Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models

2021 ◽  
pp. 097215092110267
Author(s):  
Nandita Mishraz ◽  
Shruti Ashok ◽  
Deepak Tandon
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Banking Sector ◽  
Banking System ◽  
Bankruptcy Prediction ◽  
Linear Discriminant ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Indian Banks

Financial distress is a socially and economically significant issue that affects almost every firm across the world. Predicting financial distress in the banking industry can substantially aid in the reduction of losses and can help avoid misallocation of banks’ financial resources. Models for financial distress prediction of banks are being increasingly employed as important tools to identify early warning signals for the whole banking system. This study attempts to forecast the financial distress of commercial banks by developing a bankruptcy prediction model for banks. The sample size for the study is 75 Indian banks. Logistic, linear discriminant analysis (LDA) and artificial neural network (ANN) models have been applied on the last 5 years’ (2015–2019) data of these banks. Data analysis results reveal the logistic and LDA models exhibiting similar prediction accuracy. The results of the ANN prediction model exhibit better prediction accuracy. It is expected that the results of this study will be useful for managers, depositors, regulatory bodies and shareholders to better manage their interests in the banking sector of the country.

Marketing of Banking Services in India

2017 ◽  
pp. 107-133
Author(s):  
Rakhi Arora
Keyword(s):  
Banking Sector ◽  
Technological Development ◽  
Banking System ◽  
Foreign Banks ◽  
Indian Society ◽  
Reserve Bank ◽  
Nationalised Banks ◽  
The Government ◽  
Indian Banks ◽  
Reserve Bank Of India

Banking sector plays an important role in Indian Financial Sector.It has a long history that has gone through various stages of development after Liberalization, Privatization, and Globalization (LPG) has taken place. The Indian banking sector is broadly classified into scheduled banks and non-scheduled banks. The scheduled banks are those included under the 2nd Schedule of the Reserve Bank of India Act, 1934. The scheduled banks are further classified into: nationalised banks; State Bank of India and its associates; Regional Rural Banks (RRBs); foreign banks; and other Indian private sector banks, which are controlled and governed by Reserve Bank of India (Central Bank of India) and Ministry of Finance. In this era, the government has issued licenses to the new entrants to establish new banks to serve the Indian society. This chapter focuses on to show the various undergone phases of Indian banking system, growth of deposits and credits, technological development in Indian banking sector, services provided by the Indian banks, benefits and challenges faced by the Indian banks.

scholarly journals How to Improve the Employee Productivity of Banking System in India – a Theory of Accountability Based Analysis

2018 ◽  
pp. 87-99
Author(s):  
P. S. Aithal ◽  
Prasanna Kumar ◽  
Mike Dillon
Keyword(s):  
Role Model ◽  
Banking Sector ◽  
Banking System ◽  
Service Planning ◽  
Employee Productivity ◽  
Target Setting ◽  
Public Sector Banks ◽  
Essential Components ◽  
Private And Public ◽  
Indian Banks

The recently developed theory called Theory of Accountability (Theory A) for organizations of 21st century identifies the various factors which affect the organizational human resources performance. The essential components identified to improve the productivity of any organization based on the postulates of Theory A are (1) Planning, (2) Target setting, (3) Motivation, (4) Work Strategies, (5) Responsibility, (6) Role model, (7) Monitoring & Guiding, and (8) Accountability. The objective of this paper is to apply the components of Theory A to Indian Banking system and to study how to improve the productivity of the banking system for economic progress in India. Accordingly we analysed the business model and the organizational strategy of Indian Banks in terms of their business objectives, service planning, target setting for the employees, employee motivational factors, working strategies to improve productivity, selfand mutual responsibilities among individual employees and in their teams, concept of role model in banking service innovation, continuous monitoring and guiding strategies, and finally accountability of each and every employee at different organizational levels. The applicability of Theory A on both private and public sector banks are discussed in general and suitable suggestions are proposed to the banking sector to improve productivity based on the postulates of Theory A.

scholarly journals Development of Urban Co-Operative Banks in India after Independence

2019 ◽  
Vol 8 (2) ◽  
pp. 114-118
Author(s):  
K. Shivappa
Keyword(s):  
Economic Development ◽  
Banking Sector ◽  
Banking System ◽  
Income Groups ◽  
Common Man ◽  
Development Banks ◽  
Indian Banks ◽  
Oriented Approach

It has been agreed that marketing of Bank services has been much neglected aspect of Banks and this has hindered the growth of Indian Banks in their role of assisting economic development. Banks need to change from ‘account oriented’ to ‘customer oriented’ approach. Today’s customer’s needs and expectations are changing very fast. To meet these needs and expectations a sensitively responsive Banking system is essential. The urban Co-Operative Banking sector is playing an important role. This sector is growing steadily year after year. It is providing helping hand for common man, middleclass people and small income groups.

scholarly journals Loan Waivers and Bank Credit: Reflections on the Evidence and the Way Forward

2019 ◽  
Vol 44 (4) ◽  
pp. 198-210
Author(s):  
Sudha Narayanan ◽  
Nirupam Mehrotra
Keyword(s):  
Financial Distress ◽  
Large Scale ◽  
Banking Sector ◽  
Banking System ◽  
Well Being ◽  
Policy Instrument ◽  
Negative Consequences ◽  
Executive Summary ◽  
Long Run ◽  
Macroeconomic Implications

Executive Summary In the past decade, farm loan waivers have become a policy instrument to alleviate the financial distress of farmers. Despite agreement on the theoretical rationale for such debt forgiveness and its deep contextual relevance, many fear that in the long run, loan waivers might vitiate the repayment culture in the farm sector and undermine the financial status of banks. At present, critiques of large-scale loan waivers rest on limited evidence. This article reviews and synthesizes existing research and available data on the implications of loan waivers, especially for the flow of credit to farmers from banks. On most of the issues, such as farmer well-being and repayment culture, there seems to be mixed evidence on the consequences of debt waivers. Credible evidence on macroeconomic implications is limited, mainly on account of methodological challenges. This article concludes that even if loan waivers are an inappropriate strategy to support farm incomes in sustainable ways, the wide-ranging negative impacts on the formal banking sector are perhaps overstated. A more fruitful approach would be to focus on whether loan waivers can be designed to reduce the possible negative consequences for the formal banking system as well as for macroeconomic system. The article identifies three possible instruments—loan insurance products that will help banks cope with the consequences of large-scale defaults. Second, to explore the creation of a distress fund that will cushion state finances, should there be a need for debt waivers. Third, it would be useful to consider the operation of debt relief commissions to have an ongoing process for debt waivers.

scholarly journals Deposit Competition and Financial Fragility: Evidence from the US Banking Sector

2017 ◽  
Vol 107 (1) ◽  
pp. 169-216 ◽  
Author(s):  
Mark Egan ◽  
Ali Hortaçsu ◽  
Gregor Matvos
Keyword(s):  
Financial Distress ◽  
Multiple Equilibria ◽  
Banking Sector ◽  
Banking System ◽  
Financial Fragility ◽  
Supply Side ◽  
Capital Requirement ◽  
Bank Run ◽  
Bank Regulations ◽  
The Us

We develop a structural empirical model of the US banking sector. Insured depositors and run-prone uninsured depositors choose between differentiated banks. Banks compete for deposits and endogenously default. The estimated demand for uninsured deposits declines with banks' financial distress, which is not the case for insured deposits. We calibrate the supply side of the model. The calibrated model possesses multiple equilibria with bank-run features, suggesting that banks can be very fragile. We use our model to analyze proposed bank regulations. For example, our results suggest that a capital requirement below 18 percent can lead to significant instability in the banking system. (JEL E44, G01, G21, G28, G32)

scholarly journals A Study of Changing Key Performance Indicators in Banking Sector

2019 ◽  
Vol 10 (3) ◽  
pp. 56
Author(s):  
Afroze Nazneen ◽  
Shikha Goyal ◽  
Pretty Bhalla ◽  
Vikram Jeet
Keyword(s):  
Performance Management ◽  
Performance Appraisal ◽  
Banking Sector ◽  
Banking System ◽  
Customer Relationships ◽  
Performance Measurement System ◽  
Appraisal System ◽  
Key Variables ◽  
Indian Banks ◽  
Traditional Performance

The Indian banking system has taken huge strides from being into traditional banking system to nationalization to privatization and finally into multinationals. The success dimension of Indian banks doesn’t only stand on financial indicators but it draws a thrust from, organizational culture, and customer relationships go a long way toward dictating future financial performance.The great transformation has been witnessed in the performance measurement system wherein the traditional performance appraisal system was taken over by multifaceted performance management system with feedback and continuous monitoring as inseparable part of it.In this paper, the researcher has proposed a model for measurement of performance in banking sector. And also the key variables have been found which are valuable in performance analysis.

scholarly journals Study on the Changing landscape of financial Services in Indian Banking System Opportunities and Challenges

2018 ◽  
pp. 1-10
Author(s):  
Pradeep M.D. ◽  
Sonia Delrose Noronha
Keyword(s):  
Information Technology ◽  
Financial Services ◽  
Banking Sector ◽  
Banking System ◽  
New Era ◽  
Integration Of Technology ◽  
Technological Developments ◽  
Indian Banks ◽  
Tools And Techniques ◽  
User Friendly

Financial institutions are the backbone of the Indian economy. Since economic liberalization after 1990, the Indian banking sector has witnessed growth along with remarkable improvement in its quality of assets and efficiency. Information Technology has become one's way of life in today's world that, it is difficult to imagine a world without IT. Technology which facilitates handling increased volumes at higher levels of efficiency. Hence, there is an imperative need for not mere technology up gradation but also integration of technology with the general way of functioning of banks. The Banking sector is no exception to this changing scenario which is sweeping across the world. Technology has given birth to a new era in banking. Indian banks are continuously encouraging the investment in information technology through ATMs, Netbanking, Mobile and Tele-banking, Automation of the banks, increasing use of plastic money, and the establishment of call centers. Nowadays banks are moving from disbursed operations to a centralized environment, powered by Information Technology. Banks are using new tools and techniques to reach better to its customers by offering tailor made products and Services. The changes in the banking landscape facilitated banks to compete in the new environment. Banks of the future will be a user friendly enterprise with technology aiming to achieve sustainable and valued business status. Information Technology has been imbibed in the banking operations with a vision to provide “Anytime Anywhere Banking” with customized services. In this paper we have discussed and analyzed the Changing landscape of financial Services in Indian Banking System in terms opportunities and challenges of technological developments, legal regulatory framework, and risk management.

Identifying impact of variables in deep learning models on bankruptcy prediction of construction contractors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Youjin Jang ◽  
Inbae Jeong ◽  
Yong K. Cho
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Shapley Value ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Input Variables ◽  
The Impact

PurposeThe study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.Design/methodology/approachThis study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.FindingsThe results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.Originality/valueThis study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

Bank’s battle against insider frauds ignitors and mitigators: an emerging nation experience

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Neha Chhabra Roy ◽  
Sankarshan Basu
Keyword(s):  
Financial Services ◽  
Banking Sector ◽  
Market Competition ◽  
Banking System ◽  
Principal Component ◽  
Original Data ◽  
Content Type ◽  
Primary Survey ◽  
Different Types ◽  
Indian Banks

Purpose Banks are exposed to many challenges to name a few i.e. growing market competition, political environment, market forces of demand and supply, technological changes, frauds and poor management. The banking sector devasted experiences of fraud have impacted all facets of the Banking, Financial Services and Insurance. In continuation, this study aims to revolve around themes of different types of frauds, especially insider frauds that have gained mainstream attention in recent major value fraud events with prominent Indian banks. This study will identify the types and drivers of insider frauds. Design/methodology/approach The methodology opted for the study is through confidential primary survey and focused group discussion with risk officers of banks who are associated with Indian banks for more than three years, further to understand the relation between type of Insider frauds and originating drivers were paired based on the principal component analysis. Findings Finally, the paper concludes with the conceptual mitigation framework for different types of insider fraud and driver pairs within the scope of this paper. This paper thought will support policymakers of the Indian banking system to create a more robust environment within the banking system via timely detection of frauds so that up to an extent it can be squared before it appears. Originality/value The study is innovative in the area of banks’ internal fraud management, where original data collected through a primary survey contributes to the conclusion of fraud management for various Indian banks.

On the determinants and prediction of corporate financial distress in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjay Sehgal ◽  
Ritesh Kumar Mishra ◽  
Florent Deisting ◽  
Rupali Vashisht
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Accounting Information ◽  
Support Vector ◽  
Content Type ◽  
Distress Prediction ◽  
Practical Implications

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

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