By Amulya Neelam, Dvara Research
The unsecured short-term lending model of microfinance has had several innovative processes/methods incorporated into it – the use of joint/peer liability as a substitute for collateral, targeting of women, small-ticket and frequent repayments, dynamic incentives such as progressive increases in ticket sizes, disincentives such as social sanctions and credit denial, interest rate discounts on prompt repayment, among others.
Despite the high collection efficiencies, the tight operations-driven nature of the business and the success of the microfinance business model as a supplier of formal credit to low-income households, there is evidence that when households borrow more than what they can repay, they end up in a state of being over-indebted, and many a time they have to resort to negative coping strategies such as cutting down on essential expenses or pulling children out of school to meet repayments (J.Schicks, 2012). Other forms of stress also rear its head, such as psychological stress, the strain on social ties, and reduction in living standards and quality of life (J.Prince, 2014; Ahmed. S.M, Chowdhury. M., Bhuiya.A,2001). In a previous post, we discussed definitions of over-indebtedness, indicators that have been used to identify it, and factors that contributed to it (in the context of Europe).
Like all businesses, microfinance too has avenues and opportunities for improving process efficiencies to improve outcomes for business and the customer. While research on new processes and innovations in microfinance is aplenty and have engaged both academicians and practitioners, this piece focuses on process innovations that have been tried either in experimental settings or in business settings in the past, and that were aimed at addressing concerns of borrower repayment capacity. Some of these also directly or indirectly try to address the issue of borrower over-indebtedness and the subsequent stress experienced by members of the household. We provide a thematic summary below:
Innovations in underwriting: The current creditworthiness assessment process followed in India can be described as a conversation between the loan officer and the borrower in which the loan officer fills up an application form capturing family members details, occupation profile, income and expense details, a pre-filled declaration by customer that their annual income is less than RBI prescribed cut-off, and details on the purpose for which loan is sought. This is followed by a credit bureau check by the microfinance provider (where this is an NBFC-MFI) and the decision on whether to approve or reject the loan taken by a higher officer or the Head Office. Since the information used from credit bureaus is typically only on loans from other MFIs, this paints an incomplete picture of indebtedness of the borrower. Other countries have seen different ways of carrying out credit-worthiness assessments, for instance, MFIs in Bolivia collect information on assets, compiling a scorecard based on locally relevant characteristics and using psychometric data (Arraiz. I., Bruhn. M., Stucchi. R. July 2015).
Many of the process innovations we found pointed to a stronger borrower screening process, including collecting reliable and accurate credit information at regular intervals. Apart from the standard ‘hard’ information collected on assets, ‘soft’ information including a subjective/ qualitative judgment about the borrower’s credit-worthiness can also be used. Such information would include using proxies of personal character, lifestyle habits such as consumption of narcotics, or participation in a local savings group, to name a few (WWB Note, 2003). Among discussions around the use of non-traditional data, the most discussed is that of mobile phone data which includes data points on phone model, recharges, balance enquiry, phone calls, and GPS, which gets used in credit-scoring models (Björkegren. D., Grissen. D., 2017). Such ideas are being implemented by enterprises like Tala Mobile who give micro-loans to borrowers in Kenya, Tanzania and Philippines based on data collected by an app on their smartphones (Adams. A, 2016). Quantified subjective judgments can be made where the loan officer has a list of subjective questions whose answers are on a numeric scale, thus effectively ‘hardening’ the soft information (ADB report, 2014). However, it must be noted that such credit scoring models are fraught with risks where it could develop into an opaque credit scoring system, potentially distorting credit supply in the market and resulting in the unfair exclusion of certain individuals.
Repayment capacity analysis as part of the process: Microfinance institutions typically lent primarily to women as they were deemed to be better borrowers. However, it has been found that women are in fact ‘proxy’ borrowers and they invariably end up handing over the money to their husbands who then use it for various purposes (Karim. L., 2011), even if this is not always the case. Hence the agency of the women is lost as it is the woman who must repay and demands a change of thinking in the lending process.
Lending methodologies can consider repayment capacity of the household. One could place limits on the maximum debt service level to disposable income ratios, as is indicated by a study in Kosovo which found that households having a loan repayment to household income ratio of up to 40% did not report stress (Spannuth & Pytkowska, 2011).
Fresh repayment capacity analysis before increasing loan sizes: Another weakness in the existing system is that many-a-times there is an automatic increase in loan size simply due to successful past repayments, and this is done without any fresh assessment of repayment capacity (Schicks. J.,2011). This, alongside the current incentive system structured on volumes of disbursements and portfolio quality, could be detrimental to the financial health of borrowers. Loan officers are incentivised to push loans onto earlier good borrowers who currently may not have any need, and they could resort to coercive measures of repayment from households experiencing financial difficulties to ensure a good portfolio (Rutherford. S., 2011). Therefore, a fresh repayment capacity analysis is important to consider.
Innovations in repayment schedule design: An important driver of over-indebtedness is that the product itself is not appropriate. Appropriateness would be determined by whether the repayment schedule takes into consideration the volatility in the borrower’s cashflows (Collins et al., 2009). For example, the early stage of entrepreneurial investments may not generate the kind of returns required to pay the first few instalments. Therefore, there are several ways in which flexibility can be incorporated into the contract. Flexibility in repayment frequency can be introduced by allowing the borrower to choose repayment frequency that is suited to their income frequency or to pause repayment of principal and pay only interest for certain periods chosen by the borrower (Field, Pande 2008; Czura 2015). The repayment schedule can be decided such that the borrower can choose to pay more (or less) when there is an expected cash inflow (or outflow) (Barboni and Agarwal 2017); or such that borrowers can pay the loan in part or whole before the maturity date, subject to a proportionate and minimum rate of interest (Grameen II). Another form of flexibility is repayment holidays, i.e., allowing a one-time moratorium to borrowers who choose to make illiquid and high-ticket investments into their businesses. Timing can either be chosen by the borrower (for unforeseen shocks) or by the provider (Barboni and Agarwal, 2017). A more in-depth review of this topic is available in this post.
Post-sale processes: A post-disbursal process that can be taken up to ensure repayment, and thus lower household stress, is the employment of soft-disciplining mechanisms – intermediate reminder text messages sent to borrowers, and application of mental accounting principles to put away a certain sum of money as savings towards repayment (Cadena. X., Schoar. A. May 2011).
Choice of locations to operate in: One of the necessary precursors to a microfinance institution setting up operations is local geography scoping. This would be more constructive if it were also to include an analysis of competitive hotspots, i.e., whether the area being scoped is one that is experiencing high competition. If it is, then it is very likely that the market is saturated and that the local inhabitants are already indebted. (Esubalew. A., Hermes. N., Meesters. Aljar, Aug 2010). It must be noted that even this would not be a comprehensive exercise as it would be exclusive of informal credit market players in the region.
Some of the ideas discussed above while well-intentioned, can, in fact, improve portfolio quality for the institution while not directly impacting borrowers’ ability to repay their loans (such as using information on assets, asking for mandatory savings, or locational scoping by the lender). The core issue of household distress due to over-borrowing needs greater attention. Business process solutions in this regard need to focus on ensuring households have cashflows to make repayments if they were to take on a new microloan, as articulated by some of the ideas above (such as repayment schedule design, repayment capacity of household). Households must not find themselves in a situation where they have to cut back on essential consumption in order to make repayments.
Schicks. J. (2012). The sacrifices of microborrowers in Ghana – A customer-protection perspective on measuring over-indebtedness
Cadena. X., Schoar. A. (May 2011). Remembering to pay? Reminders vs. Financial incentives for loan payments, NBER Working paper
WWB Note (2003). Credit Scoring in Microfinance: Guidelines based on experience with WWB affiliates in Colombia and the Dominican Republic. Accessed on Dec 8, 2018 from https://www.microfinancegateway.org/sites/default/files/mfg-en-paper-credit-scoring-in-microfinance-guidelines-based-on-experience-with-wwb-affiliates-in-colombia-and-the-dominican-republic-oct-2003.pdf
Asian Development Bank (2014) Access to Finance – Microfinance Innovations in the People’s Republic of China. Accessed on Dec 13, 2018 from https://www.adb.org/sites/default/files/publication/153012/access-finance-microfinance-innovations-prc.pdf
Esubalew. A., Hermes. N., Meesters. Aljar. (Aug 2010) Competition and Performance in Microfinance. Accessed on Dec 13, 2018 from https://www.cass.city.ac.uk/__data/assets/pdf_file/0008/86633/Hermes_Competition-and-Performance.pdf
Björkegren. D., Grissen. D., 2017 Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment. Accessed on Dec 13, 2018 from https://dan.bjorkegren.com/danbjork_grissen_creditscoring.pdf
Mitra S.K. (May 2009) Exploitative Microfinance Interest Rates. Asian Social Science. Accessed on Dec 14, 2019 from https://pdfs.semanticscholar.org/8c2e/02fe44d6896a2ed5f7b71cd2282e8f3f5417.pdf
Sk. Mahmudul Alam, Mahmud (2012): Does Microcredit Create Over-indebtedness? MPRA Paper No. 39124, posted 31. May 2012 10:06 UTC. Accessed on Dec 14, 2018 from https://mpra.ub.uni-muenchen.de/39124/
Karim. L., 2011. Microfinance and its discontents. women in debt in Bangladesh .Minneapolis-London: University of Minnesota Press, 2011, 255 pages, ISBN: 978-0-8166- 7094-9
Prince. J., (April 2014). The Impact of Access to Microfinance on Mental Health. Accessed on Dec 20, 2018 from https://scholarship.tricolib.brynmawr.edu/bitstream/handle/10066/14551/2014PrinceJ.pdf?sequence=1
Ahmed. S.M, Chowdhury.M., Bhuiya.A, (2001), Micro-Credit and Emotional Well-Being: Experience of Poor Rural Women from Matlab, Bangladesh, Accessed on Dec 20, 2018 from https://doi.org/10.1016/S0305-750X(01)00069-9
Arraiz. I., Bruhn. M., Stucchi. R. (July 2015), Psychometric tests as a tool to improve screening and access to credit: Evidence from Peru. Accessed on Dec 20, 2018 from https://caed2015.sabanciuniv.edu/sites/caed2015.sabanciuniv.edu/files/Irani_Arr%C3%A1iz_Psychometric9thDraft.pdf
Collins, D., J. Morduch, S. Rutherford and O. Ruthven (2009) Portfolios of the Poor: How the World’s Poor Live on $2 a Day. Princeton, NJ: Princeton University Press
Field, E., & Pande, R. (2008). Repayment frequency and default in microfinance: evidence from India. Journal of the European Economic Association, 6(2-3), 501-509
Czura, K. (2015). Do flexible repayment schedules improve the impact of microcredit? Evidence from a randomized evaluation in rural India.
Barboni, G., & Agarwal, P. (2017). Knowing what’s good for you: Can a repayment flexibility option in microfinance contracts improve repayment rates and business outcomes?
Yunus, M. (2002). Grameen Bank II: designed to open new possibilities. Grameen Dialogue, 50.
Adams. A. (2016), How Tala Mobile is Using Phone Data to Revolutionize Microfinance Accessed Dec 26, 2018 from the Forbes: https://www.forbes.com/sites/forbestreptalks/2016/08/29/how-tala-mobile-is-using-phone-data-to-revolutionize-microfinance/#3a8a1b62a9f2
Schicks. J (2011), Microfinance Over-Indebtedness: Understanding its Drivers and Challenging the Common Myths, CEB Working Paper N° 10/048 Accessed Jan 02, 2018 from
Rutherford. S., (2011) Two Cautionary Tales from Bangladesh Accessed Jan 02, 2018 from