In the financial system, non-bank financial institutions (NBFIs) play a crucial dual role. In the service of the commercial banks, which have some gaps, they supplement their functions. However, they also put commercial banks in a position of competition, which makes them more responsive to client demands and efficient. Non-Bank financial organizations, finance businesses and consumers, investment banks, and others that deal with pensions and mutual funds exist in relatively mature notions. NBFIs provide advances and loans for a commercial organizations, farming, housing, and real estate.

They also underwrite or purchase securities and lease financing, buy and sell securities, invest in shares, stocks, bonds, or debentures, and reinvest in those securities.

There are now 35 NBFI's, the first of which was opened in 1981. Out of the total number of NBFIs, 19 were established by private domestic enterprises, 13 through joint ventures, 2 were entirely under government supervision, and one is a branch of the state-owned commercial bank (SOCB). There were 277 branches of the 24 NBFIs in FY 2021, with assets totaling BDT 914.3 billion. Bangladesh has achieved remarkable progress in financial diversification, asset growth, NBFI numbers, area coverage, and account holder numbers. Despite significant progress, NBFI's face several challenges, including rising lending rates, rising NPL levels, higher interest spreads, credit concentration in trading and business, and reduced investment in the social sector.

The evaluation of NBFI's performance would enable them to carry out their responsibilities while also contributing to improving of NBFI's performance in Bangladesh. Even though several performance studies of the performance of NBFI's have been conducted in advanced industrial, emerging, and developing countries, Bangladesh has seen only a small number of these studies. A non-parametric data envelopment analysis (DEA) method is used in this work to empirically investigate the performance of Bangladeshi NBFI's in terms of technical, pure technical, and scale efficiency from 2016 to 2021.

The article has two goals: first, it examines the efficiency performance of Bangladeshi NBFI's from 2016 to 2021, and second, it offers policy recommendations for improving NBFI performance in Bangladesh. The remainder of the paper has the following structure: The second section covered a literature review. The third section of this paper focuses on methodology, while the fourth section looks at the results of overall technical, pure technical, and scale efficiency findings. The fifth section illustrates the comparative effectiveness of NBFIs, while the sixth section describes policy options. The seventh section focuses on the conclusions and recommendations.

In Bangladesh, conventional financial institutions like banks have been the subject of numerous efficiency studies, but there is a shortage of information on non-banking businesses. There are still gaps in the efficiency study of non-bank institutions in Bangladesh. Due to the importance of studies, we evaluated the efficiency of non-bank institutions in Bangladesh in this study.

The paper "Performance Analysis of non-banking finance companies using two-stage data envelopment analysis" by Dutta, Jain, and Gupta (2020) aims to evaluate the performance of non-banking finance companies (NBFCs) in India using two-stage data envelopment analysis (DEA). The authors have collected financial data on NBFCs from 2011-2019. In the first stage, the study examines the input/output efficiency of the NBFCs using traditional DEA models. In the second stage, the study explores the impact of environmental factors on the efficiency scores of NBFCs using a Tobit regression model. The authors' findings reveal that the average efficiency score of the NBFCs is relatively low, indicating significant inefficiencies in their operations. Further analysis using Tobit regression shows that macroeconomic factors such as inflation, GDP growth, and market concentration significantly affect the efficiency of NBFCs.

A data envelopment analysis" by Sharma, Rastogi, and Gupta (2020) examines the financial efficiency of Non-Banking Financial Companies-Microfinance Institutions (NBFC-MFIs) in India using data envelopment analysis (DEA). The authors collected financial data of NBFC-MFIs from the Indian Microfinance Pulse database for 2014-2018. The study aims to evaluate the technical efficiency, pure technical efficiency, and scale efficiency of the NBFC-MFIs. The authors' findings reveal that the average technical efficiency score of NBFC-MFIs is relatively low, indicating significant inefficiencies in their operations. Further analysis shows that the pure technical efficiency score is lower than the technical efficiency score, meaning that there is scope for improvement in managerial and operational practices. The authors also found that the scale efficiency of NBFC-MFIs is high, suggesting that they are operating at an optimal scale.

In "The efficiency of non-bank financial institutions: empirical evidence from Malaysia" (2006), Sufian investigates the technical efficiency of non-bank financial institutions (NBFIs) in Malaysia using data envelopment analysis (DEA). The study uses panel data covering 1998-2002 and includes 17 NBFIs. The results reveal that the average technical efficiency of NBFIs in Malaysia is low, indicating significant inefficiencies in their operations. The study also finds that small NBFIs are more efficient than larger ones, and the efficiency of NBFIs is positively related to their profitability. Moreover, the study finds that NBFIs that are more specialized in their operations tend to be more efficient than those that are more diversified. The study concludes that the Malaysian NBFIs' overall efficiency can be improved by increasing their scale of operations, adopting specialized functions, and improving their managerial and operational practices.

The profitability of businesses in Bangladesh's non-banking financial institutions (NBFIs) from 2005 to 2014 is examined by Mazumder, M. A. (2015). The findings show that profitability indicators affect net profit, but total assets, total equity, and operating income have a discernible influence on the profitability of Bangladesh's non-banking sector. Total assets are one of the most straightforward metrics for assessing the financial soundness of financial organizations. Almost all independent and dependent variables have strong positive associations, except operating costs. Except for total liabilities, term London Journal of Research in Management and Business 2 deposits, and operating expenses, almost every element has a positive effect. Debnath, G. C., Rahman, S. N., & Akhter, S. (2011) compares and analyze the liquidity positions of a few chosen non-bank financial organizations in Bangladesh from 2011 to 2015. The analysis considered the five NBFIs' places in terms of liquidity over the short and long terms according to maturity. We concluded from the complete investigation that all the chosen financial institutions have a positive and improving overall liquidity situation. However, the rate of liquidity expansion varies. Based on analysis, a significant portion of the company's short term liquidity is negative.

Data Envelopment Analysis was introduced in 1978 by Charnes, Cooper, and Rhodes. (DEA). The DEA maintains that returns to scale are constant. DEA is a non-parametric linear programming model that seeks to improve the effectiveness of each decision-making unit by optimizing its weighted output/input ratio (DMU). The efficacy of various input and output orientations was evaluated. According to Banker et al.'s assumptions, Scale efficiency (SE) and pure technological efficiency (PTE) are the outcomes of two components. The DEA gives various weights to the input and output of companies to maximize efficiency in contrast to other companies. Each unit is given a score, with one being the most effective and ranging from zero to one. The CCR model assumes the production function has constant returns-to-scale (CRS). The CCR model's objective score is technical efficiency (TE).

Consider that two DMUs require evaluation. Like DMU r , which requires X ir quantities of input and generates X volumes of output, each requires a different amount of input and yields a distinct volume of output. It is necessary that each DMU's have at least one positive input and output value, and it is anticipated that none of these values will be negative. The CCR model aims to maximize the weighted output to the weighted input ratio for the NBFIs under consideration. The objective function is maximized for NBFIs under the restriction that no other NBFIs in the sample may attain unit efficiency by utilizing the same weights. As a result, the objective function is: Here, j = j th output, j = 1,...,l; i = i th input, i = 1,...., k; r = 1, a r = an objective measure of efficiency for r th ; Y jr is the amount of output, X ir is the amount of input, The input weight is V i , the number of NBFI's is S, the number of outcomes is l, and the number of inputs is k.

By limiting the denominator of the target function to unity, the least issue can be simplified to a linear program. Thus, linear programming takes the following structure:

The production strategy and the intermediate approach are widely used to choose the input and output and compute various efficiency scores in scenarios mentioned in the literature. Technical inefficiency is 21 percent in BD Finance and 31 percent in National Housing, based on technical efficiency of 0.79 and 0.69 for BD Finance and National Housing, respectively. Technical inefficiency is 17% and 9%, respectively, for BD Finance PTE and SE, which are 0.83 and 0.91, respectively. The national housing PTE and SE scores are simultaneously 0.86 and 0.77, indicating that 23% of inefficiency is due to scale inefficiency and 14% is due to pure technical inefficiency. Regarding efficiency scores, United Capital and Lanka Bangla Finance are ranked Tenth and Twelfth, respectively. United Capital's and Lanka Bangla's technical efficiencies of 0.65 and 0.66, respectively, represent technological inefficiencies of 34% and 35%. When the scale efficiency of two companies is compared, Lanka Bangla has an 8% inefficiency and United Capital has a 20% inefficiency.

Among the NBFIs in the sample, ICB, GSP, IDCOL, and Fareast Finance exhibit continuous returns to scale (CRS) across the 2016-2021 study period, indicating that they are operating at maximum efficiency. It is impossible to adjust the production scale without diminishing effectiveness. The fact that IDLC runs at its optimal or most productive scale in 2020 and 2021 suggests that scale reduction might have enhanced efficiency during these periods. The fact that Lankabangla shows DRS from 2016 to 2019 and IRS from 2020 to 2021 suggests that it may have adjusted its production scale to be more productive from 2016

The graph reveals that in 2016, the overall technical efficiency average was 0.95, which representing 5% inefficiency. Efficiency scores improved at a constant rate of 0.97 from 2017 to 2019, showing 3% inefficiency. In the years 2020 and 2021, the technical efficiency score dropped to 0.57 and 0.54, respectively, reflecting inefficiencies of 43% and 46%. This indicates that the COVID-19 pandemic's effects prevented NBFIs from using their creative management abilities to manage the organization's resources and maintain production.

The study on the efficiency of NBFIs suggests that those with scale inefficiencies, rather than pure technical inefficiency, should focus on enhancing their management performance to enhance their technical efficiency. According to the NBFIs, to improve technological efficiency, DRS must either reduce output or diversify its product line. IRS, on the other hand, demonstrates a need to increase production capacity. As a result, while overseeing a variety of financial items, the company's management verified 100% technological efficiency.

The contribution of non-banking financial institutions (NBFIs) in Bangladesh is crucial for economic growth. Over time, NBFIs' influence on Bangladesh's financial industry has increased along with that of the conventional banking sector. The challenge posed by NBFIs to the traditional banking sector is growing. NBFIs have remarkably aided financial inclusion and made progress in closing the credit gap for retail consumers in Bangladesh's underserved and unbanked areas. NBFIs play a significant role in delivering a range of consumer services and bridging the financial services supply and demand gap for those needing loans.

Our metrics evaluate the operational effectiveness and scale economies of 17 NBFIs operating in Bangladesh. This analysis reveals that only ICB, GSP, IDCOL, and Fareast Finance received scores of one, indicating excellent efficiency in the three categories of technical efficiency, pure technical efficiency, and scale efficiency. Throughout the period, Lanka-Bangla Finance, United Finance, and National Housing have all consistently been inefficient NBFIs. The findings make it evident that the technical inefficiency in Bangladesh's non-bank financial institution industry is a result of poor input utilization, including managerial and scale inefficiencies, as well as inability to work at their maximum capabilities. Policymakers should take measures to increase the size and effectiveness of the non-bank financial sector to expand the financial sector of Bangladesh. Regulatory agencies should take extra precautions in order to improve efficiency through economies of scale. Future research may assess how information technology (IT) impacts NBFI performance in Bangladesh.