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How can FinTech TSPs support microfinance institutions to get on board the DEPA ecosystem?

Posted on by MSC

Our previous blog examined how the Data Empowerment and Protection Architecture (DEPA) and account aggregators (AAs) could disrupt India’s banking and financial ecosystem. DEPA and AAs can also empower people from the low- and moderate-income (LMI) segment by enabling them to use their data and access better financial services. DEPA intends to democratize individuals’ data using a consent-based framework and allow the movement of trusted data between service providers with customer consent.

While DEPA is set to revolutionize the Indian financial ecosystem, financial institutions like MFIs may not find onboarding this framework “appealing” or “easy.” Our previous blog discussed the challenges microfinance institutions (MFIs) face while adopting DEPA. A recap of our previous blog:

Key factors hindering DEPA adoption by microfinance-based lending institutions

Some of the critical challenges above may take some more time to resolve. These include the uniform regulation of all forms of MFIs and the increased digital footprint of customers. With the increasing digitization of payments in India, more people will join the digital ecosystem.

MFIs will need to find ways to overcome the remaining challenges and subsequently adopt the DEPA framework.

The role of emerging FinTech players is critical. The AA ecosystem brings together technology service providers (TSPs) that support financial information providers (FIPs) and financial information users (FIUs) to use the AA ecosystem through their tech infrastructure and data analytics capabilities.

A critical link in the DEPA framework is the nonprofit Sahamati, a collective of account aggregators that drives open banking in India. It allows individuals and small businesses to share their data with a third party, acting as a common platform to capture customer financial details in one place.

Sahamati has classified TSPs into three different categories based on their offerings:

As shown above, “AA data standard” TSPs can help MFIs initiate and implement the FIP and FIU modules. Whereas “data analytics” and “user experience” TSPs can provide value-added services to the institutions. These TSPs can help MFIs gradually embrace the available customer data and use it efficiently.

For any MFI, how do these TSPs score higher than any regular data analytics company?

A regular data analytics company focuses on the “data processing” part of the value chain, which means the MFI remains responsible for “data sourcing” and “data consumption.” For example, an MFI collects the customer data like bank statements from borrowers through documents or images and stores it in its system from which the data analytics company reads the data. After analysis, the results are stored in a database where the MFI can view or use them for further processing. Moreover, most data analytics companies offer generic analytics platforms. This means that MFIs must engage with these companies actively to decide the analytics they must carry out on the data. Many MFIs do not know this. Companies with niche analytics solutions often do not offer the flexibility of matching MFI needs, which differ significantly from other lenders.

MSC is the technical partner alongside CIIE.CO in the FI Lab of Bharat Inclusion Initiative. Finarkein is a promising TSP FinTech in this cohort.

How does Finarkein, a TSP in the AA ecosystem offer solutions for MFIs through its Flux platform?

Finarkein offers an end-to-end solution that has the flexibility of customization based on MFIs’ needs. The solution offers incremental change in the MFI business model instead of causing a wider disruption. For example, most MFIs today lack technological processes and rely on manual work for customer interaction, application, fulfillment, and post-servicing. Their workforce is familiar with documents. To support them, Finarkein has built a solution called “Flux” that fetches data from AA, processes it, and converts it into an easy-to-understand PDF file that the MFI workforce can use to act upon. It is curated primarily for technologically laggard NBFCs to encourage them to adopt the AA ecosystem. Flux allows Finarkein to integrate the MFIs with new technology while guiding them toward wider digital transformation.

Source: Sahamati—shows an increasing list of TSPs that help financial institutions embrace the AA framework

Onboarding as an FIU does not require MFIs to make any technological changes as Finarkein provides the technology. The only support needed from the MFI is to help the customer approve consent requests via calls or in-person engagement—Finarkein sends the customer an SMS containing the consent link. If a customer does not have a smart device, then consent can be taken on the MFI agent’s device too. In the future, consent approval can be done on WhatsApp or through phone calls too.

Finarkein can share output reports over email, which MFIs can use for underwriting. In this journey, the MFI does not build any new technical capability. Finarkein offers an end-to-end working solution that helps the MFI become an FIU and source data. Finarkein has also been working on a portal through which MFI agents can share the consent link with customers, further eliminating the need for technical integration. The company has structured it as a flexible pay-per-use model from a commercial perspective.

With the benefits that TSPs like Finarkein provide, an MFI can stop worrying about daunting technological integrations and focus more on its business.

How do TSP services further support the MFIs to build decisions based on data?

Building an early warning signal system through data analytics

Delinquency costs remain a significant pain point for MFIs. TSPs can help traditional MFI lenders manage their delinquency better with data analytics. Emerging FinTechs in the lending space use data analytics extensively to understand their portfolios and draw deep customer insights. TSPs can help traditional MFIs enhance operational efficiency by reducing customer monitoring costs, customer acquisition costs, and NPAs.

Early warning engine for debt collections using alternate data

Most MFIs’ lending portfolio is unsecured. While MFIs follow regimental processes, they lack clear insights into the borrower’s loan repayment intent. Once MFIs are onboarded onto the AA framework as an FIU, TSPs can enhance the prediction engine by importing additional customer data from other sources. Hence, integrating with AA platforms will facilitate building effective risk management solutions for MFIs.

Bounce alert reports using alternate data

The customer signs up for electronic loan repayment through the National Automated Clearing House (eNACH) for repayments. The bounce alerts system will flag if the customer has any pending dues. TSPs can help MFIs implement AI/ML-based models to study customers’ payment patterns of their loan products.

Reducing operational costs and increasing efficiency through better visualization

This is an area where TSPs can play a crucial role through their data visualization expertise. TSPs can support MFIs by analyzing customers’ pre-existing data and presenting it succinctly. They can support MFIs by building dashboards on internal and external data.

Dynamic collection scores using alternate data from other institutions and CRM data

This feature can automate and optimize the repayment collections management process, which would improve collection efficiency, minimize the risk of NPAs, minimize write-offs, and improve customer relationships, among others. Dynamic collection scores will also help reduce loan repayment collection costs due to the automated process.

AI/ML-driven personalization for customers

TSPs can provide MFIs with an AI/ML-powered platform to facilitate hassle-free and remote customer onboarding, customer screening, additional customer benefits through different interest rates, and other benefits. Moreover, it can offer the MFI better product management tools within a secure environment. MFIs can use natural language processing (NLP) through this platform to address the language barrier between new-age technology and their customers. TSPs can help develop a voice-assisted user interface for the MFIs’ application to ensure hassle-free usage by their LMI customers.

Cross-selling and upselling of credit-based products

MFIs can use TSPs to help upsell or cross-sell other credit-based products to existing or new clients. Backed by extensive data and behavioral analytics, TSPs can suggest that MFIs target specific customers to cross-sell or upsell third-party products like buy-now-pay-later facilities and credit cards.

MFIs need to examine the benefit that the DEPA ecosystem can offer. India Stack’s consent layer can potentially reduce costs and enhance the customer value proposition. It is time for MFIs to adopt data-intrinsic approaches to function efficiently in the digital age and drive financial inclusion. This is where the TSPs can come in handy.

Linking this back to where it started—we return to the story of lighting homes with electricity way back in 1882. After electricity entered homes and factories, many other inventions revolutionized the way we live, such as the induction motor, which transformed industries and further led to the power washing machine (1907), vacuum cleaners (1908), and household refrigerators (1912). These inventions led to the greater adoption and demand for electricity. The account aggregator framework will likely follow a similar path where financial service providers will be more interested in adopting it with evolving use-cases. Till then, entities like TSPs will continue to simplify the onboarding of FSPs onto the AA framework.

What holds back microfinance institutions from adopting the DEPA framework?

Posted on by MSC

Electricity is one of humanity’s greatest inventions. It all started back in 1752 when Benjamin Franklin demonstrated that lightning was electrical with his famous kite experiment. Yet from the time the first American home got electricity in 1882 to when virtually all homes in America had connected to the grid in 1960, people could not fathom how electricity would revolutionize their lives in the 20th and 21st centuries. The Indian digital ecosystem stands at the brink of a similar— albeit faster—revolution.

In 2017, the Reserve Bank of India came up with the innovative idea of “non-banking financial company (NBFC)—account aggregators (AA)” that would aggregate the financial data of individuals. Much of this critical data lies in silos managed by different players across the industry. An AA provides information on various accounts held by a customer across multiple banking and financial entities. These AAs were conceptualized to consolidate, organize, and retrieve data on various types of financial engagements by a customer across different financial instruments like insurance, mutual funds, GST data, and bank account transactions.

In 2020, the NITI Aayog released the Data Empowerment and Protection Architecture (DEPA) to empower every Indian with control over their financial data, democratize access to data, and enable the portability of trusted data between service providers. Simply put, the DEPA framework offers the user more control over how they share their data with regulated financial entities to access financial services, such as credit, insurance, and wealth management. DEPA also allows financial services providers to better understand the customer’s financial habits and offer them customized financial products.

The role of the consent manager or AA in the DEPA
Source: NITI Aayog

Two years ago, only a handful of large banks participated in this framework. Today, this ecosystem has more than 100 bank and NBFC participants. While the idea was seeded back in 2016, it was slow to gain traction. Yet once the AA framework formally went “live” in September 2021, it has been “full-steam-ahead.”

DEPA is expected to democratize data and improve individuals’ access to financial services. However, lending institutions like NBFC-microfinance institutions (MFIs) and some small finance banks with MFI-based models currently hesitate to join this framework for several reasons, mentioned later. These institutions play a critical role in driving financial inclusion and providing credit to 100+ million low- and middle-income (LMI) customers. India’s microfinance gross loan portfolio has grown at 18% CAGR in the past five years, up to June, 2021. Of the total Indian microfinance portfolio of USD 30.3 billion, NBFC-MFIs and SFBs hold a share of 49.5%, with their borrower outreach in very poor households being more than 11.6 million, as of June, 2021.

Microfinance lending portfolio as of June, 2021
Source: Reserve Bank of India

MFIs have a highly touch-based model, which results in a high cost of delivery and limited scalability. FinTechs that offer digital credit while promising efficiency, lower costs, and easier access have started to compete in this space. The emergence of new players poses the “need for digitization” of the microfinance-based model. As per a KPMG study, the figure below highlights the state of digitization of MFIs in India.

Survey findings from the KPMG study
Source: KPMG

Many MFIs in India are swiftly adopting different types of technologies. These include cashless disbursements, credit underwriting using alternate data points, psychometric evaluations, and geo-tagging of customers, among others. DEPA will likely boost this tech adoption by the sheer amount of data it would provide for customer analysis. In the process, it will strengthen MFIs’ decision-making abilities.

However, these institutions may need more time to benefit from this framework.

Operational factors

Legacy system

All MFIs follow the group-based lending model to assess their clients’ creditworthiness. About 85% of their assets are group loans. This system focuses on the customer’s repayment history in previous loan cycles and their engagement tenure. It does not depend on data points as much, since MFIs do not do a cash flow analysis to assess creditworthiness. Most MFIs optimize operational costs through regimental process engineering. Hence, many MFIs would also see that bringing in more data from external sources is unnecessary.

Mono-product institutions

Microfinance works mainly on a single product—the group loan—which has not changed much for many years unlike other products for the non-LMI segment. The only changes in the interest rate and loan size are primarily due to the RBI’s regulatory guidelines. Unlike other financial services providers (FSPs), MFIs do not use data to customize or change their product offerings. Consequently, they do not offer a case here for DEPA.

Status quo and looking for a proof of concept

The existing system has been working well for years. So no MFI wants to tinker with it. Also, small MFIs wait for large MFIs or other players like banks to show the way.

All MFIs are not regulated uniformly

Not all MFIs in India are regulated by the Reserve Bank of India (the central bank). Many small MFIs are classified as societies under the Societies Registration Act, as trusts under the Indian Trusts Act, and as not-for-profit companies under Section 25 of the Companies Act. Due to their size and regulation, these small MFIs are not keen on any innovation that puts their operations at any risk (even perceived). These non-profit MFIs are content with the status quo since they face more organizational resistance to adopting the AA framework.

Technological factors

Limited tech ability

Smaller MFIs have limited skills around technology, such as using cloud services and application programming interfaces (APIs), due to costs involved or staff capacity. This limits their inclination and indeed their capability to adopt the DEPA framework as a financial information user (FIU) or financial information provider (FIP) and develop modules with relevant customer data. Handholding remains limited, which makes such initiatives a daunting task.

Business-level factors

High costs of implementation and limited resources to employ

Implementing the AA framework, including the AA’s fees and the API charges for fetching customer data, is expensive and consequently increases the cost of lending. MFIs would be cautious in tightly contested markets and conduct a cost-benefit analysis before signing up for the new system. Some players also feel that the costs are too steep at the moment. Perhaps MFIs are waiting for costs to reduce as the DEPA system matures.

End-customer-level factors

Customer segments with limited phone ownership and low or no digital footprint

The use of the DEPA platform depends on smartphones. The AA framework requires customers to download the AA’s app on their smartphone and provide consent to share their data. The GSMA Mobile Gender Gap report 2021 shows that just 67% of women in India own a mobile phone, while only 25% own a smartphone. The NFHS survey for 2019-21 indicates just 54% of women own a mobile phone. Even though smartphone ownership is increasing, low smartphone penetration hurts DEPA’s adoption. Hence, MFIs may struggle to access meaningful data, except perhaps for their smartphone-using clientele.

Limited financial literacy

The digital and financial literacy of LMI users is limited. They struggle to navigate the app, add bank data, understand the requirement for consent, and provide consent to sharing the required data points. Most LMI customers need help with these activities.

Poor agent network and cash-based business

Most group loan customers are engaged in businesses that accept and pay in cash. Hence, they would need a vibrant agent network to digitize this cash. Currently, finding liquid agents in rural areas continues to be a challenge.

The combination of the factors above limits the adoption of this consent framework by MFIs. Could MFIs be supported in getting on board the DEPA and benefit from India Stack’s consent layer? Can FinTech firms play a role? Read our next blog that discusses how FinTechs as technical service providers (TSPs) can help such institutions adopt the AA framework.

Embedding finance for inclusion

Posted on by MSC

CreditHaat—making distribution of financial services: “targeted, simple, and effective”

Posted on by MSC

This blog is about a startup under the Financial Inclusion (FI) Lab accelerator program’s fifth cohort. The Lab is supported by some of the largest philanthropic organizations across the world – Bill & Melinda Gates Foundation, J.P. Morgan, Michael & Susan Dell Foundation, MetLife Foundation, and Omidyar Network.

The FinTech market in India is estimated at USD 31 billion in 2021, around 6% of the overall financial market valued at USD 500 billion. Over the next five years, the FinTech market will grow annually at 22% and become more mainstream than today.

Lending FinTechs comprise 16% of the 6,300 odd FinTechs countrywide. Many try to cater to the sizeable unmet credit demand of almost USD 200 billion within India’s micro, small, and medium enterprise (MSME) sector. FinTechs in this highly contested space differentiate themselves based on the efficacy of their digital credit offering to the underserved.

According to the Reserve Bank of India, digital loans increased twelvefold to INR 1,41,821 crore (USD 18.3 billion) from FY 2017 to FY 2020. The share of non-banking financial companies (NBFCs) in the digital lending ecosystem increased from 6.3% in FY 2017 to 30.3% in FY 2020. The increase can, in part, be attributed to many new-age FinTechs tapping into the loan books of NBFCs to funnel credit to last-mile customers. Despite this tremendous growth, digital lending comprises of just around 1% of the total lending in India, with private commercial banks still dominating the space.

Credit is not distributed equitably in India, especially among underserved and unserved populations. At the end of 2021, more than 50% of Indians remained credit unserved. This customer segment lacks formal credit history, making it difficult for traditional financial institutions to analyze their credit behavior. This segment is known as “credit invisible.”

CreditHaat has identified the following three significant gaps in the distribution funnel of credit product workflows:

Figure 1: Gaps in the credit distribution funnel

The light bulb moment

Tanuj Sinha, the founder of CreditHaat, is an entrepreneur who investigates market gaps and creates innovative and scalable solutions to address them. His previous startup, Finlok, influenced him to develop the idea of CreditHaat. Finlok was a digital platform that provided financial services to the traditionally underbanked customer segment. It was based on a chit fund—a saving and borrowing financial instrument in which a group of subscribers contributes a fixed monthly amount for a set period, and members receive returns and take loans based on their contributions. As Finlok grew, Tanuj identified a large customer base keen on availing credit products.

Due to the limited visibility of digital lenders, clunky user interfaces of mobile applications, and complex documentation processes, these customers could not access the right credit providers and navigate the loan application process. Tanuj identified the broken distribution chain for credit products as a significant gap that needed urgent attention. He created a digital credit marketplace solution to bridge this gap, which matches potential borrowers from the credit-invisible segment with relevant credit providers.

Further, the exciting opportunity and potential to make a large-scale impact attracted Awdhesh and Archana to join the core team. Before joining CreditHaat, Awdhesh worked with PaisaBazaar, where he was part of the growth team, and Archana has worked with Bajaj Finance and Digit Insurance previously.

Figure 2: The CreditHaat team

What does CreditHaat do differently?

The CreditHaat platform has created a comprehensive loan marketplace with a simplified lending funnel to bridge the gaps outlined in Figure 1. The platform has registered 800,000 users and has disbursed more than 37,000 loans worth INR 250 million (USD 3.3 million). The focus is on handholding customers through the lending funnel while ensuring that they match with the right lender at the best possible interest rate to prevent drop-outs. The customer can find a suitable lender and complete the journey with operational support from the back-office team, as outlined in Figure 3 below.

Figure 3: The CreditHaat lending model

The loan ticket size ranges from INR 2,000 to INR 1 million (USD 26 to USD 13,000), with tenures ranging from 62 days to five years. CreditHaat uses several channels to source customers to cater to different segments and preferences. They include fully digital acquisition and assisted acquisition through local partnerships (like business correspondents) and on-the-ground field staff for digitally inactive customers. The platform also captures additional customer data, such as demographic and income patterns, to create user profiles and personas. CreditHaat uses this data to gather insights and predict customers’ financial behavior and potential credit needs. This information helps lending partners when designing credit products for the credit-needy segment.

Impact on the low- and moderate-income (LMI) segments

CreditHaat primarily caters to customers from the LMI segment. It acknowledges the unique challenges these customers from less developed geographies grapple with. Nearly 70% of its current customer base has a total monthly household income of less than INR 25,000 (USD 325) and resides in non-metro cities. The focus is to get the right product-market fit so that customers can reach the right lender that offers them a suitable credit product.

The team believes in simplifying access to credit. It also provides an assisted model involving field agents called “Sahayaks,” who handhold customers throughout the onboarding process till disbursement.

Support from the Financial Inclusion lab

CreditHaat wants to expand its partnerships-based acquisition model by onboarding aggregators in the financial inclusion space. It intends to use the aggregators’ agent network and reach out to the target LMI customers. MSC and CIIE.CO supported the startup by developing a strategy to partner with aggregators, such as BC Network Managers (BCNMs), cooperatives, microfinance institutions (MFIs), and farmer producer organizations in its target geographies of tier 2 and tier 3 cities.

MSC developed a detailed approach for CreditHaat to target suitable aggregator partners. The CreditHaat team can use it to prioritize and onboard strategically relevant aggregator partners to enhance its visibility and outreach further among the target customer segments.

The future

CreditHaat has successfully navigated the challenges of building a startup over the past two years by managing available resources efficiently. With multiple partnerships already in the pipeline and the team’s indomitable spirit, its goal is to reach 5 million customers by July, 2023.

As a part of its long-term goal, the startup plans to diversify its product suite by offering investment, savings, and insurance products via its digital platform. CreditHaat aspires to add more aggregator partners to mobilize their existing field force and serve much-needed small-ticket credit products to LMI customers.

This blog post is part of a series covering promising FinTechs that make a difference in underserved communities. These startups receive support from the Financial Inclusion Lab accelerator program. The FI Lab is a part of CIIE.CO’s Bharat Inclusion Initiative is co-powered by MSC. #TechForAll, #BuildingForBharat.

Account Aggregator (AA) framework: Changing India’s financial ecosystem

Posted on by MSC

Read Nikhil’s Inc42 article on the Account Aggregator (AA) framework transforming FinTechs here.

 

GreyMatter: Delivering impact to farmers at the last mile

Posted on by MSC

GreyMatter is a startup under the Financial Inclusion (FI) Lab accelerator program’s fifth cohort. The Lab is supported by some of the largest philanthropic organizations worldwide—the Bill & Melinda Gates Foundation, J.P. Morgan, Michael & Susan Dell Foundation, MetLife Foundation, and Omidyar Network. MSC is a partner to the FI Lab​,​ part of CIIE.CO’s Bharat Inclusion Initiative.

As per the National Statistical Office’s (NSO) latest report, 31%[1] (93.09 million) of Indian households depend on agriculture as their primary source of income. Around 82% of these households are small or marginal, of which almost 70% of households spend more than they earn to meet their basic needs. This pushes them into a vicious cycle of debt—even for basic needs, let alone an emergency. The NSO report further warns that more than 50% of agricultural households are indebted, and the numbers continue to rise. Alarmingly, farm debt has increased by 58% in the last five years. The average outstanding loan per household stands at INR 74,121 (~USD 988) in 2018, compared to INR 47,000 (~USD 627) in 2013.

Figure 1: Average indebtedness per agricultural household in India

NSO’s report states that medium and large farmers took 69.6% of outstanding agriculture loans from institutional sources like banks, cooperative societies, and government agencies. A limited number of small and marginal farmers (SMFs) availed of such loans. The remaining SMFs borrowed money from informal sources like local moneylenders or friends and family. Respondents cited several reasons for availing of loans from informal sources—the farmer-applicants were unqualified to borrow from formal institutions, the processes were too lengthy, and interest rates against MFI-based loans were too high.

What does GreyMatter wish to solve?

Every day, SMFs in India battle multiple challenges—access to finance is just one among many issues. GreyMatter currently provides them access to finance and quality agri inputs, and is working towards helping them with other phases of the value chain.

GreyMatter’s current work focuses mainly on solving issues farmers grapple with during the initial phase of every crop season, as shown in the figure below.

Figure 2: Areas that smallholder farmers need assistance with

Upaz—from an idea on paper to its implementation as a unique product

These challenges compelled IIM Indore alumnus and Chartered Financial Analyst Neetesh to take matters into his own hands. He had been in the industry for more than 11 years and was associated with the OneAcre Fund before starting GreyMatter.

The industry largely believes smallholder farmers struggle the most with inadequate access to affordable formal finance, which hinders their growth. However, this statement is only partially true. Even if farmers get their hands on formal credit, they often fall prey to local agri-input traders who trick them into buying low-grade agri-inputs for a higher profit margin. If the farmers protest, the traders refuse to supply agri-inputs. Here, the farmers have almost no bargaining power and accept whichever agri-inputs the trader sells them.

Moreover, farmers lack suitable guidance and struggle to grow healthy crops. Unhealthy crops lead to a lower-than-estimated yield, further lowering their income from selling the produce. Since the farmers earn less, they find it difficult to pay their loan installments in full, which pulls them into a—largely informal—debt trap. This is a textbook example of the “domino effect,” as represented in figure 3.

Figure 3: The “domino effect” in smallholder farming, which leads to accumulated debt

This segment specifically needs effective interventions throughout the entire value chain. As shown in figure 2, these farmers need help with access to formal finance and access to quality agri-inputs alike, up until they sell their produce. A bid to solve this problem spurred Neetesh to create Upaz. The product is based on the buy-now-pay-later (BNPL) model, enabling smallholder farmers to access quality agriculture inputs at the best possible prices through affordable financing.

It was important for GreyMatter to ensure that the farmers use the funds only to generate income through farming and do not divert them for personal consumption. The NSO report notes another disturbing feature that only 57.5% of the total loans that respondents availed during the survey were explicitly used for agricultural purposes. This moved GreyMatter to provide in-kind credit to farmers in the form of agri-inputs instead of cash. The total loan amount is divided into six-month EMIs, which the farmers repay during the same crop season. The startup’s offering makes it convenient and affordable for farmers to purchase good-quality agri-inputs using a formal loan process that creates a credit score for them in the background.

GreyMatter employs an engine that recommends the right amount of agri-inputs for its users based on their land size and the crop type. Under the Upaz model, farmers can choose from an array of land size-based packages that apply to them, curated specially for a particular land size, which considers the region, soil types, and other factors that would determine different types or quantities of agri-inputs. Moreover, these packages are customized to fit both the crop seasons—Kharif and Rabi.

Figure 4: Example of different packages offered to smallholder farmers under Upaz

First, the farmers select a suitable package. Then a field officer places a collective order for agri-inputs on behalf of all the farmers in a particular panchayat or village. Within a few days, the company delivers the order at a pre-decided location in the village, which all the farmers can access. GreyMatter’s field officers then distribute the agri inputs equitably among farmers based on their orders.

Currently, GreyMatter procures its agri-inputs from national and state-level distributors. As it adds a substantially higher number of farmers to its network, it plans to procure products directly from manufacturers, as it could then place higher volumes of orders. This will lead to a further reduction in the unit prices of different agri-inputs.

What sets GreyMatter apart from other agri-commerce players out there?

Figure 5: Factors that differentiate GreyMatter from other service providers in the agricultural sector

Upaz can cater to different types of farmers who may have varying needs

Figure 6: Three use-cases where GreyMatter can add value to farmers

Support from the FI Lab

The Lab has offered a holistic support package to GreyMatter, ranging from mentor hours and grant capital to field studies led by financial inclusion experts in consumer and market insights. The support helped GreyMatter understand its users and their scope of usage better to build a more resilient, robust, and impactful solution.

The impact made till now and its vision for the future

GreyMatter has been operational since October, 2021. Currently, the service is live in the states of Bihar and Uttar Pradesh, with a direct impact on the livelihoods of more than 2,500 active users in its service network. However, the impact can be categorized further into the following:

Figure 7: Classification of the impact made by GreyMatter

Currently, GreyMatter is on track to expand to more than 240 villages in India, engaging a total user base of 10,000-plus smallholder farmers. While the startup is currently powered by only one financing partner, it will onboard a few more banks and non-banking financial companies (NBFCs) as financial partners. By 2024, GreyMatter intends to extend its services to 700,000-plus farmers like Bhagwan Singh, Avdoot Kumar, and Hemlata in India.

This blog post is part of a series that covers promising FinTechs making a difference for underserved communities. These startups receive support from the Financial Inclusion Lab accelerator program. The Lab is a part of CIIE.CO’s Bharat Inclusion Initiative and is co-powered by MSC. #TechForAll #BuildingForBharat

[1] Population of India, as of 2020 – 1.38 billion; Average size of a rural Indian household, as of 2012 – 4.6 persons