Non-banking Financial Companies Research Articles

Macroeconomic effect of NBFC default shock in India: a four-sector DSGE model

ABSTRACT In this study a four-sector dynamic stochastic general equilibrium model is developed by introducing banks and non-banking financial companies (NBFCs) in a general two-sector real business cycle model in context of Indian economy. Examining the impact of three shocks namely NBFC default shock, productivity shock and investment shock, default of NBFC on its loan does not have any significant impact on aggregate output, consumption and investment. The shock has localized impact on the NBFC sector itself because of its low asset size and weak interconnectedness with the Indian economy. Productivity and investment shock have significant impact on the macroeconomic variables.

A Study on Financial Performance and Growth of Non-Banking Financial Companies

This study provides a comprehensive analysis of the financial performance and growth dynamics of Non-Banking Financial Companies (NBFCs) within the contemporary financial landscape. Drawing on a review of existing literature, regulatory frameworks, and extensive financial data, the research evaluates key performance indicators, growth trajectories, and risk profiles of NBFCs over a specified time period. Through a rigorous examination of profitability, liquidity, leverage, and efficiency metrics, the study uncovers insights into the relative strengths and weaknesses of NBFCs compared to traditional banking institutions. Additionally, the analysis delves into the drivers of growth, regulatory influences, and systemic risks shaping the NBFC sector. The findings contribute to a deeper understanding of the role of NBFCs in the financial ecosystem and offer valuable insights for policymakers, regulators, investors, and industry stakeholders to foster sustainable growth and mitigate potential risks.

Empowering Women: E-Shakti's Impact on Self-Help Groups Digitization and Financial Inclusion in India

The study assesses the influence of the National Bank for Agricultural and Rural Development’s (NABARD) Project E-Shakti on the digitization of Self-Help Groups (SHGs) and the economic position of women in India. The study evaluates the project's outcomes using data from NABARD's Status of Microfinance in India reports from 2007-08 to 2022-23. The number of SHGs credit linked, savings of SHGs, loans disbursed to them, loans outstanding against them, and non-performing assets (NPA) were the variables considered for the study. Trend analysis and correlation were applied to the available data. For the analysis of the data, the Python algorithm was used. The study's findings indicate that Project E-Shakti intensely boosted the number of districts served and SHGs linked to formal finance, reducing their dependency on informal sources. The project also resulted in a significant increase in loan disbursement to SHGs, indicating enhanced financial sustainability and accessibility. The digitization of SHGs improved women's economic conditions by allowing them to engage in income-generating activities. The programme helped to reduce non-performing assets, signifying its beneficial impact on the financial health of SHGs. Refinance to Non-Banking Financial Companies- Microfinance Institutions (NBFC-MFIs) showed a boost after E-Shakti but then a volatile pattern was observed in the subsequent years. Project E-Shakti was a success in terms of promoting digitization, increasing financial inclusion, and improving women's economic empowerment in SHGs. To maximize the long-term benefit of digitization programmes, the study proposes increasing their scope, investing in digital literacy training, and enhancing monitoring and evaluation procedures.

Measuring Financial Performance of Non-Banking Financial Companies (NBFCs): Evidence from Select BSE-Listed NBFCs In India

The present study examines the effects of reserves on the proportion of loans disbursed, cost incurred in ratio to income, total assets and total equity on the financial performance, that is, profitability of Indian Non-Banking Financial Companies (NBFCs). The study applies Fixed Effects Panel Regression model to BSE-listed, eleven NBFCs selected on basis of market capitalisation for the period from financial year 2001 to 2023. It was found that excess of reserves in proportion to loans disbursed significantly and negatively affected profitability. In order to maintain liquidity for liability obligations, Indian NBFCs cut short on credit disbursals, which affected profit, as interest income is the main source of revenue for Indian NBFCs. Another important finding in the case of Indian NBFCs was that with an increase in the size of total assets, profitability was negatively affected. It implies that the loans disbursed or investments that were being made by Indian NBFCs were either of poor asset quality or had low-income or profit-generating capacity. The Indian NBFCs invested in or disbursed loans mainly to the risky and unbanked housing and infrastructure sectors and non-creditworthy retail customers, which adversely affected their profitability.

Role of Technology in Transforming Non-Banking Financial Companies in India

Role of Technology in Transforming Non-Banking Financial Companies in India

A Comparative Analysis of Regulatory Framework of Non-Banking Financial Companies in Pakistan and India

A Comparative Analysis of Regulatory Framework of Non-Banking Financial Companies in Pakistan and India

Mahindra and Mahindra Financial Services Ltd– A Case Study

Purpose: This paper is about the organizational analysis of Mahindra and Mahindra Financial Services Ltd [MMFSL]. There are investment products with moderate returns and relatively minimal risk. Numerous public sector banks, private sector banks, non-banking financial companies, and other financial organizations provide these investing facilities. From the perspective of investors, this research offers a variety of details about Mahindra and Mahindra's financial services portfolio, the many goods and services they offer, as well as the steps a consumer must take. Design/Methodology/Approach: Companies that provide financial services set their goals, which may include things like customer satisfaction, market expansion, revenue growth, risk management, and regulatory compliance. To enable effective collaboration, decision-making, and accountability, a clear organizational structure is built. This entails evaluating consumer demand, researching the competition, and seeing chances for product innovation or difference. Findings/Result: Over the years, MMFSL's loan book and profitability have increased gradually. The company's financial success is influenced by several variables, such as interest rates, the general state of the economy, and the demand for automobiles and equipment. To lower risk and boost revenue streams, MMFSL has been seeking to diversify its business beyond car finance. They have grown to finance more asset classes like commercial vehicles, used cars, and construction equipment. To improve the convenience and accessibility of loan applications, approvals, and repayments, they have launched a few digital platforms and initiatives. Originality/Value: It primarily focuses on learning about organizations in-depth, including the responsibilities of the organization and competency mapping, to understand how well the firm is working and to examine its current competitors. The proposed work determines the company's liquidity condition. The SWOT analysis framework is used for evaluating the company's strengths, weaknesses, opportunities, and threats. Paper Type: Case Study.

Constraints to the promotion of financial inclusion in India: an empirical study of implementing agencies under Pradhan Mantri MUDRA Yojana

PurposeThe study aims to discern the primary obstacles confronted by the implementing agencies in their efforts to foster financial inclusion through the “Pradhan Mantri MUDRA Yojana” (PMMY).Design/methodology/approachTo collect primary data, a semi-structured questionnaire was developed. Around 120 loan officers from the implementing agencies (Scheduled Commercial Banks (SCBs), Regional Rural Banks (RRBs), Small Finance Banks (SFBs), Non-Banking Financial Companies (NBFCs) and Micro- Finance Institutions (MFIs)) of Haryana were randomly selected to fulfill the objectives. To categorize the perceived problems into discrete factors, the “factor analysis” technique was employed. The scales were then regressed on factors linked to the demographic characteristics of the loan officers to validate the hypotheses.FindingsThe study highlighted the primary obstacles impeding the advancement of financial inclusion, which encompass a range of factors. These include challenges in management, infrastructure, politics, finance and technology. Furthermore, the study established the association of the explanatory variables, namely gender, age, educational qualification, location and experience of the officers, with the extracted constraints. Notably, the experience of loan officers emerged as the most influential variable contributing to the promotion of financial inclusion through the scheme.Originality/valueThe current body of literature lacks any empirical investigation focusing on the perspectives of the implementing agencies regarding the challenges they encounter in advancing FI. Given the significance of FI in India, where access to formal financial services remains a critical issue, this research adds value by addressing the gaps in understanding the problems encountered.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0462

OPPORTUNITIES AND CHALLENGES IN E-BANKING SERVICE IN DIGITAL ERA

Another word often used interchangeably with electronic banking is “digital banking”. Both words are used equally often. However, strictly speaking, banks rarely or do not use paper money. However, the use of paper money is still widespread. Today's ATMs are an important part of the banking system because they allow consumers to withdraw money when needed. In the digital economy, there are almost no monetary transactions. Cash is often thought of as something that is free. But using cash is expensive. Foreign exchange spending is discussed in a Harward Business Review research article titled “Countries that would benefit most from a cashless society.” Consumer expenses (like ATM fees, foreign exchange commissions, etc.) are included in cash costs. Because, To analyze the impact of CBDC concerns on the global environment, Dong (2021) studied statistical models. If CBDCs are the only method of capturing value, then higher interest rates cannot lead to financial disintermediation. Due to the complementarity of these deposits and the deposits offered by CBDCs, banks and companies will lend more money and invest more in them. The central bank has asked banks and non-banking financial companies (NBFCs) to ensure that digital lending applications (DLA) do not automatically increase credit limits and access to resources their mobile. borrower. RBI said borrowers must be given time to review or seek exit from the digital loan by paying the principal and corresponding Annual Percentage Rate (APR) within this period without incurring penalties in 'Guide to digital lending' for regulated institutions or RESs (banks). and NBFC). However, it is still unclear how CBDC will actually affect the banking industry and the economy. Ten of the eleven countries that have introduced official digital currencies are small nations in the Eastern Caribbean, including Jamaica and the Bahamas. In October 2021, Nigeria became the only major country to introduce CBDC for retail. Therefore, the RBI will have to introduce CBDC and conduct pilot testing very carefully. Banks' operating costs have been significantly reduced thanks to digital banking. With this, banks can now offer better deposit interest rates while charging lower service fees. Bank profits increased due to reduced operating costs. Conclusion: Indian banks still have a long way to go before adopting technology at par with banks around the world. Keywords: Digital Banking, Digital Currency, Blockchain Technology, Digital Retail Lending

A Study On Financial Performance Analysis With Reference To Real Touch Finance Limited And Selected NBFCs In India Using Camel Model

The emergence of Non-Banking Financial Companies (NBFCs) in India is a response to the substantial unbanked population, making them vital players in the financial market. Institutions like Real Touch Finance Limited (RTFL), Dharani Finance Ltd, and Shriram Finance Ltd have experienced rapid growth due to the rising demand for funds in areas like education, housing, marriage, vehicles, and leisure. A comprehensive CAMEL analysis from 2018 to 2022 assessed their financial performance. RTFL displayed positive trends in management efficiency, earnings, and asset quality. Dharani Finance Ltd faced volatility in returns and profitability challenges, while Shriram Finance Ltd had positive capital adequacy but struggled with liquidity and ROA. RTFL outshone with improved margins and non-interest income. In conclusion, RTFL appears relatively stronger, highlighting the importance of ROCE and ROA for shareholder wealth. This study provides valuable insights into improvement areas, growth prospects, and risks, emphasizing capital utilization, cost management, profitability, and efficient liquidity management for long-term success.

Exercise decision of employee stock options: does Herding Bias influence the employees' decision?

PurposeEmployee Stock Options [ESOs] have been used widely as a component of employees' compensation. To maximise the incentive effect of these options it is very important to understand the exercise decision of the employees. This is an important financial decision that is dependent on both rational and psychological factors. This paper aims to study the mediating role of Herding Bias on Personality Traits and the employees' decision to exercise ESOs.Design/methodology/approachThe data were collected through a self-structured questionnaire from 210 employees of Banks and NBFCs [Non-Banking Financial Companies] who have received and exercised the ESOs. SPSS MACRO version 25 was used to understand the mediational effect of Herding Bias on Personality Traits and Employees' decision to exercise their ESOs.FindingsThe results showed that Personality Traits affect the employees' decision to exercise their ESOs. The study also shows a partial negative mediating effect of Herding Bias on Personality Traits and employees' decision to exercise ESOs.Originality/valueLimited study has been conducted on how the employees make their decision to exercise ESOs. Although extant studies have touched upon the importance of including behavioural biases in ascertaining the exercise decision of the employees, the predictors of the behavioural biases have not been studied under this context. To the best of the author's knowledge, this study is the first in itself to study the inter-linkage between Personality Traits, Herding Bias and employees' decision to exercise ESOs.

Systemically important financial institutions and drivers of systemic risk: Evidence from India

The aim of this study is twofold: (1) identify the systemically important financial institutions in India and their contributing role to Indian systemic risk during various global and domestic events; and (2) uncover the factors driving systemic risk in Indian financial institutions. To serve the first aim, we estimate financial institutions' involvement in the overall risk of the Indian financial market using the component expected shortfall approach. The results show that the State Bank of India, ICICI Bank, HDFC Bank, and HDFC Ltd. contribute more than 50% to the aggregate risk in India, demonstrating a significant systemic risk concentration. These financial institutions are also referred to as domestic systemically important banks (D-SIBs) by the Reserve Bank of India (the central bank in India). Financial institutions show an increased contribution to overall systemic risk after the systemic events considered in the study. Furthermore, results indicate that banks play a dominating role in contributing to systemic risk in India; however, the analysis also indicates the increasing contribution of non-banking financial companies and insurance firms to the gross risk of the Indian financial market in recent years. To serve the second aim, we apply panel data regressions and show that the probability of default, leverage, loan, liquidity, board size, and Basel norms are significant drivers of systemic risk. Besides the importance of our findings for investment and risk management decisions during crisis periods, our findings have implications for policymakers overseeing the financial system in India, assessing potential vulnerabilities and designing prudential regulatory and action plans to maintain financial stability.

A Study on the Non-Banking Financial Companies (NBFCs) in India

A Study on the Non-Banking Financial Companies (NBFCs) in India

Crop Insurance Premium Recommendation System Using Artificial Intelligence Techniques

Purpose: The objective of this study is to build a crop insurance premium recommender model which will be fair to both crop insurance policy holders and crop insurance service providers. Theoretical Framework: The Nonparametric Bayesian Model (modified) is the name of the proposed model suggested by Maulidi et al. (2021) and it consists of six variables which are regional risk, cultivation time period, land area, claim frequency, discount eligibility (local variable) and premium. Discount eligibility variable is introduced to encourage right farming practices among farmers. Design/methodology/approach: Descriptive research method is used in this study as it is used to accurately represent the characteristics of a group of items. The population for this study is 943 respondents. The entire dataset is used for in-depth and accurate analysis. Five Artificial Intelligence models (Machine Learning models) are proposed for crop insurance premium prediction and they are Ada Boost Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Support Vector Regressor and K-Neighbors Regressor. Among them Gradient Boosting Regression model has given the highest accuracy. Thus, Gradient Boosting Regression model is the most suitable model to be recommended for crop insurance premium prediction. Findings and Suggestions: Regional risk, land area, claim frequency and cultivation time period is the order of independent variables from highest to least in terms of regression coefficient. This relative importance helps Non-Banking Financial Companies (NBFCs) to suggest farmers that they should concentrate most on the regional risk or chances of crop failure in a particular region in which they are doing agriculture and least on the cultivation time period of a crop or the season in which a crop is cultivated. Two suggestions for future researchers are to extend this research work to other parts of Tamil Nadu and to apply hybrid machine learning techniques to the proposed model. Practical Implication: Unlike the existing formula-based traditional method used for calculating crop insurance premium, artificial intelligence models (machine learning models) can automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve its accuracy based on new data. Hence, the crop insurance premium suggested by the most accurate model among the artificial intelligence models used in this study will be fair to both NBFCs and farmers. Here, fair means moderate. On the other hand, the crop insurance premium suggested by the existing formula-based method may not be fair in the long term as they cannot automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve. Originality/value: In this research article, the relative importance of independent variables in the proposed model is determined and it helps NBFCs to suggest farmers that they should concentrate most on the region they are doing agriculture and least on the cultivation time period of a crop. Additionally, a machine learning model which can automatically learn and improve itself is used and hence the crop insurance premium predicted by it will be fair. Finally, the entire population containing 943 respondents details is analysed.

Impact of bank competition on financial stability-a study on Indian banks

PurposeNowadays, the competition is not only emerging from within the banking sector, but nonbanking companies like nonbanking financial companies (NBFCs) and FinTech are also growing in size and numbers, offering innovative financial products and services, giving a stiff competition to Indian banks. Thus, this study aims to investigate whether competition from within and outside the banking sector enhances or reduces the financial stability of the banking industry.Design/methodology/approachThe study uses Herfindahl–Hirschman index to measure market share and Z score to measure financial stability. The study further examines the role of NBFCs and FinTech companies in impacting the financial stability by introducing variables like innovation, cybercrimes, systemically important institutions, etc. Thereafter, panel regression has been applied.FindingsEmpirical results show a positive relation of market share with financial stability, implying that increased competition in the Indian banking industry erodes the market power, adversely affecting the profit margins which encourages banks to take more risk and which may impact financial stability. The study shows a positive impact of innovation on financial stability which implies that the competition is acting as an enabler for banks. The authors find a negative relation of systemic important NBFCs with financial stability. The authors observe a negative association of cybercrimes with financial stability, reflecting that competition emerging from FinTech sector has exposed banks to new risks.Research limitations/implicationsThe policymakers should make sure that the competition of banks with other financial institutions, such as FinTech sector, remains healthy; otherwise, it can jeopardize the entire financial system. It is for the policymakers to define a boundary for FinTech sector, as the development of this sector has exposed the banking industry to new kinds of risks potential to create financial instability. The banks should do a comprehensive check on the company to which it is granting loans, and the government should amend laws. Though big banks have huge potential, consolidations can pose challenges at a macroeconomic level.Originality/valueFinTech firms are a new entrant in the financial world which are providing immense competition to the banking sector, and thus radically changing the entire financial system. Therefore, it is extremely vital to study and explore the role of NBFCs and the FinTech industry as the main variable to analyze bank competition, which to the best of the authors’ knowledge is completely missing in the previous studies.

How Does Tobin’s Q Respond to Merger and Acquisition Announcements: Evidence of Listed Indian Firms

Purpose: The aim of this study is to examine the linkage between mergers and acquisition announcements and firm’s valuation taking into consideration Tobin’s q. Theoretical framework: The Q theory of investment propounded by James Tobin (1969) is suitable for the present study. According to this theory, there is a significant correlation between a company's market value and its rate of investment. Following this, indicators of the relative potential profitability of investments are the valuations that are placed in financial markets on the securities of companies as ratios to the costs of replacing their assets, which is denoted by q. Design/methodology/approach: The current investigation examines Tobin's q distribution for 140 listed firms on the Bombay Stock Exchange from 2011 to 2022. Tobin’s q is calculated for the financial service sector of the Indian economy. Findings: The results study suggests that Stock markets react significantly to the valuation of Indian firms due to M&A announcements in the category of non-banking financial companies. More specifically, 40 companies have a high Tobin’s q ratio in the full sample of firms. According to the findings, the majority of M&A announcements had no effect on the Tobin's q of the sampling companies. This indicates that the stocks of companies operating in the financial services sector are not affected by news of mergers and acquisitions. It demonstrates that the Indian capital market responded normally to information on mergers and acquisitions. Research, Practical & Social implications: The study helps financial analysts, top-level management, and stakeholders of a company to correctly evaluate the impact of mergers and acquisitions announcements on Tobin’s q of firms as a performance measurement metric. Originality/value: The value of the study can be evaluated from the fact that it investigates the relationship between mergers and acquisitions announcements and Tobin's q as a crucial parameter for corporate valuation, which is the first of its kind for emerging economies.

"Navigating Human Resource Practices: Insights into Job Satisfaction in Tamil Nadu's Non-Banking Financial Companies"

Because of the vital responsibilities they play in nation-building and financial inclusion, non-banking financial companies are playing a significant role in encouraging inclusive growth in the nation. The primary concentration of NBFCs is on loan-related activities, particularly gold loans, and advances, as well as shares, bonds, debentures of various kinds, leasing, hire-purchase agreements, and the insurance industry. Respectively, they constantly view their employees as the foundation of their business and work to ensure employee satisfaction. However, the Human Resource Department has a difficult time getting to know its workers, which means that the majority of NBFCs experience greater attrition rates. Thus, 450 respondents were used in this study to identify and comprehend the human resource policies and practices used by NBFCs and determine the relevant links between these practices and job satisfaction. For this descriptive study, data were gathered from 450 employees of top Gold Loan NBFCs in Tamilnadu State using a stratified random selection method. Correlation analyses were used to create and test research hypotheses. The findings show that job satisfaction is strongly correlated with performance evaluation, extrinsic and intrinsic motivation, talent acquisition and selection, employee engagement, and human resource development.

Do NBFCs propagate real shocks?

Notwithstanding a smaller share of total loans vis-à-vis commercial banks, we investigate a possible role of Non-banking financial companies (NBFCs) in propagating a real shock to the rest of the economy. Our two-sector model captures emerging economy characteristics such as NBFC borrowings from commercial banks, heterogeneities in financial constraints, and labour market friction faced by firms. Our theoretical and simulation results, using Indian parameters, indicate that an idiosyncratic shock (i.e., higher realization of the failed firms) and a sectoral productivity shock (in the sector financed by NBFCs) increase the interest rate charged by the banks, and the unemployment rate while reducing the real wages and per capita capital formation. However, the reverse happens given a structural shock, assumed as an increase in the average number of failed firms. Early detection of such shocks and quick policy intervention are required to provide a cushion for capital formation and job creation.

비은행권 금융회사 대출의 가계연체율 결정요인에 관한 연구

The purpose of this study is to analyze the dynamic interrelationship between household overdue rate of the banking and the non-banking financial companies, the factors of the household’s ability to pay, and the factors of household’s equity capital. We set up the model based on the solvency hypothesis and the equity capital hypothesis to analyze the effect of household overdue rate in banking and non-banking sectors. We identified the dynamic effects between related variables through impulse response analysis and variance decomposition analysis using the VAR model. The result of the analysis is as following. First, the household overdue rate in the banking sector was affected only by the previous one. Because the rate remains very low, the household default risk in the banking sector is very low. Second, the household overdue rate in the non-banking sector was more affected by household consumption than household income. Third, the household overdue rate in the non-banking sector was more affected by changes in house prices in metropolitan area than non-metropolitan area. Forth, the household overdue rate in the non-banking sector was more affected by changes in house prices in single-family house than apartment and multi-units house. According to these research results, it is necessary to stabilize the price control and reduce the burden of the financially vulnerable in terms of managing the risk of household overdue rate of non-banking companies. Furthermore, the government should strengthen integrative financial regulations of non-banking financial companies to manage potential household default risks.

ANALYSIS AND PROGNOSTICATION OF BANKRUPTCY AMONG BSE (BOMBAY STOCK EXCHANGE INDIA) LISTED COMPANIES

When a company faces difficulty in maintaining stability in financial performance of the company, there is a vast chances of bankruptcy occurrence. There are two possible ways when the company cannot financially survive, one is debt restructuring and another one is proceeding for bankruptcy. The objective of the research is to predict the sample companies which have the chance of bankruptcy or not. The research design and sampling method for this project is descriptive research and non-probability sampling method. Using Sample size of 186 Non-Banking Financial companies’ secondary data that is five years financial report, the Altman Z score analysis (Multi Discriminant Analysis) was conducted with a help of Microsoft office Excel 2021. Based on the analysis results, 11 out of 186 companies were in unsafe zone in 2021. Similarly, 14 out of 186 companies were in unsafe zone in 2020, and 8 out of 186 companies were in unsafe zone in 2019.