Early identification of ‘weak’ banks is essential for safety and soundness of the banking sector. Longer the delay in such identification, heavier the cost on an economy. In this article, we apply multiclass classification to classify banks operating in the Indian banking sector as ‘strong’ and ‘weak’ banks. Such classification is expected to provide direction to bankers for bank management. Using average return on equity (ROE) as basis for classification, we apply five machine learning models to the bank data set, namely Naïve Bayes, support vector machine, k-nearest neighbours, random forest and average neural networks. We find that all five models are able to predict the bank classes with a very high degree of accuracy. Ratio of non-performing assets to net advances turned out to be the most important variable in classifying banks as ‘weak’, followed by inflation and real exchange rate. The study is the first of its kind that successfully applies machine learning models in the Indian banking sector.
AiqunW., ZicongH., YilinW., KolivandH., BalasV. E., PaulA., & RamachandranV. (2020). Risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model. Journal of Intelligent & Fuzzy Systems, 39(4), 5915–5925. https://doi.org/10.3233/JIFS-189066
2.
AltmanN. S. (1992). An introduction to kernel and nearest neighbor nonparametric regression. The American Statistician, 46, 175–185.
3.
Basel Committee on Banking Supervision. (2015). Guidelines for identifying and dealing with weak banks. Bank for International Settlements. https://www.bis.org/bcbs/publ/d330.pdf
4.
CaoD.-Z., PangS. L., & BaiY.-H. (2005). Forecasting exchange rates using support vector machines. In Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, pp. 18–21.
5.
DomingosP., & PazzaniM. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103–130.
6.
DonepudiP. K. (2017). AI and machine learning in banking: A systematic literature review. Asian Journal of Applied Science and Engineering, 6(3), 157–162.
7.
DongdongL., WangD., LiM., & XiangY. (2020). DNN models based on dimensionality reduction for stock trading. Intelligent Data Analysis, 24, 19–45.
8.
DufloE., & BanerjeeA. (2021, October15). Three economists & a revolution. The Times of India. https://timesofindia.indiatimes.com/blogs/toi-edit-page/three-economists-a-revolution-2021s-economics-nobel-laureates-freed-the-discipline-from-limiting-bonds-of-theory/
9.
FixE., & HodgesJ. L. (1951). Discriminatory analysis, nonparametric discrimination: Consistency properties(Technical Report 4). USAF School of Aviation Medicine, Randolph Field.
10.
HoT. K. (1995). Random decision forests. In Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1, pp. 278–282. https://ieeexplore.ieee.org/document/598994
11.
HorobetA., BelascuL., IonitaI., & OprescuA.-T. S. (2014). A neural networks perspective on the financial integration of European capital markets. Economic Computation and Economic Cybernetics Studies and Research, 48(1), 1–12.
12.
HuangJ., ChaiJ., & ChoS. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14. https://doi.org/10.1186/s11782-020-00082-6
13.
HuangS., CaiN., PachecoP. P., NarrandesS., WangY., & XuW. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics, 15(1), 41–51. https://dx.doi.org/10.21873/cgp.20063
14.
KuhnM. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1–26.
15.
KumarS. W., ShiueY. R., ChenS. C., & ChengH. M. (2009). Applying enhanced data mining approaches in predicting bank performance: A case of Taiwanese commercial banks. Expert Systems with Applications, 36(9), 11543–11551.
16.
LeH. H., & VivianiJ.-L. (2018). Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios. Research in International Business and Finance, 44(c), 16–25.
17.
LoukerisN., & EleftheriadisI. (2015). Further higher moments in portfolio selection and a priori detection of bankruptcy, under multi-layer perceptron neural networks, hybrid neuro-genetic MLPs, and the voted perceptron. International Journal of Finance & Economics, 20(4), 341–361.
18.
MaltarolloV. G., KronenbergerT., EspinozaG. Z., OliveiraP. R., & HonorioK. M. (2019). Advances with support vector machines for novel drug discovery. Expert Opinion on Drug Discovery, 14(1), 23–33. https://dx.doi.org/10.1080/17460441.2019.1549033
19.
McHughM. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica (Zagreb), 22(3), 276–282.
20.
MellitA., PavanA., & BenghanemM. (2013). Least squares support vector machine for short-term prediction of meteorological time series. Theoretical & Applied Climatology, 111(1), 297–307.
21.
MitchellT. M. (1997). Machine learning. McGraw-Hill.
22.
NarasimhamM. (1998). Report of the committee on banking sector reforms. Reserve Bank of India.
23.
OzgurO., KaragolE. T., & OzbugdayF. C. (2021). Machine learning approach to drivers of bank lending: Evidence from an emerging economy. Financial Innovation, 7(20). https://doi.org/10.1186/s40854-021-00237-1
24.
PetropoulosA., SiakoulisV., StavroulakisE., & VlachogiannakisN. (2020). Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting, 36. https://doi.org/10.1016/j.ijforecast.2019.11.005
25.
RanjanR., & DhalS. (2003). Non-performing loans and terms of credit of public sector banks in India: An empirical assessment (Occasional Paper. 24, pp. 81–121). Reserve Bank India.
26.
RaviV., KurniawanH., ThaiP. N. K., & Ravi KumarP. (2008). Soft computing system for bank performance prediction. Applied Soft Computing, 8(1), 305–315.
27.
RussellS., & NorvigP. (1995). Artificial intelligence: A modern approach (p. 25). Prentice-Hall.
28.
SharmaA., & ChopraA. (2013). Artificial neural networks: Applications in management. IOSR Journal of Business and Management, 12, 32–40.
29.
VermaM. S. (1999). Report of the working group on restructuring of weak public sector banks. Reserve Bank of India.
30.
WeiJ., YeT., & ZhangZ. (2021). A machine learning approach to evaluate the performance of rural bank. Complexity. https://doi.org/10.1155/2021/6649605
31.
XuM., WongT. C., & ChinK. S. (2013). Modeling daily patient arrivals at emergency department and quantifying the relative importance of contributing variables using artificial neural network. Decision Support Systems, 54(3), 1488.
32.
ZhangL., HuH., & ZhangD. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation, 1. https://doi.org/10.1186/s40854-015-0014-5
