The market for Mobile Instant Credit (MIC) is growing rapidly both in India and abroad and has gained credence as a means to financial inclusion for the underserved segment. This has attracted the interest of the policy and the research community to study its impact. However, the literature on the impact of MIC is at a nascent stage, accompanied by a weak Theory of Change. This blog discusses three areas of impact that are understudied yet relevant and the need for drawing on lessons from over two decades of research in microfinance.
Digital credit is often positioned as an important pathway to financial inclusion, facilitating access to credit for those with little to no credit history. Significant philanthropic funding and state support have been diverted to the cause of digital credit with the goal of building and sustaining the livelihoods and well-being of low-income households. The market for digital credit has witnessed prolific growth with innovative models in partnership with banks, non-bank companies, telecom providers, and fintechs emerging globally. Therefore, understanding how accessing and using digital credit impacts individuals and households is an important area of study. Digital credit as a product, however, encompasses several typologies. For this blog, we restrict ourselves to what is popularly known as Mobile Instant Credit (MIC). MIC is defined as loans disbursed and repaid remotely through mobile phones. These are typically small-size unsecured loans that are disbursed immediately, and almost always within 24 hours (CEGA, 2024). In the Indian context, MIC can be equated with unsecured small ticket-size personal loans that are offered via digital lending apps. The average ticket size of these loans is roughly Rs. 10,000[i]. According to a report by FACE[ii], for the first half of FY 24-25, fintech personal loans accounted for 76% (in volume) and 12% (in value) of the share in the total personal loans market in India. The growth trajectory in the digital credit market both in India and other developing economies, makes the impact question a pertinent area of research.
While the literature on MIC and its impact is limited, the joint study by CEGA and IPA on Mobile Instant Credit: Impacts, Challenges, and Lessons for Consumer Protection[iii] is one of the few studies to synthesise evidence on the effects of MIC. The report presents a summary of six impact evaluation studies and finds that the effects of MIC on household welfare are largely neutral. Welfare is measured using metrics such as households’ improved ability to respond to shocks and manage day-to-day finances, increase in consumption, increase in asset ownership, increase in network cellular usage (time and money spent using a mobile phone for calls and messages), and improvement in subjective wellbeing. The only outcome variable that shows some signs of promise is the one on subjective well-being (measured as an individual’s self-reported life satisfaction, measures of depression or distress, and perceptions of one’s standing in social status) – of the two studies that studied the impact of MIC on subjective wellbeing, both found large positive effects. For the remaining outcome metrics, MIC either had no impact or inconsistent impact.
Reflecting on these findings pushes us to question how the loans are being used and what potential benefits the households might derive from these loans. The report highlights that more than half of all MIC loans are used for managing day-to-day consumption. These are typically categorized as everyday use, buying food, medical emergencies, paying school fees, transportation expenses, etc. Given the use cases of these loans, can these loans realistically increase households’ asset ownership, savings, and consumption? Are there alternate approaches to measure the effects of MIC both in terms of the outcome metrics and the impact methodologies? These are some of the questions that this blog raises and attempts to address. In the next section, we discuss three key areas of impact that are under studied and that MIC evaluations should pay special attention to.
1. Consumption smoothing and financial health
The CEGA-IPA report finds that households largely devote MIC for consumption purposes. Therefore, studying the impact of MIC requires greater emphasis on understanding households’ ability to manage various categories of expenses and other downstream effects as a result of their access to MIC. Parallelly, it is also important to understand the context in which households borrow these loans. We know from existing research that the financial lives of low-income households (LIHs) are characterized by insufficiency, instability, and illiquidity in incomes, thereby compounding the nature of uncertainty that they deal with[iv]. Given this context, LIHs set an ordered priority for themselves in their money use- managing basics (cashflow management to fulfill their daily needs and therefore smooth consumption), coping with risk (managing and recovering from shocks), and raising lump sums (building a corpus for various kinds of expenses)[v]. Therefore, small ticket MIC might be best suited for the first two out of the three priorities listed above- MIC can help fill gaps in liquidity thereby helping households make necessary expenses and manage basics; it can also help them cope with risks by accessing credit that can partially or fully fund emergency expenses. However, it most likely is not an appropriate tool for raising lump sums as these loans are not very large. This framework therefore helps us think through an alternative approach to measuring the impact of MIC- not necessarily in terms of an increase in household consumption but rather in terms of understanding if households can maintain a stable path of consumption towards meeting their basic expenses, paying school fees regularly without having to make any costly sacrifices, spiking their consumption temporarily for festival expenses, etc. In effect, MIC can prove to be a useful tool that can help households effectively manage some of their financial needs. This framing is also closely related to the concept of financial health, which is broadly defined as the extent to which an individual or household can smoothly manage their current and future financial obligations[vi]. However, the concept of financial health also encompasses planning for the future, for which MIC might not be the right tool. Therefore, it is useful to recognize that while some aspects of financial health could be directly affected as a result of access to MIC, certain other aspects might not. Testing and validating this hypothesis will be necessary to strengthen the theory of change on the impact of MIC. Using the right tools, however, to measure this impact is equally important. While financial health can be measured using a standard set of questions via a structured survey[vii], consumption smoothing could be best measured using a high-frequency approach by collecting data at a weekly or monthly frequency to understand household consumption trends. The impact of MIC on households’ ability to manage their money effectively can then be studied using both qualitative and quantitative techniques- a financial health module that captures qualitative perceptions of households’ current and future financial lives and a quantitative tool that captures the volatility in household consumption.
2. Interaction between MIC and the socio-cultural context of LIHs
Another strand of impact that is worth studying is to understand the interaction of MIC with the socio-cultural practices of LIHs and how both influence each other. The motivation for this theme of research stems from the fact that for LIHs financial and social relations are deeply intertwined[viii]. In other words, finance is not just a tool or an instrument but also a social relation filled with meaning and emotions. LIHs rely on these social relationships to strategise their money management needs[ix]. For example, a common savings strategy among LIHs is to save their money with a trusted friend or neighbour called the ‘moneyguard’. This prevents the saver from succumbing to temptation and frivolous spending and at the same time signals the saver’s trust in the moneyguard, thereby lubricating the social relationship between the two. Similarly, borrowing from friends and family is a very common way of bridging liquidity gaps and raising emergency funds for LIHs. Note therefore that these relationships are both financial and social. When formal finance in the form of MIC scales within these communities and potentially complements and/or substitutes informal channels of finance which are also social relations, there is reason to believe that it will influence the sociocultural fabric of these communities. Questions that then gain importance are- what happens to social relationships within low-income communities with an increased supply of MIC? Do these social relationships thrive, survive, or die? Does social cohesion within communities weaken and how do communities perceive these changes? Similarly, questions can be asked to understand the influence of socio-cultural practices of LIHs on the way MIC is designed and delivered. For example, what are the socio-cultural practices that make MIC relevant to a particular customer segment and how is MIC changing over time in keeping with the changing context of its customers and their families?
3. MIC and customer protection concerns
Customer protection concerns in the realm of digital credit is a relatively well researched space. The CEGA-IPA report documents evidence from several countries in Africa regarding consumer risks arising out of accessing and using digital credit. These most prominently pertain to opaque terms and conditions, frauds, and scams, data security and privacy violations, and debt distress, all of which directly harm the consumer leading to both monetary loss and psychological distress[x]. Similar consumer risks have also surfaced in India over the last few years[xi]. One approach to studying the impact of MIC would therefore be to understand the heterogeneous impact of MIC on household and individual outcomes based on the differential user experience and customer journey. This approach would involve collecting data in the form of structured questions on the various types of risks customers face in availing a MIC loan and the extent of monetary loss and distress the harm has caused customers and their families. For example, the impact of MIC on subjective well-being might be low or negative if MIC leads to debt distress for the customer. Similarly, hidden and high prices in a MIC loan can lead to a customer making the wrong choice affecting his/her income. Studying customer protection risks alongside the household-level impact of MIC explicitly acknowledges the potential harm that using digital credit might cause and thereby avoids the bias that most financial inclusion impact studies fall prey to that of assuming that inclusive finance can only lead to positive impacts.
Concluding thoughts
The theory of change for understanding the welfare effects of MIC should be centered around the features of the loan (both in terms of product and process design) and the context in which these loans are taken up and used. Developing this ground-up qualitative understanding will help in refining the theory of change and measuring impact more accurately. It took more than two decades of research on microfinance to conclude its modest and non-transformative effects on low-income households. Recent literature reviews on microfinance show that “results are subject to significant variation across geographies, programme design, and beneficiaries, and the heterogenous effects do lead to significant gains for certain populations”[xii]. Consensus is also building for the view that borrowers use microcredit to smooth consumption and spike spending (for lumpy purchases, which can be hard to save for) and that gains from microcredit could be seen in the timing and composition of spending during the year rather than annual averages. Impact evaluations in the field of MIC and more broadly digital credit should draw from learnings in the field of microfinance evaluations so that why, when, and how digital credit works doesn’t have to go through the same hype cycle of enthusiasm, inflated expectations, and disillusionment as Duvendack and Mader (2019)[xiii] put it very vividly.
Footnote:
[i] While the average ticket size for unsecured personal loans offered by Fintech NBFCs is Rs. 9,225, the loan size ranges from less than Rs. 25,000 to more than Rs. 5,00,000- 57% of loans are for less than Rs. 50,000, 25% of loans are between Rs. 50,000 to Rs. 2,00,000, 15% of loans are between Rs. 2,00,000 to Rs. 5,00,000, and the remaining 3% of loans are for more than Rs. 5,00,000.
[ii] FACE is an RBI recognized Self-Regulatory Organisation for the Indian Fintech-Sector (SRO-FT)
[iii] IPA & CEGA. 2024.Mobile Instant Credit: Impacts, Challenges, and Lessons for Consumer Protection. https://reports-cega.berkeley.edu/mobile-instant-credit-report/
[iv] Collins et al. 2009. Portfolios of the Poor. http://www.portfoliosofthepoor.com/
[v] ibid
[vi] UNSGSA. 2021. Measuring financial health: concepts and considerations. https://www.unsgsa.org/publications/measuring-financial-health-concepts-and-considerations
[vii] An approach to financial health measurement is proposed in the Dvara-XKDR Financial Inclusion Measurement framework which can be found here- https://dvararesearch.com/measuring-financial-inclusion-a-project-report/
[viii] Ghosh. 2022. The Print. Can financial decisions be free of emotions? Why its not the case in Indian households- https://theprint.in/economy/can-financial-decisions-be-free-of-emotion-why-its-not-the-case-in-indian-households/1170255/
[ix] Mas & Murty. 2017. Money, decisions, and control: building DFS that help poor customers cope and strategise. https://www.cgap.org/sites/default/files/Working-Paper-Money-Decisions-and-Control-Aug-2017.pdf
[x][x] Chalwe-Mulenga, Majorie, Eric Duflos, and Gerhard Coetzee. 2022. The Evolution of the Nature and Scale of DFS Consumer Risks: A Review of Evidence. https://www.cgap.org/sites/default/files/publications/ slidedeck/2022_02_Slide_Deck_DFS_Consumer_Risks.pdf.
[xi] Dvara Research. 2021. A convening on emerging customer risks in digital lending in India. https://dvararesearch.com/a-convening-on-emerging-customer-risks-in-digital-lending-in-india/
[xii] Jing et al. 2025. Microfinance. https://voxdev.org/voxdevlit/microfinance
[xiii] Duvendack & Mader. 2019. Impact of financial inclusion in low-and middle-income countries: A systematic review of reviews- https://pmc.ncbi.nlm.nih.gov/articles/PMC8356488/
