DETERMINAN PROBABILITY OF DEFAULT DALAM PERHITUNGAN EXPECTED CREDIT LOSS PERBANKAN
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Abstract
This study aims to analyze the influence of the credit risk profile, credit growth target and the macro economy (GDP, exchange rate and inflation) on the probability of default in generating expected credit loss as stipulated in PSAK 71. The research was conducted at PT Bank X during the 2016-2020 observation using multiple linear regression analysis. The results of this study state that the credit risk profile, credit growth target and the exchange rate have a positive and significant effect on the probability of default (PD), while GDP and inflation have a significant negative effect on PD. The research results imply that the implementation of good credit risk management is expected to reduce the rate of default which can be caused by the credit growth target and macroeconomic conditions, especially GDP and exchange rates which have a significant effect on defaults which will ultimately affect the formation of expected credit loss (ECL) in the financial statements.
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Hartanto, A. D., & Setijaningsih, H. T. (2023). DETERMINAN PROBABILITY OF DEFAULT DALAM PERHITUNGAN EXPECTED CREDIT LOSS PERBANKAN . Akurasi : Jurnal Studi Akuntansi Dan Keuangan, 6(1), 157-176. https://doi.org/10.29303/akurasi.v6i1.329
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References
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Agus, S., Irwanto, A.K. and Maulana, T.T.N.A. (2014). The Analysis of Financial Ratios Effect to Default Probability of Four Banks in LQ 45 Group at BEI.
Al-Tamimi, H.A.H. (2002), “Risk management practices: an empirical analysis of the UAE commercial banks”, Finance India, Vol. 16 No. 3, pp. 1045-1057.
Azeez, A.A dan Ekanayake E.M.N.N, 2015. “Determinants of Non Performing Loan in Licensed Commercial Banks : Evidence From Srilanka”. Journal of Asian Economic and Financial Review, Vol. 5(6). p. 868-882.
Bank for International Settlements (1999), “A new capital adequacy framework”, Consultative paper issued by Basel Committee on Banking Supervision, available at: www.bis.org/publ/bcbs50.pdf (accessed November 20, 2016).
Berger, A.N.; DeYoung, R. Problem loans and cost efficiency in commercial banks. J. Bank. Financ. 1997, 21, 849–870.
Bergh, D. D., Connelly, B. L., Ketchen, D. J., & Shannon, L. M. (2014). Signalling theory and equilibrium in strategic management research: An assessment and a research agenda. Journal of Management Studies. https://doi.org/10.1111/joms.12097
Damanhur, Albra, W., Syamni, G., & Habibie, M. (2018). What is the Determinant of Non-Performing Financing in Branch Sharia Regional Bank in Indonesia (pp. 265–271). https://doi.org/10.1108/978-1-78756-793-1-00081
Dar, A.A., Anuradha, N. and Qadir, S. (2019). Estimating probabilities of default of different firms and the statistical tests, Journal of Global Entrepreneurship Research, Vol. 9 No. 1, pp. 1-15.
Dechow, P.M. and Dichev, I.D. (2002). The Quality of Accruals and Earnings. The Role of Accrual Estimation Errors.
Deloitte. (2018). Applying the expected credit loss model to trade receivables using a provision matrix. available at: https://www.iasplus.com/en/publications/global/a-closer-look/provisionmatrix/at_download/file/FINAL147785%20IFRS%209%20Provision%20Matrix%20v3.pdf.
Eisenhardt, K. M. (1989). Agency Theory: An Assessment and Review. https://www.jstor.org/stable/258191
Farkas, Z.N. (2016). The Interaction of the IFRS 9 Expected Loss Approach with Supervisory Rules and Implications for Financial Stability. pp. 1.
Ghosh, Amith, 2015. “Banking-industry specific and regional economic determinants ofnon- performing loans: Evidence from US states”. Journal of Financial Stability Vol.20. p 93– 104.
Ginting, A. M. (2017). Pengaruh Makroekonomi Terhadap Non Performing Loan (NPL) di Indonesia: Studi Non Performing Loan Perbankan. Jurnal Ekonomi Dan Kebijakan Publik, 7(2), 159. https://doi.org/10.22212/jekp.v7i2.669.
Greuning, H. and Bratanovic, S.B. (2003), Analyzing and Managing Banking Risk: A Framework for Assessing Corporate Governance and Financial Risk, 2nd ed., The World Bank, Washington, DC.
Hamid, M. K., & Rahman, F. (2019). Impact of Macroeconomic Variables on Non-performing Loan in Banking Sector of Bangladesh. 8, 157–168.
Hassan, A. (2009), “Risk management practices of Islamic banks of Brunei Darussalam”, The Journal of Risk Finance, Vol. 10 No. 1, pp. 23-37.
Ikatan Akuntan Indonesia (2016). http://iaiglobal.or.id/berita-kegiatan/detailberita-1011=pengesahan-psak-71-psak-72-dan-amendemen-psak-62.
Jensen, M. and Meckling, W. (1976): "Theory of the firm: managerial behavior, agency costs and ownership structure", Journal of Financial Economics 3, 305–360.
Keeton, W. R. (1999). Does Faster Loan Growth Lead to Higher Loan Losses? Economic Review Federal Reserve Bank of Kansas City.
KPMG (2018), IFRS 9: transition impact on banks in the UAE and gulf cooperation council. https://home.kpmg/om/en/home/insights/2018/11/ifrs-9–transition-impact-onbanks-in-the-gulf.html.
Krugman, P.R., & Wells, R. (2021). Macroeconomics. http://public.eblib.com/choice/PublicFullRecord.aspx?p=6631216
Leka, B., Bajrami, E., & Duci, E. (2019). Key Macroeconomic Drivers on Reducing Non Performing Loans in Albania. Academic Journal of Interdisciplinary Studies; Vol 8, No 2 (2019): July 2019. https://www.mcser.org/journal/index.php/ajis/article/view/10460
Lim, C.Y., Lim, C.Y. and Lobo, G.J. (2013). IAS 39 Reclassification Choice and Analyst Earnings Forecast Properties, Journal of Accounting and Public Policy, Vol. 31 No. 5, pp. 121-134.
Lubis, D. and Mulyana, B. (2021). The Macroeconomic Effects on Non-Performing Loan and its Implication on Allowance for Impairment Losses, pp. 21.
Mankiw, N. G. (2006). Pengantar ekonomi makro terjemahan Criswan Sungkono (R. Widyaningrum (Ed.); 3rd ed.). Salemba Empat.
Mazreku, I., Morina, F., Misiri, V., Spiteri, J., & Grima, S. (2018). Determinants of the Level of Non-Performing Loans in Commercial Banks of Transition Countries. European Research Studies Journal, XXI. https://doi.org/10.35808/ersj/1040.
Mileris, R. (2014). MACROECONOMIC FACTORS OF NON-PERFORMING LOANS IN COMMERCIAL BANKS. Ekonomika, 93, 22–39. https://doi.org/10.15388/Ekon.2014.0.3024.
Naibaho, K., & Rahayu, S. M. (2018). Pengaruh GDP, Inflasi, Bi Rate, Nilai Tukar Terhadap Non Performing Loan Bank Umum Konvensional Di Indonesia (Studi Pada Bank Umum Konvensional Yang Terdaftar Di Bursa Efek Indonesia Periode 2012-2016). Jurnal Administrasi Bisnis, 16(2).
Nurismalatri, N. (2014). Analisis Pengaruh Makro Ekonomi Terhadap Kredit Bermasalah Perbankan Indonesia. Jurnal Sekuritas, Saham, Ekonomi Keuangan & Investasi, 1(2), 103–116. https://doi.org/http://dx.doi.org/10.32493/skt.v1i2.750.
Rizal, A., Zulham, T., & Asmawati, A. (2019). Analisis Pengaruh Pertumbuhan Ekonomi, Inflasi, dan Suku Bunga Terhadap Kredit Macet di Indonesia. Jurnal Ekonomi Dan Kebijakan Publik Indonesia, 6(1), 1–16. https://doi.org/10.24815/ekapi.v6i1.14255
Rizky, M., Qodarina, N. & Firmansyah, A. (2022). Manajemen Laba Sebelum dan Setelah Penerapan PSAK 71 pada Perusahaan Subsektor Perbankan di Indonesia.https://owner.polgan.ac.id/index.php/owner/article/view/706.
Rose, Peter, S., Hudgins, Conway (2008). Bank Management and Financial Services. http://repository.vnu.edu.vn/handle/VNU_123/90225.
Simons, D. & Rolwes, F. (2009). Macroeconomic Default Modeling and Stress Testing. https://www.ijcb.org/journal/ijcb09q3a6.htm.
Sinaga, J. S., Muda, I., & Silalahi, A. S. (2020). The Effect of BI Rate, Exchange Rate, Inflation and Third Party Fund (DPK) on Credit Distribution and Its Impact on Non Performing Loan (NPL) on XYZ Commercial Segment Bank. Universal Journal of Accounting and Finance, 8(3), 55–64. https://doi.org/10.13189/ujaf.2020.080301.
Sloan, R.G. (1996). Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?. The Accounting Review 71 (July): 289-315.
Sugiarto & Suroso (2020). Innovation of Impairment Loss Allowance Model of Indonesian Financial Accounting Standards 71. https://www.emerald.com/insight/2515-964X.htm.
Syahid, D. C. N. (2016). Pengaruh Faktor Eksternal dan Internal Terhadap Kredit Bermasalah Serta Dampaknya Terhadap CKPN Menurut PSAK 55. Perbanas Review, 2(1).
Szarowská, I. (2018). Effect of macroeconomic determinants on non-performing loans in Central and Eastern European countries. International Journal of Monetary Economics and Finance, 11, 20. https://doi.org/10.1504/IJMEF.2018.090564.
Tambunan, M.O., & Hanggraeni, W. (2022). Probability of Default (PD) per Province to Estimate a More Granular Impairment Credit Loss for Bank ABC.
Walton, P. (2004), IAS 39: where different accounting models collide”, Accounting in Europe, Vol. 15 No. 7, pp. 76-86.
Setya Wijaya, R. (2019). Pengaruh Faktor Makro Ekonomi terhadap Kredit Bermasalah pada Bank Umum di Indonesia. OECONOMICUS Journal of Economics, 4(1), 36–48. https://doi.org/10.15642/oje.2019.4.1.36-48.
Vaněk, T., & Hampel, D. (2017). The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2), 759–776. https://doi.org/10.11118/actaun201765020759.
Yang, B.H. (2017). Point-in-time PD term structure models for multi-period scenario loss projection: Methodologies and implementations for IFRS 9 ECL and CCAR stress testing. https://mpra.ub.uni-muenchen.de/76271/
Yogiswari, N., & Badera, I. (2019). Pengaruh Board Diversity Pada Nilai Perusahaan Dalam Perspektif Corporate Governance. E-Jurnal Akuntansi, 26(3), 2070 - 2097. doi:10.24843/EJA.2019.v26.i03.p15.
Agić, Z., & Jeremić, Z. (2018). Macroeconomic and specific banking determinants of nonperforming loans in Bosnia and Herzegovina. Industrija, 46, 45–60. https://doi.org/10.5937/industrija46-14956.
Agus, S., Irwanto, A.K. and Maulana, T.T.N.A. (2014). The Analysis of Financial Ratios Effect to Default Probability of Four Banks in LQ 45 Group at BEI.
Al-Tamimi, H.A.H. (2002), “Risk management practices: an empirical analysis of the UAE commercial banks”, Finance India, Vol. 16 No. 3, pp. 1045-1057.
Azeez, A.A dan Ekanayake E.M.N.N, 2015. “Determinants of Non Performing Loan in Licensed Commercial Banks : Evidence From Srilanka”. Journal of Asian Economic and Financial Review, Vol. 5(6). p. 868-882.
Bank for International Settlements (1999), “A new capital adequacy framework”, Consultative paper issued by Basel Committee on Banking Supervision, available at: www.bis.org/publ/bcbs50.pdf (accessed November 20, 2016).
Berger, A.N.; DeYoung, R. Problem loans and cost efficiency in commercial banks. J. Bank. Financ. 1997, 21, 849–870.
Bergh, D. D., Connelly, B. L., Ketchen, D. J., & Shannon, L. M. (2014). Signalling theory and equilibrium in strategic management research: An assessment and a research agenda. Journal of Management Studies. https://doi.org/10.1111/joms.12097
Damanhur, Albra, W., Syamni, G., & Habibie, M. (2018). What is the Determinant of Non-Performing Financing in Branch Sharia Regional Bank in Indonesia (pp. 265–271). https://doi.org/10.1108/978-1-78756-793-1-00081
Dar, A.A., Anuradha, N. and Qadir, S. (2019). Estimating probabilities of default of different firms and the statistical tests, Journal of Global Entrepreneurship Research, Vol. 9 No. 1, pp. 1-15.
Dechow, P.M. and Dichev, I.D. (2002). The Quality of Accruals and Earnings. The Role of Accrual Estimation Errors.
Deloitte. (2018). Applying the expected credit loss model to trade receivables using a provision matrix. available at: https://www.iasplus.com/en/publications/global/a-closer-look/provisionmatrix/at_download/file/FINAL147785%20IFRS%209%20Provision%20Matrix%20v3.pdf.
Eisenhardt, K. M. (1989). Agency Theory: An Assessment and Review. https://www.jstor.org/stable/258191
Farkas, Z.N. (2016). The Interaction of the IFRS 9 Expected Loss Approach with Supervisory Rules and Implications for Financial Stability. pp. 1.
Ghosh, Amith, 2015. “Banking-industry specific and regional economic determinants ofnon- performing loans: Evidence from US states”. Journal of Financial Stability Vol.20. p 93– 104.
Ginting, A. M. (2017). Pengaruh Makroekonomi Terhadap Non Performing Loan (NPL) di Indonesia: Studi Non Performing Loan Perbankan. Jurnal Ekonomi Dan Kebijakan Publik, 7(2), 159. https://doi.org/10.22212/jekp.v7i2.669.
Greuning, H. and Bratanovic, S.B. (2003), Analyzing and Managing Banking Risk: A Framework for Assessing Corporate Governance and Financial Risk, 2nd ed., The World Bank, Washington, DC.
Hamid, M. K., & Rahman, F. (2019). Impact of Macroeconomic Variables on Non-performing Loan in Banking Sector of Bangladesh. 8, 157–168.
Hassan, A. (2009), “Risk management practices of Islamic banks of Brunei Darussalam”, The Journal of Risk Finance, Vol. 10 No. 1, pp. 23-37.
Ikatan Akuntan Indonesia (2016). http://iaiglobal.or.id/berita-kegiatan/detailberita-1011=pengesahan-psak-71-psak-72-dan-amendemen-psak-62.
Jensen, M. and Meckling, W. (1976): "Theory of the firm: managerial behavior, agency costs and ownership structure", Journal of Financial Economics 3, 305–360.
Keeton, W. R. (1999). Does Faster Loan Growth Lead to Higher Loan Losses? Economic Review Federal Reserve Bank of Kansas City.
KPMG (2018), IFRS 9: transition impact on banks in the UAE and gulf cooperation council. https://home.kpmg/om/en/home/insights/2018/11/ifrs-9–transition-impact-onbanks-in-the-gulf.html.
Krugman, P.R., & Wells, R. (2021). Macroeconomics. http://public.eblib.com/choice/PublicFullRecord.aspx?p=6631216
Leka, B., Bajrami, E., & Duci, E. (2019). Key Macroeconomic Drivers on Reducing Non Performing Loans in Albania. Academic Journal of Interdisciplinary Studies; Vol 8, No 2 (2019): July 2019. https://www.mcser.org/journal/index.php/ajis/article/view/10460
Lim, C.Y., Lim, C.Y. and Lobo, G.J. (2013). IAS 39 Reclassification Choice and Analyst Earnings Forecast Properties, Journal of Accounting and Public Policy, Vol. 31 No. 5, pp. 121-134.
Lubis, D. and Mulyana, B. (2021). The Macroeconomic Effects on Non-Performing Loan and its Implication on Allowance for Impairment Losses, pp. 21.
Mankiw, N. G. (2006). Pengantar ekonomi makro terjemahan Criswan Sungkono (R. Widyaningrum (Ed.); 3rd ed.). Salemba Empat.
Mazreku, I., Morina, F., Misiri, V., Spiteri, J., & Grima, S. (2018). Determinants of the Level of Non-Performing Loans in Commercial Banks of Transition Countries. European Research Studies Journal, XXI. https://doi.org/10.35808/ersj/1040.
Mileris, R. (2014). MACROECONOMIC FACTORS OF NON-PERFORMING LOANS IN COMMERCIAL BANKS. Ekonomika, 93, 22–39. https://doi.org/10.15388/Ekon.2014.0.3024.
Naibaho, K., & Rahayu, S. M. (2018). Pengaruh GDP, Inflasi, Bi Rate, Nilai Tukar Terhadap Non Performing Loan Bank Umum Konvensional Di Indonesia (Studi Pada Bank Umum Konvensional Yang Terdaftar Di Bursa Efek Indonesia Periode 2012-2016). Jurnal Administrasi Bisnis, 16(2).
Nurismalatri, N. (2014). Analisis Pengaruh Makro Ekonomi Terhadap Kredit Bermasalah Perbankan Indonesia. Jurnal Sekuritas, Saham, Ekonomi Keuangan & Investasi, 1(2), 103–116. https://doi.org/http://dx.doi.org/10.32493/skt.v1i2.750.
Rizal, A., Zulham, T., & Asmawati, A. (2019). Analisis Pengaruh Pertumbuhan Ekonomi, Inflasi, dan Suku Bunga Terhadap Kredit Macet di Indonesia. Jurnal Ekonomi Dan Kebijakan Publik Indonesia, 6(1), 1–16. https://doi.org/10.24815/ekapi.v6i1.14255
Rizky, M., Qodarina, N. & Firmansyah, A. (2022). Manajemen Laba Sebelum dan Setelah Penerapan PSAK 71 pada Perusahaan Subsektor Perbankan di Indonesia.https://owner.polgan.ac.id/index.php/owner/article/view/706.
Rose, Peter, S., Hudgins, Conway (2008). Bank Management and Financial Services. http://repository.vnu.edu.vn/handle/VNU_123/90225.
Simons, D. & Rolwes, F. (2009). Macroeconomic Default Modeling and Stress Testing. https://www.ijcb.org/journal/ijcb09q3a6.htm.
Sinaga, J. S., Muda, I., & Silalahi, A. S. (2020). The Effect of BI Rate, Exchange Rate, Inflation and Third Party Fund (DPK) on Credit Distribution and Its Impact on Non Performing Loan (NPL) on XYZ Commercial Segment Bank. Universal Journal of Accounting and Finance, 8(3), 55–64. https://doi.org/10.13189/ujaf.2020.080301.
Sloan, R.G. (1996). Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?. The Accounting Review 71 (July): 289-315.
Sugiarto & Suroso (2020). Innovation of Impairment Loss Allowance Model of Indonesian Financial Accounting Standards 71. https://www.emerald.com/insight/2515-964X.htm.
Syahid, D. C. N. (2016). Pengaruh Faktor Eksternal dan Internal Terhadap Kredit Bermasalah Serta Dampaknya Terhadap CKPN Menurut PSAK 55. Perbanas Review, 2(1).
Szarowská, I. (2018). Effect of macroeconomic determinants on non-performing loans in Central and Eastern European countries. International Journal of Monetary Economics and Finance, 11, 20. https://doi.org/10.1504/IJMEF.2018.090564.
Tambunan, M.O., & Hanggraeni, W. (2022). Probability of Default (PD) per Province to Estimate a More Granular Impairment Credit Loss for Bank ABC.
Walton, P. (2004), IAS 39: where different accounting models collide”, Accounting in Europe, Vol. 15 No. 7, pp. 76-86.
Setya Wijaya, R. (2019). Pengaruh Faktor Makro Ekonomi terhadap Kredit Bermasalah pada Bank Umum di Indonesia. OECONOMICUS Journal of Economics, 4(1), 36–48. https://doi.org/10.15642/oje.2019.4.1.36-48.
Vaněk, T., & Hampel, D. (2017). The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2), 759–776. https://doi.org/10.11118/actaun201765020759.
Yang, B.H. (2017). Point-in-time PD term structure models for multi-period scenario loss projection: Methodologies and implementations for IFRS 9 ECL and CCAR stress testing. https://mpra.ub.uni-muenchen.de/76271/
Yogiswari, N., & Badera, I. (2019). Pengaruh Board Diversity Pada Nilai Perusahaan Dalam Perspektif Corporate Governance. E-Jurnal Akuntansi, 26(3), 2070 - 2097. doi:10.24843/EJA.2019.v26.i03.p15.