Assessing the long-term stability of Microfinance Institutions in India: A quantitative analysis of key operational metrics to highlight the extent of financial and social efficiency achieved by MFIs

International Journal of Research and Innovation in Social Science (IJRISS) | Volume II, Issue IX, September 2018 | ISSN 2454–6186

Assessing the long-term stability of Microfinance
Institutions in India: A quantitative analysis of key
operational metrics to highlight the extent of financial
and social efficiency achieved by MFIs

Neel Malhotra

12, Singapore International School, Mumbai, India

Abstract— Microfinance institutions (MFIs) have played an important role in enhancing financial inclusion in India. The unprecedented growth of the MFI industry highlights the success of their business models and is a testimony to the success and sustainability of the industry. The Indian regulator, Reserve Bank of India (RBI), has also played an instrumental role in facilitating the growth of this industry. This research paper is an attempt to understand the various factors that drive growth and efficiency for these microfinance institutions. Empirical analysis suggests that geographical and social factors and variations in them played a crucial role in the growth as well as the productivity and efficiency for the MFI Industry.
Data for top MFIs was used to analyze the following:
a) Comparative analysis of different MFIs across lending models (JLG vs SHG) and ownership structures (Not-for-profit vs for-profit).
b) Various operational metrics that contribute to the financial and social efficiency of MFIs, including the shifting focus towards urban areas from rural areas, and stupendous growth in the North-Eastern Region.
c) The social, economic and political environment, with focus on external shocks that are difficult to quantify and assess at the time of credit delivery.
d) Recent shifts in the business models that mitigate some of the risks and pave the way for further Financial Inclusion.
e) Using Multivariable linear regression and Holt’s trend-corrected double exponential smoothing to build an Associative model that forecasts the future of the MFI industry.
The study of different lending models and ownership structures clearly revealed a preference for the combination of Joint Lending Groups with “for-profit” as the most efficient business model and data on productivity and efficiency validates this thesis. Even though the lending is to the lower strata of the society, the profit motive not only enhances efficiency in the credit delivery process, but also helps generate internal capital and attracts capital investment from institutional investors. Moreover, the integration of technology along with introduction of Aadhaar have been instrumental in driving operational efficiencies. Improved efficiencies and risk-monitoring systems have led to declining credit costs, with some MFIs converting into Small Finance Banks or merging with larger NBFCs. The study also concludes that the shift towards individual loans, especially to existing borrowers is likely to bring about the next leg of growth for the MFI Industry.

Keywords—Lending models, Ownership structures, Joint-Liability, Weighted Average Sustainability Index, Weighted Productivity Index, Multivariate Regression analysis, Econometric Forecasting tools.

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