Improving the Quantification Model of Internal Control Impact on Banking Risks
Risk management in banking sector is a key issue
linked to financial system stability and its importance has been
elevated by technological developments and emergence of new
financial instruments. In this paper, we improve the model previously
defined for quantifying internal control impact on banking risks by
automatizing the residual criticality estimation step of FMECA. For
this, we defined three equations and a maturity coefficient to obtain
a mathematical model which is tested on all banking processes and
type of risks. The new model allows an optimal assessment of residual
criticality and improves the correlation rate that has become 98%.
[1] A. Dima and I. Orzea, Risk Management in Banking,
AcademyPublish.org.
[2] Anti-Money Laundering and Terrorist Financing Measures and Financial
Inclusion, FATF (Financial Action Task Force) Guidance, February 2013.
[3] M. McLeay, A. Radia and R. Thomas, Money creation in the modern
economy, Bank’s Monetary Analysis Directorate, Quarterly Bulletin 2014.
[4] V. Svat and M. Fleischmann, IS/IT risk management in banking industry,
University of Economics, Faculty of Informatics and Statistics, Prague.
[5] Basel Committee on Banking Supervision: Principles for Sound Liquidity
Risk Management and Supervision, Bank for International Settlement,
September 2008.
[6] Securities Markets Risk Outlook, IOSCO Research Department Staff,
2014-2015.
[7] Interest rate risk in the banking book, Basel Committee on Banking
Supervision, January 2001.
[8] T. Magnusson, A. Prasad and I. Storkey, Guidance for Operational Risk
Management in Government Debt Management, March 2011.
[9] J. Henry and C. Kok, A macro stress testing framework for assessing
systemic risks in the banking sector, European Central Banque,
Occasional Paper series No 152, October 2013.
[10] Policy and conceptual overview of the security risk management process,
Annexe A.
[11] Risk Assessment and Risk Management Methods:Information Packages
for Small and Medium Sized Enterprises (SMEs), ENISA ad hoc working
group on risk assessment and risk management, Deliverable 2, Final
version, Version 1.0, June 2006.
[12] E. Tonci and L. Zvonimir, Risk assessment of emergency plan
application, The International Emergency Management Society, 14th
Annual Conference, Trogir, Split, Croatia, June 2007.
[13] Residual Risk Assessment for the Pulp and Paper, EPA’s Office of Air
Quality Planning and Standards Office of Air and Radiation, December
2011.
[14] G. Tolbert, Residual Risk Reduction, Geogia, November 2005.
[15] A. Hindson, L. Drennan, J. Cain, L. Rieger, D. Melville, G. Lees,
S. Gillen and N. Mockett, Risk Appetite and Tolerance, Guidance paper
from the Institute of Risk Management, September 2011.
[16] D. Weese, Overview of risk assessment methods and applications,
Executive Director of AMGEC, June 2006.
[17] L. Lipol and J. Haq, Risk Analysis Method: FMEA/FMECA in the
Organizations, University of Boras, Sweden:IJBAS-IJENS Vol: 11 No:05,
2011.
[18] B. Jenkins, Risk Analysis helps establish a good security posture; Risk
Management keeps it that way, Countermeasures. Inc, 1998.
[19] T. Kanchu and M. Kumar, Risk management in banking sector: An
empirical study: ISSN 2277- 3622, February 2013.
[1] A. Dima and I. Orzea, Risk Management in Banking,
AcademyPublish.org.
[2] Anti-Money Laundering and Terrorist Financing Measures and Financial
Inclusion, FATF (Financial Action Task Force) Guidance, February 2013.
[3] M. McLeay, A. Radia and R. Thomas, Money creation in the modern
economy, Bank’s Monetary Analysis Directorate, Quarterly Bulletin 2014.
[4] V. Svat and M. Fleischmann, IS/IT risk management in banking industry,
University of Economics, Faculty of Informatics and Statistics, Prague.
[5] Basel Committee on Banking Supervision: Principles for Sound Liquidity
Risk Management and Supervision, Bank for International Settlement,
September 2008.
[6] Securities Markets Risk Outlook, IOSCO Research Department Staff,
2014-2015.
[7] Interest rate risk in the banking book, Basel Committee on Banking
Supervision, January 2001.
[8] T. Magnusson, A. Prasad and I. Storkey, Guidance for Operational Risk
Management in Government Debt Management, March 2011.
[9] J. Henry and C. Kok, A macro stress testing framework for assessing
systemic risks in the banking sector, European Central Banque,
Occasional Paper series No 152, October 2013.
[10] Policy and conceptual overview of the security risk management process,
Annexe A.
[11] Risk Assessment and Risk Management Methods:Information Packages
for Small and Medium Sized Enterprises (SMEs), ENISA ad hoc working
group on risk assessment and risk management, Deliverable 2, Final
version, Version 1.0, June 2006.
[12] E. Tonci and L. Zvonimir, Risk assessment of emergency plan
application, The International Emergency Management Society, 14th
Annual Conference, Trogir, Split, Croatia, June 2007.
[13] Residual Risk Assessment for the Pulp and Paper, EPA’s Office of Air
Quality Planning and Standards Office of Air and Radiation, December
2011.
[14] G. Tolbert, Residual Risk Reduction, Geogia, November 2005.
[15] A. Hindson, L. Drennan, J. Cain, L. Rieger, D. Melville, G. Lees,
S. Gillen and N. Mockett, Risk Appetite and Tolerance, Guidance paper
from the Institute of Risk Management, September 2011.
[16] D. Weese, Overview of risk assessment methods and applications,
Executive Director of AMGEC, June 2006.
[17] L. Lipol and J. Haq, Risk Analysis Method: FMEA/FMECA in the
Organizations, University of Boras, Sweden:IJBAS-IJENS Vol: 11 No:05,
2011.
[18] B. Jenkins, Risk Analysis helps establish a good security posture; Risk
Management keeps it that way, Countermeasures. Inc, 1998.
[19] T. Kanchu and M. Kumar, Risk management in banking sector: An
empirical study: ISSN 2277- 3622, February 2013.
@article{"International Journal of Business, Human and Social Sciences:72332", author = "M. Ndaw and G. Mendy and S. Ouya", title = "Improving the Quantification Model of Internal Control Impact on Banking Risks", abstract = "Risk management in banking sector is a key issue
linked to financial system stability and its importance has been
elevated by technological developments and emergence of new
financial instruments. In this paper, we improve the model previously
defined for quantifying internal control impact on banking risks by
automatizing the residual criticality estimation step of FMECA. For
this, we defined three equations and a maturity coefficient to obtain
a mathematical model which is tested on all banking processes and
type of risks. The new model allows an optimal assessment of residual
criticality and improves the correlation rate that has become 98%.", keywords = "Risk, Control, Banking, FMECA.", volume = "10", number = "2", pages = "615-6", }
