risks of three Chinese commercial banks, and the empirical results indicate that this integrated AHP-DEA method is simple and user-friendly, making it suitable for cross-bank operational risk assessment.

Suggested Citation

  • Yuan Hong & Shaojian Qu, 2024. "Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment," Mathematics, MDPI, vol. 12(7), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:968-:d:1363319
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    References listed on IDEAS

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    1. Dalla Valle, L. & Giudici, P., 2008. "A Bayesian approach to estimate the marginal loss distributions in operational risk management," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3107-3127, February.
    2. Liu, Fuh-Hwa Franklin & Hai, Hui Lin, 2005. "The voting analytic hierarchy process method for selecting supplier," International Journal of Production Economics, Elsevier, vol. 97(3), pages 308-317, September.
    3. Marco Moscadelli, 2004. "The modelling of operational risk: experience with the analysis of the data collected by the Basel Committee," Temi di discussione (Economic working papers) 517, Bank of Italy, Economic Research and International Relations Area.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Martin Neil & Norman Fenton & Manesh Tailor, 2005. "Using Bayesian Networks to Model Expected and Unexpected Operational Losses," Risk Analysis, John Wiley & Sons, vol. 25(4), pages 963-972, August.
    6. Andrew Sanford & Imad Moosa, 2015. "Operational risk modelling and organizational learning in structured finance operations: a Bayesian network approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(1), pages 86-115, January.
    7. Wade D. Cook & Moshe Kress, 1990. "A Data Envelopment Model for Aggregating Preference Rankings," Management Science, INFORMS, vol. 36(11), pages 1302-1310, November.
    8. Madjid Tavana & Mehdi Soltanifar & Francisco J. Santos-Arteaga, 2023. "Analytical hierarchy process: revolution and evolution," Annals of Operations Research, Springer, vol. 326(2), pages 879-907, July.
    9. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    10. Zhou, Xiaoping & Durfee, Antonina V. & Fabozzi, Frank J., 2016. "On stability of operational risk estimates by LDA: From causes to approaches," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 266-278.
    11. Han, Jinmian & Wang, Wei & Wang, Jiaqi, 2015. "POT model for operational risk: Experience with the analysis of the data collected from Chinese commercial banks," China Economic Review, Elsevier, vol. 36(C), pages 325-340.
    12. Valérie Chavez-Demoulin & Paul Embrechts & Marius Hofert, 2016. "An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(3), pages 735-776, September.
    13. Y M Wang & K S Chin & J B Yang, 2007. "Three new models for preference voting and aggregation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1389-1393, October.
    14. Kabir Dutta & Jason Perry, 2006. "A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital," Working Papers 06-13, Federal Reserve Bank of Boston.
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