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Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method

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  • Moch Panji Agung Saputra

    (Master of Mathematics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Sukono

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Diah Chaerani

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

Abstract

The application of industry 4.0 in banking presents many challenges, with several operational risks related to downtime and timeout services due to system failures. One of the operational risk management steps is to estimate the value of the maximum potential losses. The purpose of this study is to estimate the maximum potential losses for digital banking transaction risks. The method used for estimating risks is the EVaR method. There are several steps in this study. The first step is to resample the data using MEBoot. This process is a simulation of the operational risk loss data of digital banking. Next, the threshold value is determined to obtain the extreme data value. Then, a Kolmogorov–Smirnov test is conducted to fit the data with the GPD. Afterward, the GPD parameter is estimated. Then, EVaR is calculated using a portfolio approach to obtain a combination of risk values as maximum potential losses. The analysis results show that the maximum potential loss is IDR144,357,528,750.94. The research results imply that the banks need to pay attention to the maximum potential losses of digital financial transactions as a reference for risk management. Therefore, banks can anticipate the adequacy of reserve funds for these potential risks.

Suggested Citation

  • Moch Panji Agung Saputra & Sukono & Diah Chaerani, 2022. "Estimation of Maximum Potential Losses for Digital Banking Transaction Risks Using the Extreme Value-at-Risks Method," Risks, MDPI, vol. 10(1), pages 1-18, January.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:1:p:10-:d:716531
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    References listed on IDEAS

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    1. Muteba Mwamba, John & Mhlanga, Isaah, 2013. "Extreme conditional value at risk: a coherent scenario for risk management," MPRA Paper 64387, University Library of Munich, Germany.
    2. Beccalli, Elena, 2007. "Does IT investment improve bank performance? Evidence from Europe," Journal of Banking & Finance, Elsevier, vol. 31(7), pages 2205-2230, July.
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    Cited by:

    1. Carlin C. F. Chu & Simon S. W. Li, 2024. "A multiobjective optimization approach for threshold determination in extreme value analysis for financial time series," Computational Management Science, Springer, vol. 21(1), pages 1-14, June.
    2. Moch Panji Agung Saputra & Diah Chaerani & Sukono & Mazlynda Md. Yusuf, 2023. "Reserve Fund Optimization Model for Digital Banking Transaction Risk with Extreme Value-at-Risk Constraints," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    3. Fatemeh Fattahi & Ali Hadi & Mohammad Afzalinejad & Farhad Hosseinzadeh Lotfi, 2024. "A Modified Network DEA Model for Bank Efficiency Analysis Considering Risk Factors," SN Operations Research Forum, Springer, vol. 5(4), pages 1-24, December.

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