Credit Reference Bureau (CRB) integration in SACCO lending: Lessons from Uganda

Posted on by harshita

Uganda enters a new phase of formalization of its credit market. Recent data from the Bank of Uganda shows that the number of borrowers captured in the Credit Reference Bureau (CRB) systems increased from 2.9 million to 4.1 million between 2024 and 2025. During the same period, the credit inquiries rose by 28.4%, from 653,400 to 838,700. These trends reflect the growing use of credit data and risk-based lending across banks, savings and credit cooperative organizations (SACCOs), microfinance institutions (MFIs), and digital lenders.

At face value, this is a success story. Greater access to credit information should support better lending decisions and stronger repayment performance. However, emerging evidence suggests a more nuanced reality. While CRB use is associated with better performance, it does not guarantee stronger repayment outcomes.   

It is vital to examine how lenders use CRBs in loan screening and how that shapes borrower behavior over time to understand the outcomes. 

As the monitoring, evaluation, and learning (MEL) partner, MSC (MicroSave) Consulting examined this issue through a risk-based pricing pilot implemented under the wider MasterCard Foundation’s Micro and Small Enterprise Recovery Facility (MSERF). The Financial Sector Deepening (FSD) Uganda implemented the facility, while one selected SACCO participated in the pilot. MSC complemented these insights with borrower- and portfolio-level data from the gnuGrid CRB platform. 

Data from Uganda’s credit market indicates higher performance among institutions that use CRB systems. Evidence from portfolios supported under the MSERF reinforces these findings. Institutions that use CRB systems report significantly lower default rates of around 3.9% compared to 28.4% among non-users.  

 

Figure 1. Source: gnuGrid CRB 

However, the relationship between CRB use and portfolio performance is more complex than it first appears. Between 2022 and 2025, MSERF disbursed UGX 99.34 billion (~USD 26.7 million) to 22 participating financial institutions. The facility reached more than 334,000 borrowers and generated a loan volume of UGX 381.24 billion (~USD 102.5 million). Despite this scale and widespread CRB access, portfolio performance remains uneven. Default rates among CRB users have increased over time, and outcomes vary significantly across institutions, even among those using the same CRB systems. The difference is not access to data, but how institutions use it.   

The primary role of CRB data is to improve borrower selection. CRBs enable more informed credit decisions by providing visibility into repayment history, outstanding obligations, and exposure levels.

Figure 2. How loan officers evaluate credit reports 

However, in many institutions, CRB reports remain a procedural step. Staff pulls and reviews the reports, but they do not incorporate them into structured decision frameworks. Lending decisions continue to rely on personal judgment or informal criteria. 

By contrast, high-performing institutions translate CRB signals into clear decision rules, such as score thresholds, rejection criteria, and risk-based loan structuring. These rules lead to more consistent borrower selection and better portfolio outcomes.  

Evidence from the MSERF risk-based pricing (RBP) pilot illustrates how this transition plays out in practice. Analysis of matched loan portfolio data from the gnuGrid platform shows that, over time, structured SACCO scoring increasingly influenced pre-disbursement decisions, particularly between May and December 2025.  

During this period, clearer differentiation appeared across risk bands. The CCC and DDD received the largest average loan sizes, approximately UGX 9.26 million (~USD 2,452.97) and UGX 11.45 million (~USD 3,033), respectively. Despite this higher exposure, these segments recorded the lowest levels of current delinquency by value. In contrast, weaker segments underperformed despite lower exposure. The EEE showed weaker repayment outcomes, and III exhibited the highest PAR30, PAR60, and PAR90 levels. 

These patterns suggest that the scoring model effectively ranks borrower risk, and that value is realized when scores are actively used in pre-disbursement decisioning. When this occurs, scoring results in improved risk differentiation and more efficient credit allocation. 

However, the effectiveness of such systems depends on use and on how scoring models are designed. Emerging evidence highlights several constraints that affect accuracy and fairness. 

First, data source gaps can introduce structural bias. For example, when Mobile Network Operator (MNO) data is used only partially, borrowers on one network may receive an implicit advantage, while others are penalized despite similar behavior. In such cases, scores reflect data availability rather than true risk. 

Second, static representations of risk, such as lifetime maximum arrears, can misrepresent borrower behavior. Borrowers who experienced temporary shocks, such as during COVID-19, may continue to be penalized even as their repayments improve. This weakens incentives for recovery and misaligns with the goals of programs, such as the MSERF, which aim to support resilience. 

Third, the weighting of risk variables can unintentionally reinforce exclusion. For example, reliance on collateral as a primary risk determinant may disadvantage women, whose repayment performance is often strong, but asset ownership is limited. While SACCOs often mitigate this through guarantorship, these mechanisms are not always reflected in scoring models, creating a disconnect between actual risk mitigation and model outputs. These issues highlight that the value of CRB data is shaped by institutional use, model integrity, and contextual relevance.

Figure 3: Sample SACCO credit report 

The gap between CRB access and effective use is most visible among SACCOs. In practice, this gap lies not in data availability but in how CRB data is used within the lending process. 

Figure 4: A typical SACCO lending process flow with CRB data 

Despite their central role in financial inclusion, many SACCOs lack the systems and capacity needed to translate CRB data into consistent lending decisions. A primary constraint is limited digitization. Many SACCOs rely on manual processes and fragmented systems, which make it difficult to integrate CRB data into workflows or generate consistent assessments. 

Governance and capacity challenges further weaken credit decision quality. Weak governance leads to inconsistent application of credit policies, while high override rates undermine discipline. Limited staff capacity to interpret CRB data reduces its effective use. 

Data ecosystem gaps also persist. CRB data primarily captures formal credit histories, but many SACCO clients operate informally with limited documentation. Without complementary data, such as cash flow or behavioral indicators, risk assessment remains incomplete. 

SACCOs also face a persistent tension between inclusion and risk management. Without strong systems and segmentation frameworks, this often results in either a rise in defaults or overly conservative lending. 

In addition to these operational constraints, SACCOs face emerging challenges related to scoring model design. These models rely on incomplete data sources, static risk measures, and heavily weighted collateral variables; their outputs may not reflect borrower realities. This is particularly important in SACCO contexts, where informal income, guarantor-based lending, and recovery trajectories shape repayment behavior. Without continuous calibration, scoring models risk reinforcing bias instead of improving decision quality. CRB systems influence lender decisions and borrower behavior. 

When repayment histories are recorded and shared, borrowers have stronger incentives to maintain discipline. Timely repayment becomes valuable, as it builds a credit history and improves future access to credit. This is reflected in stronger performance among institutions that actively use CRB data. 

However, this effect depends on system credibility and visibility. Many SACCO borrowers remain partially outside the formal credit systems, which reduces the immediate consequences of default. As a result, incentives to maintain strong repayment behavior are weaker. 

Rising default rates among CRB users, as depicted in Figure 1, reinforce this point. CRB data improves initial screening but does not guarantee sustained repayment performance as lending expands into new or higher-risk segments. Stronger outcomes occur where CRB data is embedded within broader systems that combine multiple data sources, continuously monitor borrower performance, and adapt lending decisions accordingly.  

This is particularly important for underserved segments, such as youth borrowers, whose thin credit histories require complementary indicators to support both inclusion and repayment.   

Uganda’s credit ecosystem is at an inflection point. Access to borrower data has expanded, but the next phase depends on how that data is used. The key shift is from data access to decision intelligence. This requires embedding CRB data into credit policies and decision frameworks, the combination of CRB data with internal and behavioral data, the strengthening of governance, the reduction of discretionary overrides, and investment in digitization to enable consistent, scalable decision-making. For funders, this means moving beyond capital toward capability-building. For financial institutions, it requires rethinking how credit decisions are structured and executed. 

Uganda’s credit ecosystem has moved from data scarcity to data availability but has not reached decision-level integration.  

Figure 5. Financial infrastructure maturity framework that illustrates the transition from basic functionality to fully integrated and interoperable systems 

For MSERF 2.0 and similar initiatives, this requires a shift in focus to support SACCO digitization, pairing capital with technical assistance to strengthen credit risk management, data use, and institutional governance, and tracking decision quality alongside loan volumes. 

Uganda’s experience shows that CRB systems improve transparency, but their impact depends on how effectively they are integrated into decision-making and how accurately scoring models reflect borrower realities. Poorly calibrated models can introduce bias, misrepresent risk, and weaken incentives for positive repayment behavior. 

The focus has shifted from CRB adoption to effective CRB use for SACCOs and financial institutions. The key question is how to turn CRB data into better decisions, and ultimately, better outcomes. 

Building community-managed service systems around rural livelihood assets in Bahraich, Uttar Pradesh

Posted on by harshita

In Bahraich, Uttar Pradesh, a solar irrigation pump is doing more than lowering diesel use. Managed by the Ekta women’s group in Mohanpur Mafi, it helps farmers irrigate more reliably, cultivate across more seasons, and sell irrigation water to nearby farmers when local demand within the group falls. A household biodigester adds another layer to the rural economy. It converts cattle dung into cooking gas and returns slurry to farms as an organic input. At the bio resource center (BRC), women associated with the Udyami Mahila Producer Company Ltd. (UMPCL), a farmer producer company (FPC), produce bio-inputs that offer farmers a lower-cost pathway to soil health management, pest control, and natural farming, with institutional support from the Trust Community Livelihoods (TCL).

The TCL model works because it connects energy, water, soil, livestock waste, and women-led institutions within one livelihood system. For practitioners who work in decentralized renewable energy and regenerative agriculture, Bahraich raises an important question.How can rural livelihood assets across energy, water, and regenerative agriculture move beyond supported pilots and become locally governed service systems that create recurring value for farmers? 

Many rural energy programs focus primarily on assets, such as pumps, biodigesters, solar panels, or clean cooking units. In Bahraich, the model builds an ecosystem around these assets to support local economic development in agriculture. 

This blog examines three priority assets and how communities use, pay for, maintain, and convert them into recurring value. 

1. Renewable energy as community-managed infrastructure 

The solar irrigation system in Mohanpur Mafi offers a strong lesson in service delivery. The system includes 15 solar panels and a 5 horsepower (hp) pump. It currently irrigates about 12.5 acres and can potentially serve 20 acres, along with nearby farmers. 

The women-led group manages water distribution, payment collection, recordkeeping, and group savings. As a result, the system functions as a community-managed service model, rather than a simple diesel replacement. The immediate gain is lower diesel dependence. The deeper benefit is a shift in cultivation practices. Reliable irrigation allows farmers to cultivate crops during the Kharif, Rabi, and Zaid seasons. These seasons correspond to the monsoon, winter, and short summer cropping periods. 

Before the solar irrigation system, diesel costs and supply uncertainty made Zaid cultivation difficult for many farmers. The system changes the cropping calendar by providing water when farmers need it. 

Group members pay a reported USD 0.026 or INR 2.5 per unit of water, while non-members pay a reported USD 0.031 or INR 3 per unit. The group deposits the revenue into its savings account. The pump supports crop production, generates local revenue, and strengthens community ownership. 

The shift in this approach matters because the scale of decentralized renewable energy depends on ownership, pricing, maintenance, and recurring user value. During non-irrigation periods, communities can assess productive uses, such as flour milling or local processing. Any expansion, however, requires clear demand, load requirements, pricing, and maintenance arrangements. 

This aligns with broader evidence that solar irrigation can reduce energy costs and improve farm productivity. Bahraich’s sharper lesson, though, is the women-managed service model built around the pump. 

2. Biodigesters and the household-farm loop 

The biodigester represents the household-level component of the model. It operates within the household economy and depends on livestock ownership, daily dung availability, cooking needs, and the household’s ability to use slurry on farms. The household that the authors visited during the field visit reported that the biodigester provides about three hours of cooking gas each day in normal conditions. The model requires two buffaloes or three cows, with a daily dung output of 30–40 kg. 

Before adopting biogas, the household reportedly spent about USD 125 or INR 12,000 each year on liquefied petroleum gas (LPG). The family also relied on firewood and cow-dung cakes. The benefit is, therefore, reduced fuel dependence, rather than full replacement of all cooking fuel across all seasons. 

Gas production declines during winter because lower temperatures slow digestion. Hence, households may require backup cooking options.

Veena Devi, a resident and user of the biodigester in Bhraich, reported that the biodigester reduced the need to make dung cakes and collect firewood. It also simplified cooking and kept utensils cleaner. The household applies slurry from the biodigester to fields, particularly during plowing. Farmers can also use the slurry to prepare bio-inputs.

 This finding aligns with broader evidence from India that household biogas can reduce women’s fuel-collection burden while producing digestate that supports crop nutrition. The biodigester also connects directly to the regenerative agriculture component of the model. Household livestock waste becomes cooking fuel, and slurry returns nutrients to the farm system.

3.The BRC as a community bio-input system 

The BRC anchors the regenerative agriculture component of the model. Women produce bio-inputs, conduct demonstrations, and support producer structures that connect products with local demand. The center produces products that address practical farm needs. Mitha supports crop growth, khatta supports disease management, and kadva helps control pests. Farmers use these products on cereals, vegetables, spices, and high-value crops. 

Field discussions suggest a significant cost advantage. Chemical inputs for a typical farm size of 0.2 acres may cost about USD 21 or INR 2,000. Bio-inputs from the BRC may cost approximately USD 2–3 or INR 200–300 for the same area, based on crop requirements and field conditions. 

Adoption spreads through demonstrations, peer learning, TCL awareness sessions, and visible crop performance. The BRC therefore supports a local system in which women produce inputs, demonstrations build farmer confidence, and producer structures bring products closer to users. The center also connects directly to the decentralized renewable energy interventions. Solar irrigation improves water availability for more intensive cultivation. At  the same time, BRC inputs help farmers manage soil health and input costs. 

The key lesson from the TCL Bahraich model is that assets are only the starting point. The surrounding system turns energy, water, soil nutrients, and women-led institutions into recurring economic value. The TCL and MSC (MicroSave Consulting) now seek ways to translate this experience into an investable pathway for decentralized renewable energy, regenerative agriculture, and rural income growth. 

Rewiring the system: Financial education as infrastructure, not isolated programs

Posted on by harshita

This brief reframes financial education as a supporting component of financial capability infrastructure rather than a primary consumer protection tool. Global evidence and MSC’s experience across 50+ countries reveal that financial education delivers modest but durable results when targeted, behavior-linked, and delivered at teachable moments. Meaningful consumer outcomes depend on structural interventions, such as product design, disclosure standards, provider accountability, liability frameworks, and effective grievance redress mechanisms.

Can early warning systems prevent nutritional insecurity before diets fail?

Posted on by Nashrah Shareef

When floods affect a village in India, the nutritional impact does not begin with visible malnutrition. It often begins with disrupted roads, weaker markets, lost wages, higher food prices, and reduced access to health and nutrition services. Households may cut back on eggs, lentils, vegetables, fruits, or milk early, long before any child is identified as underweight or wasted. By the time these effects appear in a register or database, families may already have lost weeks. Nutrition systems still respond after malnutrition becomes visible worldwide. Nutritional vulnerability may already have built silently for months by the time support reaches those communities. 

Over three billion people suffer from malnutrition in all its forms, while more than 150 million children suffer from stunted growth due to chronic undernutrition. The World Health Organization (WHO) reported in 2025 that climate change will likely place millions more at risk of malnutrition in the next decades. This scale of nutritional risk frequently remains undetected until late stages. A 2025 study using Egypt’s Demographic and Health Survey data showed that deep neural networks can help predict children’s nutritional status, highlighting the potential of advanced analytics for earlier risk detection.  

Nutrition systems must now respond to risks that become more frequent, interconnected, and difficult to predict. A failed harvest, floods, or income shock may not immediately lead to visible hunger, but it can quickly affect the quality of household diets.  

Emerging approaches across low- and middle-income countries show how different elements of anticipatory nutrition systems are already taking shape. 

In Bangladesh, flood forecasting has been linked to anticipatory social protection, which demonstrates how climate risk signals can trigger early support for vulnerable households before shocks fully translate into food and nutrition stress. In Peru, national efforts to reduce chronic child malnutrition demonstrate the value of integrated monitoring, in which anthropometric, dietary, and maternal-child nutrition indicators are used together to track vulnerability and guide public action. In Rwanda, mHealth and digital community health systems underscore the importance of last-mile response capacity, which enables frontline workers to use real-time information to improve maternal and child health service uptake. Together, these examples show that anticipatory nutrition governance is not built through prediction alone; it requires systems that connect risk detection, program targeting, and local response. 

This is critical because nutrition risks rarely appear suddenly. In most contexts, deterioration starts with a gradual decline in diet quality. Families may reduce consumption of vegetables, fruits, or protein-rich foods as climate shocks affect supply chains or incomes fall. Pérez-Escamilla and Lott examined these patterns in their 2019 chapter on food insecurity and nutrition insecurity. They documented how measurement challenges can obscure early stages of dietary decline.   

The consequences of delayed detection are significant. Children may already face weakened immunity, developmental delays, and long-term growth impacts by the time severe malnutrition becomes visible. Families may already be under severe stress while health systems respond to acute crisis conditions, rather than prevent them. In 2024, UNICEF reported on nutrition and care for children with wasting and documented these cascading effects.   

Our current models are mostly reactive and risk becoming increasingly unsustainable as climate volatility intensifies and economic shocks become more frequent. 

Advances in artificial intelligence (AI) and digital systems create opportunities to shift nutrition governance from reactive response toward anticipatory action. Researchers and governments now explore how integrated data systems can identify nutritional risks before severe outcomes emerge. The World Food Programme’s work on predictive analytics shows how integrated data and risk modeling can support earlier humanitarian action and strengthen food security responses before crises escalate. 

MSC is committed to advancing inclusion in the digital age, with AI increasingly central to how we think about impact at scale. Our recent role in launching the Alliance for Inclusive AI reflects this priority and helps ensure that AI serves underserved populations, while supporting practical, responsible solutions across sectors. For nutrition, this means looking beyond prediction alone. Our work across public systems, agriculture, health, and nutrition shows that digital tools create impact only when embedded within strong delivery systems, trusted institutions, and last-mile implementation models. Anticipatory nutrition systems must follow the same logic. AI and predictive analytics can generate early signals, but governments and frontline systems must act on them before nutritional stress deepens. Our experience points to four key enablers that need to be strengthened: 

  • Data systems for early nutritional risk detection 

First, most existing nutrition monitoring systems measure outcomes rather than predict risks. Future systems must prioritize real-time and forward-looking indicators that capture dietary diversity, micronutrient adequacy, and food affordability before severe malnutrition emerges. 

This system requires integration of diverse datasets into unified nutrition intelligence systems that support early warning and rapid response. These datasets include climate information, epidemiological data, food market trends, health service utilization, dietary indicators, and household vulnerability data. Such systems must also be designed with strong data governance, privacy, and security safeguards to ensure that sensitive household, health, and vulnerability data is collected, shared, and used responsibly, with clear protocols for consent, access control, anonymization, and accountability. 

  • Interoperable systems across sectors 

Secondly, nutritional insecurity is shaped by interconnected systems, such as agriculture, health, climate, water, and sanitation. Yet, these systems often function independently, which limits coordination and slows down response times.  

Anticipatory nutrition governance depends on interoperability across sectors. Acute weather data systems should connect directly with nutrition surveillance platforms, epidemiological data systems, and local health systems. This connection allows emerging risks to trigger coordinated action more effectively. For central governance systems, this means moving from ministry-specific dashboards to interoperable command systems that allow different ministries to share risk signals, define response thresholds, and coordinate action through common protocols.  

  • Digital infrastructure and frontline capacity 

Thirdly, even the best predictive systems are ineffective if local actors cannot act on the information they generate. Governments need stronger digital public infrastructure and local response systems that enable real-time and decentralized decision-making. Frontline health workers, community nutrition cadres, and local governments need access to timely, localized, and actionable information that supports rapid identification of emerging nutrition risks at the community level. 

This requires sustained capacity-building so that these local actors can interpret risk signals, use digital tools confidently, and translate early warnings into timely household outreach, service referrals, and local response planning. 

  • Behavior change and preventive interventions 

Finally, link anticipatory nutrition systems to targeted interventions that help households maintain diet quality before nutritional outcomes worsen. Insights from these nutrition surveillance systems should inform the timely delivery of nutrition-sensitive social protection, targeted supplementation, maternal and child nutrition services, and community-based counseling. Behavior change communication strategies must also be tailored to emerging risks to help households sustain dietary diversity, appropriate feeding practices, and care-seeking behaviors during periods of climatic, economic, or food system stress. 

The way forward: From predicting nutrition risks to preventing them 

At a macro level, nation-states require systems that can detect risks early, connect signals across sectors, and translate insights into rapid local action. 

Emerging approaches across the Global South demonstrate how integrated systems can identify vulnerability before crises deepen. Evidence on digital health delivery in resource-limited settings also shows that omni-channel, outcomes-focused approaches can help scale interventions when they are designed around users, service delivery pathways, and measurable outcomes. 

The next frontier is to bring these approaches together into integrated nutrition intelligence systems. This means connecting nutrition surveillance, acute weather data, digital community health platforms, and behavior change interventions so that risks are detected early and translated into timely action. WHO’s work on integrated surveillance and climate-informed health early warning systems offers a useful direction for how these linkages can operate in practice. Investment in such systems is both a public health and economic priority. The World Bank estimates that every dollar invested in nutrition interventions can generate USD 23 in economic returns through improved health, productivity, and human capital outcomes.  

The way forward is to expand nutrition interventions while also building the digital and institutional systems to target them earlier, more effectively, and at scale. The strength of future nutrition systems will depend on their ability to detect vulnerability before it becomes visible. Predictive tools can help identify emerging risks, but strong implementation models and last-mile delivery systems are essential to translate early signals into timely action and better outcomes. 

Financing Africa’s fisheries: From informality to investability

Posted on by Nashrah Shareef

Kamarinyang Aqua Park in Busia County, Kenya, illustrates both the promise and the financing challenge within Africa’s fisheries sector. The Kenya Climate-Smart Agriculture Program was established around 2021, in partnership with the County Government of Busia. The Aqua Park received more than KES 65 million (~USD 503,000) in investment and established 78 fishponds. 

Yet, infrastructure alone did not result in a functioning enterprise, as the ponds remained unstocked. Project members also lacked the financial capability and collective management systems needed to operate the facility productively. This gap highlights how grant-funded initiatives can struggle to sustain impact without a viable market or a clear enterprise pathway. 

The situation changed in March 2025, when 130 members received financial education and training on group dynamics from MSC (MicroSave Consulting) and the Association of Women in Fisheries Blue Economy Kenya (AWFBEK). Members reorganized into sub-groups, assigned pond responsibilities across women, youth, persons with disabilities, and cooperative members, and agreed to contribute KES 100 (~USD 0.77) every two weeks to support joint activities. 

By June 2025, the ponds were stocked with tilapia and catfish, and the first harvest followed in February 2026. The shift went beyond pond use to include greater ownership, accountability, and readiness to engage more effectively with formal and digital finance, such as the Chama app. 

The Kamarinyang story goes beyond financial education. It shows how enterprise support financial capability and how sector-appropriate finance must come together to move fisheries enterprises from informality toward investability. It also reflects a wider challenge across the fisheries sector. 

Africa’s fisheries and aquaculture sector faces two competitive realities. It is already a major source of food and nutrition, with aquatic foods supplying about 18% of animal protein in Africa. At the same time, the continent faces a projected annual fish deficit of 11 million metric tons by 2030 unless supply expands significantly. Meeting this demand will require a substantial increase in production. 

Aquaculture is widely seen as a sustainable and viable pathway. Yet, the investment needed to scale production in Africa has not materialized, with, according to recent analyses, an estimated annual financing gap of approximately USD 12 billion.   

Financial institutions often perceive the fisheries sector as inherently high risk and commercially unattractive. Some of this risk is real, driven by weak records, limited collateral, long production cycles, and high transaction costs relative to the size of small loans. Yet, this perceived risk also reflects limited familiarity with the sector, as indicated by MSC’s engagement with partner financial institutions (PFIs). 

Many lenders do not fully understand how fisheries enterprises operate, such as their seasonality, cash flow patterns, margins, growth trajectories, and risk exposure. This limits their ability to assess creditworthiness or design products that meet their working capital and investment needs. In aquaculture, this challenge is even more pronounced, as lenders must assess biological risks, such as disease outbreaks, stock losses, and weather-related shocks. They often lack the sector knowledge needed to interpret these risks effectively.  

Yet, fisheries enterprises conduct substantial commercial transactions and demonstrate strong business discipline, despite constrained resources and market conditions. Young women and men are central to the fisheries economy, which accounts for 70% of employment in the sector across East and Southern Africa.  

Many transactions are organized through Beach Management Units (BMU), chamas, and other local networks that help businesses coordinate, save, borrow, and trade. The problem is that much of this economic activity is informal and poorly documented. As a result, businesses with real turnover, market relationships, and demonstrated financial discipline remain largely invisible to lenders.  

MSC’s work in the sector reveals the intensity of this activity and the extent of unmet financial need. These enterprises cannot grow, modernize, or scale operations without finance or acquire productive assets. They remain commercially active but structurally constrained.

High-cost informal borrowing diverts cash from already thin margins, limiting traders’ ability to reinvest profits and expand their businesses. These examples highlight the sector’s unrealized potential. If these enterprises can survive, adapt, and continue transacting under such constrained conditions, their growth potential with access to appropriate finance, assets, and business support is likely to be far greater. 

The financing challenge in fisheries cannot be solved through a single intervention. When enterprise transactions are poorly documented and business practices remain informal, financial institutions struggle to assess cash flows, governance, repayment capacity, and risk. As a result, viable businesses may be excluded from credit or offered products that are too costly, too short-term, or poorly aligned with their operating cycles. This weakens repayment capacity and discourages demand for formal finance, even among commercially active enterprises.   

These constraints require financial education and enterprise support. Savings discipline, record-keeping, technical training, and stronger group governance help translate informal economic activity into information that financial institutions can use. While they cannot eliminate all risks, they reduce uncertainty and make businesses easier to understand and assess. 

Financial education also influences how entrepreneurs engage with financial services. Familiarity with financial terms, product features, loan conditions, and repayment obligations can support better borrowing decisions and long-term financial health. 

However, effective financial behavior requires more than training. Financial education is more likely to influence behavior when it is practical, delivered at teachable moments, and linked to timely access to relevant financial solutions. This approach allows entrepreneurs to apply and consolidate new knowledge and build financial capability through use. 

MSC’s ongoing work under Women and Youth Economic Empowerment in Fisheries through Inclusive Market Access (WYEEFIMA) reflects this approach. This project combines enterprise support with financial sector engagement to improve financial readiness and unlock more suitable pathways to inclusion, sustainability, and job creation. 

The following table summarizes how specific financial capability interventions address key constraints and support better outcomes for fisheries enterprises.  

Financial education lays the foundation for graduating from informal mechanisms to structured financial services. MSC’s post-training results in Busia show how stronger financial capability can improve financial readiness by making business performance and risk more visible and manageable. 

For financial institutions, the opportunity extends beyond increased lending to fisheries enterprises. It includes creating structured pathways that help commercially active enterprises become finance-ready and bankable. They must distinguish the needs of different actors, such as working capital for traders and asset finance for producers, and design products that reflect production cycles, cash flow patterns, and repayment realities. These institutions must also improve underwriting for informal enterprises by working with partners to strengthen financial capability, governance, record-keeping, and transaction visibility. 

Africa’s fisheries sector has strong demand and a diverse enterprise base. Yet, it lacks a financing system that understands the sector’s business cycles and uses increasingly accessible, high-quality data to recognize viable businesses and inform product specifications. Financial institutions, value-chain actors, development partners, and local enterprise networks must act in coordination to close this gap. Together, these stakeholders can strengthen business discipline, improve visibility, and design fit-for-purpose financing that enables enterprises to grow.  

Over the next three years, MSC will partner with financial institutions and sector partners to test practical pathways to expand finance for commercially active fisheries enterprises. We invite financial institutions to collaborate  to strengthen fisheries portfolios through better underwriting, fit-for-purpose products, and enterprise support. 

Migration as a household investment and why finance arrives too late

Posted on by harshita

Households in Bangladesh with family members working abroad celebrate remittances as a lifeline with profound economic and social significance. They spend months or even years assembling the resources needed to send earners overseas and must wait for the first remittance to arrive. In communities that send migrants, such as Tangail, families gradually scrape together savings and loans to finance recruitment costs, travel, and documentation. 

One BURO member described how she liquidated a savings account she had built for more than 17 years to finance her son’s migration to Saudi Arabia. She chose savings rather than a loan because loan repayment begins immediately, while income from migration can take time to stabilize. 

Among BURO clients in Tangail, the decision to send someone abroad is often discussed as part of a broader household strategy. Migration offers the potential for higher income, but it also involves substantial uncertainty. The financial decision comes with an emotional cost. During fieldwork, we met many women whose husbands or sons could only return home once every two or even three years. Families are forced to navigate long periods of separation alongside the financial risks migration entails. 

Families must assemble significant funds before departure to cover recruitment fees, travel costs, and documentation. The World Bank’s Migration and Development Brief estimates that remittance inflows to Bangladesh totaled around USD 23 billion in 2024. Yet behind these national figures lie thousands of household decisions about whether migration is financially possible and how the risks will be managed. 

These decisions are rarely individual. In households, family members weigh risks and potential returns together. Savings frequently play a central role in these discussions, as households gradually accumulate funds that can support migration expenses or reduce reliance on borrowing. 

Field observations among BURO members in Tangail suggest that households rarely rely on a single financial source to fund migration. Instead, families assemble resources through a layered approach. Savings accumulated over many years often form the foundation. Long-term deposits allow households to gradually build capitals that can be mobilized when migration opportunities arise. These savings reduce the amount households need to borrow and provide a degree of financial flexibility during uncertain periods. 

Households then layer loans on top to cover the remaining costs. For example, one BURO member described taking a loan of BDT 200,000 (USD 1,630) to support her son’s migration abroad, which is an example of how borrowing often complements existing savings rather than replacing them. Migration costs are higher than household income, so families often commit resources well before departure, without certainty about when migration income will begin. 

How remittances reshape financial behavior 

The flow of remittances is rarely smooth or predictable. Migrants often need time to secure stable employment, and transfers may arrive irregularly based on working conditions and payment arrangements. During this early phase, households continue to rely on savings to manage uncertainty. 

Remittances also appear to shift financial behavior within some communities. In several BURO centers in Tangail, staff observed that households that receive regular remittances often rely less on loans and instead accumulate savings from these transfers. In one center, 13 of 16 households had family members working overseas, and women were saving remittance income through Deposit Pension Scheme (DPS) accounts.  

Field observations also showed how remittance income translated into tangible household investments over time. In these BURO centers, families used savings from remittances to purchase land, improve housing, invest in livestock, and finance vehicles, such as autorickshaws, which supported local income generation. 

In one household, regular remittances of around BDT 80,000–100,000 (USD 650-815) per month enabled repayment of a housing loan worth BDT 400,000 (USD 3,261) while the family continued to save through contractual savings accounts for children’s future education and security. Because many men work abroad, women often become the household’s financial managers. They receive remittances, decide how much to save, and allocate funds for education, assets, or emergencies. 

In some households, migration also becomes cyclical. Family members who migrate earlier help finance the departure of other family members to create repeat migration pathways within the same household. Cleaner guidance on investment options are required. 

The timing gap in migration finance 

Migration decisions expose households to multiple risks long before any income begins to flow. Families must rely on recruitment intermediaries whose costs and reliability are often uncertain, commit to savings or loans without guarantees about employment conditions, and prepare for the possibility that wages may be delayed or lower than expected after arrival. In some cases, migrants discover that jobs differ from what was promised or that living expenses abroad reduce the amount they can send home. During this period, households need to repay migration-related loans even though remittance income has not yet begun. 

At this stage, wrong decisions can have significant consequences. A Business Standard report shows that workers who travel under the apparently “free visa” arrangements lost an estimated BDT 30,000 crore (USD 25 million) in 2022 alone due to inflated recruitment costs and fraudulent intermediary practices. Migration monitoring by Andy Hall has also documented cases in which Bangladeshi workers paid large recruitment fees, only to arrive overseas and find that promised jobs did not exist or wages were withheld for months. As a result, families at home struggle to repay migration-related loans without remittance income. 

This creates a timing gap. Financial systems tend to engage once remittances begin to arrive, which focuses on transfers and mobilizing savings. These services are valuable, but households need support when they prepare for migration and manage the risks that make migration possible in the first place.  

This gap points to an opportunity to rethink how those services support migration.  

Financial risks are the highest before departure. So, households can benefit from tools that combine savings, financing, and information. Structured savings products could help families gradually accumulate migration funds, while migration-linked loans could bridge financing gaps through repayment structures aligned with migration timelines. For example, a combination of savings accumulation with short grace periods or phased repayment schedules can reduce financial strain on households before remittance income begins. 

Once remittances begin, financial services can shift to help households manage these flows effectively. Flexible savings tools and advisory services can help families convert remittance income into long-term financial resilience and productive investments.  

Current efforts to support migrant households 

Moreover, BURO has initiated the “Remittance Management for Socioeconomic Stability of Migrant Families” initiative under the Safal program, supported by the UNCDF and the Swiss Embassy. This initiative is being implemented in selected branches in Tangail and Munshiganj. It strengthens how migrant households plan, manage, and use remittance income effectively and represents an important step toward more lifecycle-oriented engagement with migrant families. 

While these efforts represent important progress, they also highlight the opportunity to expand structured financial support across the migration journey.  

Designing for the realities households face 

Households will continue to pursue migration with or without formal financial support. For many families, it represents a rare pathway to higher income and improved opportunities. The challenge is therefore not whether migration occurs, but how to reduce the risks surrounding it. Better financial preparation can help households avoid excessive debt, reduce exposure to fraudulent intermediaries, and manage the uncertainty that often accompanies the early stages of migration. 

Migration is more than a story of labor or remittance. It is also a financial journey. Financial systems that engage before households take risks can help them prepare better and convert remittances into a secure pathway toward resilience and opportunity.