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Reserve Fund Optimatization Model For Digital Banking Transaction Risk With Extreme Value-at-risk Constraints

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dc.contributor.authorMoch Panji Agung Saputraen_US
dc.contributor.authorDiah Chaeranien_US
dc.contributor.authorSukonoen_US
dc.contributor.authorMazlynda Md Yusufen_US
dc.date.accessioned2024-05-29T02:28:48Z
dc.date.available2024-05-29T02:28:48Z
dc.date.issued2023
dc.date.submitted2024-1-31
dc.descriptionMathematics Volume 11 Issue 3507 Page (1-16)en_US
dc.description.abstractThe digitalization of bank data and financial operations creates a large risk of loss. Losses due to the risk of errors in the bank’s digital system need to be mitigated through the readiness of reserve funds. The determination of reserve funds needs to be optimized so that there is no large excess of reserve funds. Then the rest of the reserve fund allocation can be used as an investment fund by the bank to obtain additional returns or profits. This study aims to optimize the reserve fund allocation for digital banking transactions. In this case, the decision variable is value reserved based on potential loss of each digital banking, and the objective function is defined as minimizing reserve fund allocation. Furthermore, some conditions that become limitation are rules of Basel II, Basel III, and Article 71 paragraph 1 of the Limited Liability Company Law. Since the objective function can be expressed as a linear function, in this paper, linear programming optimization approach is thus employed considering Extreme Value-at-Risk (EVaR) constraints. In the use of EVaR approach in the digital banking problem, it is found that the loss meets the criteria of extreme data based on the Generalized Pareto Distribution (GPD). The strength of reserve funds using linear programming optimization with EVaR constraints is the consideration of potential losses from digital banking risks that are minimized so that the allocation of company funds becomes optimum. While the determination of reserve funds with a standard approach only considers historical profit data, this can result in excessive reserve funds because they are not considered potential risks in the future period. For the numerical experiment, the following risk data are used in the modeling, i.e., the result of a sample simulation of digital banking losses due to the risk of system downtime, system timeout, external failure, and operational user failure. Therefore, the optimization model with EVaR constraints produces an optimal reserve fund value, so that the allocation of bank reserve funds becomes efficient. This provides a view for banking companies to avoid the worst risk, namely collapse due to unbalanced mandatory reserve funds.en_US
dc.identifier.citation: Saputra, M.P.A.; Chaerani, D.; Sukono; Md. Yusuf, M. Reserve Fund Optimization Model for Digital Banking Transaction Risk with Extreme Value-at-Risk Constraints. Mathematics 2023, 11, 3507. https:// doi.org/10.3390/math11163507en_US
dc.identifier.doi10.3390/math11163507
dc.identifier.epage16
dc.identifier.issn2227-7390
dc.identifier.issue3507
dc.identifier.spage1
dc.identifier.urihttps://www.mdpi.com/search?sort=pubdate&page_no=12&page_count=50&year_from=1996&year_to=2024&journal=mathematics&view=default
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10782
dc.identifier.volume11
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.relation.ispartofMathematicsen_US
dc.subjectdigital banking; reserve fund; optimization; EVaRen_US
dc.titleReserve Fund Optimatization Model For Digital Banking Transaction Risk With Extreme Value-at-risk Constraintsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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