Whose Balance Sheet is This? Neural Networks for Banks’ Pattern Recognition

The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’ 2000-2014 monthly 25-account balance sheet data to test whether it is possible to classify them with fair accuracy. Results demonstrate that the chosen method is able to classify out-of-sample banks by learning the main features of their balance sheets, and with great accuracy. Results confirm that balance sheets are unique and representative for each bank, and that an artificial neural network is capable of recognizing a bank by its financial accounts. Further developments fostered by our findings may contribute to enhancing financial authorities’ supervision and oversight duties, especially in designing early-warning systems.

[1]  Ivo D. Dinov,et al.  Deep learning for neural networks , 2018 .

[2]  TamKar Yan,et al.  Predicting bank failures , 1990 .

[3]  Amir F. Atiya,et al.  Bankruptcy prediction for credit risk using neural networks: A survey and new results , 2001, IEEE Trans. Neural Networks.

[4]  Ulrich Eggers,et al.  The Economics Of Money Banking And Financial Markets , 2016 .

[5]  Peter Sarlin.  On biologically inspired predictions of the global financial crisis , 2012, Neural Computing and Applications.

[6]  Markus Holopainen,et al.  Toward robust early-warning models: a horse race, ensembles and model uncertainty , 2015, SSRN Electronic Journal.

[7]  I. Hasan,et al.  Financial Crises and Bank Failures: A Review of Prediction Methods , 2009 .

[8]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[9]  Hussain Ali Bekhet,et al.  Credit risk assessment model for Jordanian commercial banks : neural scoring approach , 2014 .

[10]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[11]  R. C. Wu,et al.  Neural network models: Foundations and applications to an audit decision problem , 1997, Ann. Oper. Res..

[12]  Melody Y. Kiang,et al.  Predicting Bank Failures: A neural network approach , 1990, Appl. Artif. Intell..

[13]  Lanouar Charfeddine,et al.  Islamic versus conventional banks in the GCC countries: A comparative study using classification techniques , 2015 .

[14]  CLASSIFICATION OF DOMESTIC AND FOREIGN COMMERCIAL BANKS IN TURKEY BASED ON FINANCIAL PERFORMANCES USING LINEAR DISCRIMINANT ANALYSIS , LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELS , 2011 .

[15]  Gottfried Rudorfer.  Early Bankruptcy Detection Using Neural Networks , 1995, APL.

[16]  Hal R. Varian,et al.  Big Data: New Tricks for Econometrics , 2014 .

[17]  P. McNelis.  Neural networks in finance , 2004 .

[18]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[19]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[20]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[21]  B. Kaliski.  Encyclopedia of business and finance , 2001 .

[22]  Xavier Brédart.  Bankruptcy Prediction Model Using Neural Networks , 2014 .

[23]  Galit Shmueli,et al.  To Explain or To Predict? , 2010, 1101.0891.

[24]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[25]  Mohsen Nazari,et al.  Measuring Credit Risk of Bank Customers Using Artificial Neural Network , 2013 .

[26]  Andrea Roli,et al.  A neural network approach for credit risk evaluation , 2008 .

[27]  Ignacio Olmeda,et al.  Hybrid Classifiers for Financial Multicriteria Decision Making: The Case of Bankruptcy Prediction , 1997 .

[28]  Kar Yan Tam,et al.  Neural network models and the prediction of bank bankruptcy , 1991 .

[29]  Martin T. Hagan,et al.  Neural network design , 1995 .

[30]  Marco Fioramanti,et al.  Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach , 2006 .

[31]  Shorouq Fathi Eletter,et al.  Neuro-Based Artificial Intelligence Model for Loan Decisions , 2010 .

[32]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.