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Optimal regulation of banking system’s advanced credit risk management by unified computational representation of business processes across the entire banking system

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  • Abdulrahman Alrabiah

Abstract

The impetus for this paper came after the financial crisis of 2007–2008, its global consequences and specifically how incomplete information “information asymmetry” between local banks and regulators extremely affected the banking sector. Financial institutions and regulators are—from a technical point of view–not yet fully integrated and standardised. The inaccuracy in banks’ data and the long (quarterly) intervals between reports to the regulators leads to delayed interventions by local supervisory regulators. Most regional banks use an internal ratings-based approach (IRB) that allows them to use their own methods to calculate credit risks, which makes it difficult for regulators to verify and validate the banks’ data without a standardised procedure and the benefit of fully automated connectivity for the regulatory reporting system through sophisticated IT tools. The importance of this issue, for the central banks, motivates the researcher to investigate and seek technology solutions in the interests of maximising the technical efficiency of the regulatory banking system. This paper is focused on the banking regulatory reporting system that uses IRB approach to evaluate credit risk. Due to the importance and the sensitivity of IRB approach on the banking credit risk assessments, a case study is examined and a tailored regulatory reporting system framework is proposed. The proposed framework integrates a private cloud computing network with standardised, automated and integrated features that would provide regulators and practitioners with a new method to enhance the regulatory reporting system.

Suggested Citation

  • Abdulrahman Alrabiah, 2018. "Optimal regulation of banking system’s advanced credit risk management by unified computational representation of business processes across the entire banking system," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1486685-148, January.
  • Handle: RePEc:taf:oaefxx:v:6:y:2018:i:1:p:1486685
    DOI: 10.1080/23322039.2018.1486685
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    Cited by:

    1. Laura Grassi & Davide Lanfranchi, 2022. "RegTech in public and private sectors: the nexus between data, technology and regulation," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 441-479, September.
    2. Abdulrahman Alrabiah & Steve Drew, 2020. "Proactive Management of Regulatory Policy Ripple Effects via a Computational Hierarchical Change Management Structure," Risks, MDPI, vol. 8(2), pages 1-29, May.
    3. Abdulrahman Alrabiah & Steve Drew, 2022. "A framework for managing regulatory policy life-cycle challenges: an empirical design," Journal of Banking Regulation, Palgrave Macmillan, vol. 23(2), pages 210-223, June.

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