IDEAS home Printed from https://ideas.repec.org/a/ibn/assjnl/v15y2019i10p49.html
   My bibliography  Save this article

Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia

Author

Listed:
  • Suwandi Suwandi
  • Noer Azam Achsani
  • Dedi Budiman Hakim
  • Halim Alamsyah

Abstract

Since it was first operating in 2005 until 2017, Indonesia Deposit Insurance Corporation (IDIC) has liquidated 91 rural banks which were determined as failed banks by supervision authority. The cause of the failing of the bank is mainly due to the incapability of the bank to meet the minimum Capital Adequacy Ratio (CAR). Bank’s capital was shrunk by the loss caused by fraud. The fraud is mostly induced by the lack of good corporate governance implementation. By using the logistic regression, it can be concluded that (1) the incomplete of responsibility letter which will be used in the event of bank failure, submitted by the bank commissioners; (2) the incomplete of responsibility letter which will be used in the event of bank failure, submitted by the bank directors; (3) role duplication between shareholders and board of directors; and (4) bank had classified as special supervision, have impact on the increase of rural banks failure. At the same time, the compliance level of rural banks to a correct premium payment has impacted to decrease of rural banks failure possibilities.

Suggested Citation

  • Suwandi Suwandi & Noer Azam Achsani & Dedi Budiman Hakim & Halim Alamsyah, 2019. "Bank Failure Prediction Model Based on Governance: A Case of Rural Banks in Indonesia," Asian Social Science, Canadian Center of Science and Education, vol. 15(10), pages 1-49, October.
    • Handle: RePEc:ibn:assjnl:v:15:y:2019:i:10:p:49
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ass/article/download/0/0/40881/42210
    Download Restriction: no
    File URL: https://ccsenet.org/journal/index.php/ass/article/view/0/40881
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    2. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    4. Peterson K. Ozili, 2017. "Bank earnings management and income smoothing using commission and fee income," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 13(4), pages 419-439, August.
    5. Peterson K. Ozili, 2017. "Bank earnings management and income smoothing using commission and fee income," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 13(4), pages 419-439, August.
    6. Amin Jan & Maran Marimuthu, 2016. "Bankruptcy Profile of Foreign versus Domestic Islamic Banks of Malaysia: A Post Crisis Period Analysis," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 332-346.
    7. Raffaella Calabrese & Paolo Giudici, 2015. "Estimating bank default with generalised extreme value regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1783-1792, November.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    2. Abdelghani Maddi, 2018. "Analyse scientométrique de la crise économique," CEPN Working Papers 2018-08, Centre d'Economie de l'Université de Paris Nord.
    3. Caporale, Guglielmo Maria & Cerrato, Mario & Zhang, Xuan, 2017. "Analysing the determinants of insolvency risk for general insurance firms in the UK," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 107-122.
    4. repec:hal:cepnwp:hal-01922256 is not listed on IDEAS
    5. Abdelghani Maddi, 2018. "Analyse scientométrique de la crise économique : Courants de pensée, auteurs influents et thématiques," Working Papers hal-01922256, HAL.
    6. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    7. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    8. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    9. Samuel Opoku & Kingsley Opoku Appiah & Prince Gyimah, 2024. "Can We Predict the Financial Distress of Banks in Sub-Saharan Africa?," SAGE Open, , vol. 14(3), pages 21582440241, August.
    10. Forgione, Antonio Fabio & Migliardo, Carlo, 2018. "Forecasting distress in cooperative banks: The role of asset quality," International Journal of Forecasting, Elsevier, vol. 34(4), pages 678-695.
    11. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    12. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    13. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    14. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Journal of Entrepreneurial and Organizational Diversity, European Research Institute on Cooperative and Social Enterprises, vol. 3(1), pages 89-103, June.
    15. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    16. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    17. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.
    18. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    19. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    20. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    21. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:assjnl:v:15:y:2019:i:10:p:49. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.