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Unlocking capital for the WASH sector at scale

Posted on by harshita

The water, sanitation, and hygiene (WASH) sector has no shortage of need but lacks proof. Until investors can clearly see where their capital goes, how it performs, and the outcomes it delivers, private finance will remain far below the scale needed to close the global WASH gap. And this gap today is massive. As per the WHO–UNICEF Joint Monitoring Program (2025), 2.1 billion people still lack access to safe drinking water. Another 3.4 billion lack basic sanitation, while 1.7 billion lack basic hygiene. Closing these gaps by 2030 will require almost three times the current investment. Public budgets cannot provide the additional USD 100 billion each year.

The funding shortfall is structural. Developing countries spend nearly USD 165 billion each year on water infrastructure. Public finance accounts for 91% of this amount, while private investment accounts for less than 2%. Tariffs, taxes, transfers, and official development aid provide the rest. Governments cannot increase public spending due to competing fiscal priorities, high debt, and the long payback periods of WASH investments. WASH also receives limited climate finance.  

Of the USD 1.9 trillion tracked in 2023, only USD 49 billion was allocated to water and wastewater management. Private capital has therefore become essential. Mobilizing private capital is therefore a necessity, not a convenience. 

The sector has reached an inflection point. Impact investment in WASH grew by 33% between 2015 and 2019, which made it the fastest-growing sector tracked by the Global Impact Investing Network (GIIN). Development finance institutions, such as the International Finance Corporation (IFC), FMO-Entrepreneurial Development Bank, and DEG, now co-invest with private partners across Asia and Africa. Yet, capital flows remain modest. The constraint is not in appetite but in the evidence generated. 

How fragmented reporting holds back WASH investments 

Credible impact measurement is essential to attract private capital to WASH. Investors and development finance institutions rely on standardized metrics to assess portfolio performance, compare investments, and show results to stakeholders. 

The lack of a harmonized measurement framework creates fragmented reporting requirements. Financial service providers (FSPs) must satisfy multiple reporting standards. This duplication increases reporting fatigue and limits comparability across portfolios and geographies. Existing WASH frameworks measure service access and public expenditure well. However, they do not meet investor needs or assess investment performance effectively. A review of existing monitoring tools identified four major gaps that continue to constrain private capital in the WASH sector: 

  • The tools track service-level and policy indicators but capture limited data on private finance flows and loan performance to be useful for capital allocation decisions. 
  • They require FSPs to track and report on indicators that sit outside their existing management information systems (MIS), which creates a reporting burden.  
  • They measure outputs at the institutional or portfolio level, but lack the granularity to attribute health, income, or service outcomes to individual clients or loans. 
  • They allow each investor or framework to define their own indicators and methodology, which leaves no common measurement language to compare investments across portfolios and sectors. 

Other sectors demonstrate the value of harmonized reporting. The Green Bond Principles helped expand the green finance market from less than USD 1 billion in 2012 to more than USD 500 billion annually within a decade. Standardized reporting also accelerated microfinance growth. WASH now needs the same foundation. A common measurement language can give investors the confidence to commit capital at scale. 

Closing the evidence gap through the e-MFP WASH action group impact indicator framework 

The European Microfinance Platform (e-MFP) WASH Action Group impact indicator framework was developed to address the gaps identified above. It provides a practical method aligned with global standards for institutions that work in resource-constrained environments. The framework has three goals:  

  • Reduce reporting burdens on FSPs; 
  • Improve data comparability across portfolios; 
  • Strengthen the link between finance deployed and outcomes generated. 

The framework draws on internationally recognized standards, including the WHO–UNICEF Joint Monitoring Program, UNICEF’s Global Framework for Urban Water, Sanitation, and Hygiene, and WHO’s guidance on water, sanitation, and hygiene. It provides a consistent basis for measuring WASH investments across geographies. 

The framework  includes 11 core indicators across four dimensions: Financial, social, climate, and service levels. Financial indicators show how capital performs. The remaining dimensions measure the changes that investment creates on the ground. Together, they provide the evidence base that responsible investors need. 

Each core indicator includes sub-indicators that allow disaggregation by borrower segment, enterprise size, gender, geography, WASH subsector, loan characteristics, climate contribution, and service levels. This level of detail helps financial institutions understand how much capital they should deploy, who receives it, how it performs, and what outcomes it achieves. 

From design to reality 

The framework evolved through three phases. During Phase 1 (2022-2023), e-MFP and Aqua for All commissioned the financial advisory firm Rebel to develop the WASH handbook and a draft indicator framework. This work established the conceptual foundation for standardized WASH impact measurement. During Phase 2 (2024), MSC (MicroSave Consulting) evaluated the draft with asset managers, financial institutions, and WASH SMEs. The assessment examined its practicality and alignment with existing measurement practices. 

Phase 3 refined and operationalized the framework. FSPs across Asia and Africa, along with data platforms, tested the framework. The pilot assessed whether institutions could embed it within existing systems without creating an unsustainable reporting burden. The exercise mapped institutional data, reviewed MIS systems, and assessed data granularity. It also identified gaps in reporting capacity and in WASH-related information. 

Rather than introducing parallel reporting systems, the pilot incorporated missing data fields into existing MIS wherever possible. Where FSPs lacked WASH or climate classifications, the pilot introduced tagging mechanisms and targeted system modifications that institutions could integrate into routine reporting. 

Figure 1: Classification of indicators by reporting effort and data collection method 

The pilot also refined the original 16 indicators into 11 core indicators. It retained, redefined, merged, or removed indicators based on operational feasibility and analytical relevance. The project developed a suite of practical tools to support implementation. These include an operational manual with standardized indicator definitions and reporting guidance. They also include standardized MIS templates for portfolio and financial indicators, as well as structured survey instruments and digital data-collection tools for outcome indicators that MIS cannot capture.  

The framework classifies indicators as MIS-based or survey-based. It also groups them by reporting effort: Low for MIS-derived indicators, moderate for survey-based indicators, and high for indicators that require retrospective classification. This approach supports phased adoption based on institutional readiness. Together, these tools help institutions integrate the framework into existing reporting systems and promote consistent and comparable WASH reporting.  

Figure 2: Phases in the development of the WASH action group framework 

The road from framework to ecosystem adoption 

The e-MFP WASH Action Group has developed the data architecture, tools, and guidance needed for credible, comparable, and decision-useful WASH impact measurement. The pilot confirmed an important lesson: Impact measurement creates value only when investment decisions incorporate it from the outset. It should not become a compliance exercise. 

Figure 3: Scaling adoption of the framework 

The next step is ecosystem-wide adoption. Investors, FSPs, asset managers, and data platforms should converge around a common reporting approach. Institutions should adopt the framework in phases. They should first integrate indicators into existing MIS and later expand into survey-based outcome measurement. They should also establish baseline values for social, climate, and service-level indicators to support meaningful impact assessment over time. Regular evaluations and framework reviews can strengthen comparability, improve investment decisions, and mobilize more private capital for the WASH sector. The framework is ready. The sector now has an opportunity to adopt it collectively and put it into practice. 

For the sanitation entrepreneur who seeks capital to serve more households, better impact measurement may seem far removed from day-to-day operations. Yet, it can determine whether investors have the confidence to commit capital in the first place. A common measurement language can help the sector show results more credibly, compare investments more consistently, and mobilize private capital at a scale that matches the challenge. Ultimately, unlocking capital for WASH is not only a financing challenge. It is an evidence challenge. The framework offers a path to address both. 

Priority sector lending in Kenya: A practical pathway to inclusive and productivity-led growth

Posted on by harshita

Introduction

Kenya’s financial system is among the most advanced in Africa. It is supported by a strong digital infrastructure, widespread mobile money usage, and a dynamic banking sector. Yet, despite this progress, credit allocation remains uneven, especially for agriculture, micro, small, and medium enterprises (MSMEs), women- and youth-led enterprises, green sectors, and early-stage innovators.

Agriculture contributes to around a fifth of Kenya’s gross domestic product (GDP). The sector employs more than 40% of the total population and approximately 60% of the rural population. However, credit from commercial banks to the sector remains disproportionately low. Credit issues also plague MSMEs, which contribute to 40% of GDP and form the backbone of Kenya’s economy. They continue to face chronic credit rationing due to limited collateral, limited financial histories, and high perceived credit risk.

At the same time, Kenya’s national development agenda, which includes “big four” priorities, the Bottom-Up Economic Transformation Agenda (BETA), climate-resilient agriculture, and affordable housing, requires structured credit expansion into underserved but high-impact sectors.

The PSL framework for Kenya as a case for priority sector lending

Priority sector lending (PSL) provides a structured approach to addressing systemic credit gaps in the sector. PSL is a policy instrument designed to channel an adequate flow of credit to sectors critical for economic growth. These include agriculture, MSMEs, social infrastructure, affordable housing, and energy projects that conventional banking institutions often overlook. These focus sectors may evolve or change as per the respective central bank’s periodic review, undertaken to align them with the country’s stage and state of economic development. PSL significantly advances equitable growth, financial inclusion, and long-term economic stability.

Key benefits of the PSL framework include:

  • Inflation-safe stimulus: Studies have shown that well-implemented targeted sector credit under PSL promotes supply-side growth and reduces consumption-driven inflation. PSL channels credit to productive, supply-side sectors, such as agriculture, MSMEs, logistics, renewable energy, and affordable housing, which increase output capacity and reduce supply bottlenecks.
  • Liquidity reallocation: A PSL system that offers cash reserve ratio (CRR) rebates for lending to designated priority sectors will help convert locked-up or non-earning reserves to productive credit. This system helps release liquidity into the system. Historical data of select countries states that total credit to productive sectors increased proportionally with an increase in the credit target. This step could help increase the flow of credit in the system without necessarily expanding the money supply.
  • Growth despite restrictive policy: With PSL, financial institutions (FIs) and banks would have clear instructions for lending to agriculture, MSMEs, and other economically weaker sectors. FIs can more confidently lend to sectors that may otherwise seem too risky through risk-sharing mechanisms, such as the Credit Guarantee Scheme (CGS).

Based on global experience across countries, including India, Indonesia, Brazil, and Tanzania, the PSL framework has proven effective. Kenya can adapt this model that guides banks to systematically direct a share of their lending toward strategically essential sectors. These international examples show that when PSL is well-designed, supported by digital infrastructure, credit guarantees, risk-sharing mechanisms, and flexible compliance pathways, it can expand credit access without destabilizing the financial system.

In Kenya’s case, PSL could channel financing into agriculture value chains, micro and small enterprises, green energy and climate-smart sectors, affordable housing, and businesses owned by youth or women. This approach will stimulate broader economic transformation across the country.

Furthermore, a Kenyan version of the PSL framework can be designed to complement rather than dilute the Central Bank of Kenya’s prudential and Basel-aligned capital framework. While CBK’s prudential guidelines require banks to maintain minimum capital adequacy ratios and adopt risk-based capital management consistent with Basel principles, PSL could assist in allocating credit to sectors deemed nationally important. While banks can continue to assess credit risks, make provisions, and maintain adequate capital buffers in line with Basel norms against their respective PSL exposures, the inclusion of provisions such as lower risk weights could help improve the risk-return profile for PSL. This can ensure that the banking sector’s policy/development objectives are achieved without compromising economic stability.

Research by MSC (MicroSave Consulting) shows that directed credit programs worldwide succeed when they align lending incentives with national development goals. These programs offer risk mitigation for lenders and integrate strong monitoring systems to ensure that credit flows are sustainable and impactful.

Kenya’s PSL-ready ecosystem: Converting existing programs into a coherent framework

Kenya already has several foundational elements of a PSL ecosystem, but these mechanisms are not structured under a single formal PSL framework. In the past decade, the government and the Central Bank of Kenya (CBK) have repeatedly directed credit to underserved sectors through targeted instruments. These instruments include the CGS for MSMEs, the Agriculture Credit Guarantee Scheme, the Women Enterprise Fund, the Youth Enterprise Development Fund, and the Hustler Fund. Meanwhile, the Agricultural Finance Corporation and Kenya Development Corporation implement value chain financing programs. Additionally, Kenya’s Financial Sector Development Plan (FSDP) outlines clear goals to expand inclusive credit to MSMEs, climate-resilient agriculture, low-cost housing, and green sectors. These sectoral priorities align with global PSL programs.

A Kenyan PSL framework would not need to replicate India’s quota-driven model. However, it can pivot toward a more modified PSL approach, as with Indonesia and Tanzania, where banks follow guided targets supported by incentives, guarantees, concessional refinancing, and digital compliance systems, rather than strict mandates. Indonesia’s model shows how credit expansion can be driven through policy incentives, partial credit guarantees, and digital financial infrastructure, which include real-time credit tracking platforms and government-backed guarantee institutions. These institutions include the Indonesian Credit Guarantee Public Company (PT Jamkrindo and PT Persero) and Indonesian Credit Insurance (PT Askrindo).

These institutions allow banks to meet inclusive finance goals without destabilizing the sector. Tanzania’s experience similarly shows how policy-driven and market-based lending mechanisms can expand agricultural and SME credit without rigid quotas, which is supported by wholesale lending through the Tanzania Agricultural Development Bank.

Kenya already operates along these lines. The CGS for MSME reflects Indonesia’s and Brazil’s guarantee-led models by de-risking banks and encouraging lending to MSMEs. The Access to Government Procurement Opportunities (AGPO) program and dedicated women and youth enterprise program create steady borrower pipelines. India’s targeted PSL categories and Indonesia’s UMi and KUR programs achieve this through focus on women, microentrepreneurs, and informal enterprises. Kenya’s own agriculture guarantee and refinancing arrangements reflect Brazil’s structured rural credit system, where concessional facilities, refinance windows, and first-loss guarantees enable directed lending. These similarities indicate that the conceptual building blocks of PSL are already embedded across Kenya’s financial and policy ecosystem.

The formalization of these existing elements under a single, coherent PSL framework would enable Kenya to align bank lending with its high-priority national goals systematically. A Kenyan PSL model could glean lessons from PSL frameworks, such as tiered targets, risk-sharing facilities, co-lending pathways, credit guarantee integration, digital monitoring, and flexible compliance. Based on these lessons, the PSL model could strategically channel finance into agriculture value chains, MSMEs, green and climate-smart sectors, affordable housing, women- and youth-owned enterprises, and the broader digital economy. It shows that directed credit programs succeed when they align incentives with development goals, incorporate credit guarantees, reward high-quality portfolios, and maintain strong monitoring systems to ensure sustainable, impactful credit flow.

Toward a phased and digitally enabled PSL architecture for Kenya

The first step is to unify existing credit programs and guarantees into a coordinated national framework to operationalize PSL in Kenya. Clear sector definitions, eligibility criteria, and reporting obligations support this framework. All successful PSL systems, which include India’s quota-driven model, Indonesia’s incentive-based MSME framework, Brazil’s directed credit system, and Tanzania’s policy-driven approach, rely on centralized, well-defined sectoral guidelines and periodic reviews. Based on this, the CBK could issue a foundational PSL policy note that recognizes Kenya’s ongoing directed-credit programs and outlines sector-based lending expectations. In the initial stage, these expectations can remain indicative rather than mandatory and reflect Indonesia and Tanzania’s gradual implementation pathways that balance flexibility with developmental intent.

Kenya already has foundational strengths, which include digital rails, strong e-KYC capabilities, national ID systems, and mobile-enabled credit scoring. These strengths can reduce the cost and friction of credit extension to priority sectors. The integration of Savings and Credit Cooperative Organizations (SACCOs), microfinance institutions (MFIs), mobile lenders, and commercial banks into a unified, real-time credit information–sharing ecosystem would reflect the digital compliance platforms used in Indonesia and India. The centralized dashboards of these platforms track loan disbursements, borrower history, and PSL performance.

Further, the risk mitigation will be central to a viable Kenyan PSL system. MSC’s study on PSL shows that credit guarantees, concessional refinancing, and structured risk-sharing mechanisms substantially reduce delinquency risks and crowd-in bank lending to underserved sectors. Kenya’s existing CGS reflects this global architecture. The framework can be strengthened through wider agricultural coverage and a shift from individual loan guarantees to portfolio-based guarantees similar to Indonesia’s Jamkrindo and Askrindo models. These models introduce differentiated guarantee coverage for women, youth, climate-linked enterprises, and underserved regions.

Additionally, to combine climate or weather insurance with agricultural loans would align Kenya with Brazil’s ABC+ sustainable agriculture financing, where integrated risk-mitigation instruments stabilize loan portfolios. Kenya’s strong value chains in tea, coffee, dairy, horticulture, and fisheries can also adopt upstream and downstream financing channels. This reflects the diversified lending models followed across the globe, where banks fund value-chain actors through cooperatives, processors, MFIs, and digital marketplaces.

A staged rollout will be the most suitable path for Kenya. During the first two years, PSL can serve as a soft-guidance framework that consolidates existing programs, harmonises reporting systems, and strengthens guarantee facilities similar to Tanzania’s gradualist approach and Indonesia’s phased MSME expansions. As the ecosystem matures, Kenya can transition to a more structured regime with formal targets, supported by a market for tradable market instruments, such as priority sector lending certificates (PSLCs), based on India’s successful PSLC system, which incentivizes over-performance and enables market-driven compliance.

In the final phase, the PSL framework can be broadened to encompass green and climate-resilient finance, innovation-led enterprises, digital economy firms, and affordable housing value chains. This approach is consistent with global practices, such as Brazil’s green taxonomy, Indonesia’s sustainable MSME finance, India’s evolving sectoral definitions, and Kenya’s Vision 2030 and FSDP priorities.

A phased implementation ensures credibility, stability, and alignment with Kenya’s institutional realities. Kenya can develop a PSL system that starts with flexible guidance, evolves into formal targets, and ultimately uses digital monitoring, guarantee-backed risk mitigation, and tradable compliance instruments. This approach channels structured, sustainable, and monitored credit flows into high-impact sectors while preserving financial sector stability.

A woman’s name on the property title is only the beginning

Posted on by harshita

When a woman’s name appears on a property title, what does it really signify? Does it reflect genuine ownership, control over assets, and financial agency, or does it simply indicate formal inclusion? India has made significant progress in expanding women’s participation in the workforce, property ownership, and formal housing finance through legal reforms, public policy, and lender initiatives. Yet, formal inclusion does not automatically translate into economic agency. The challenge now is to ensure that inclusion translates into meaningful participation as earners, borrowers, asset owners, and financial decision-makers. Housing lies at the heart of this challenge because, for most households, a home is their largest asset and an important source of long-term financial security. 

A secure home offers far more than shelter. It provides privacy, stability, dignity, and safety, while also supporting work, generating income, and offering protection during periods of financial distress. For decades, women have played a central role in building and sustaining households, yet their contribution has rarely translated into property ownership or access to formal housing finance. Women’s growing contribution to household income, together with supportive policies and lender initiatives, is creating stronger pathways to home ownership and housing finance. 

In 2023-24, the labor force participation rate among women aged 15 and above rose to 41.7%, up from 23.3% in 2017-18, while the share of women in employment increased from 22% to 40.3%. The redesigned Periodic Labor Force Survey (PLFS) for 2025, the first survey under the new format also points to continued momentum, with rural women’s participation rising further up to 45.9%. Yet, higher workforce participation does not necessarily mean women have stable or well-paying jobs, as much of their employment remains informal, self-employed, or linked to family enterprises. These trends suggest that, despite rising labor force participation, important gaps in the quality of employment and meaningful economic participation remain. At the same time, they show that women’s contribution to household income is becoming increasingly visible and harder for financial institutions to overlook. This growing economic visibility has also coincided with an increase in women’s recorded ownership of housing, although much of this ownership remains joint rather than independent. 

UNFPA’s analysis of the National Family Health Survey (NFHS)-4 and NFHS-5 data shows that the share of women aged 15-49 who owned a house, independently or jointly, increased to 42.3% in 2020-21 from about 37.1% in 2015-16.  

This progress has been supported by legal reforms, government housing programs, state-level incentives, and initiatives by financial institutions. Together, these measures have created stronger pathways for women to own property and access housing finance. At the national level, the Hindu Succession (Amendment) Act, 2005, gave daughters in Hindu joint families the same coparcenary rights as sons. Government housing programs then created direct pathways to ownership. Under the Pradhan Mantri Awas Yojana–Urban 2.0, houses receiving central assistance are generally required to be registered in the name of the female head of the household or jointly in the names of both spouses, subject to specified exceptions. About 8.9 million PMAY-U houses stood in women’s sole or joint names by August 2024. By January 2026, the government reported that it had sanctioned 9.0 million houses to women. Under Pradhan Mantri Awas Yojana Gramin (PMAY-G), around 19.5 million of the 26.8 million houses completed by December 2024 were registered solely in a woman’s name or jointly in the names of both spouses. This represents about 73% of completed houses under the program. The program now aspires to achieve 100% women’s ownership. 

State governments have reinforced these measures by reducing the upfront cost of property registration for women. In Delhi, stamp and transfer duty stands at 4% for women purchasers, compared with 6% for men. Financial institutions also introduced incentives, such as lower interest rates for women owners or co-owners and higher loan eligibility when they included a woman’s income in household assessments. In 2023, the International Finance Corporation (IFC) committed up to USD 100 million to IIFL Home Finance, with half the funding earmarked for women’s housing finance. Women-focused portfolios have become more than an inclusion objective. They are also emerging as an important funding and business strategy for financial institutions. These policies and market interventions are increasingly reflected in women’s participation in housing finance and their performance as borrowers. 

Over the five years through December 2025, the number of women borrowers registered a compound annual growth rate (CAGR) of 14.2%, compared with 8.2% for men. Women represented 32.2% of outstanding housing-loan portfolios, and their repayment performance was also marginally stronger, with 2.2% of women’s home-loan balances being overdue by 31 to 180 days, compared with 2.5% for men. Even after accounting for the smaller base, the data indicate that women are emerging as a comparatively resilient borrower segment, with repayment performance that is marginally better than that of men. 

MSC’s (MicroSave Consulting) recent research on affordable housing across selected geographies revealed strong demand for self-construction, renovation, reconstruction, and incremental expansion. Women accounted for more than two-fifths of the study participants. They expressed a desire to build on existing plots, add rooms or floors, repair aging structures, and move out of rented or inadequate homes. Although women did not always interact directly with lenders, their preferences shaped key household decisions. Affordability, privacy, sanitation, ventilation, children’s space, and household safety consistently influenced those decisions. Women often served as co-decision-makers during housing decisions. They also shaped what households considered affordable equated monthly installments (EMIs) and acceptable financial risk. Lenders should therefore assess women’s housing demand beyond the number of primary female applicants. Women often influence the purpose, affordability, and repayment of housing loans even when a man submits the application. 

As India has made strong progress in recording women as owners and borrowers. The next challenge is to give that formal visibility economic meaning. Affordable housing finance can translate women’s growing presence in property and credit records into meaningful economic empowerment. Property ownership can strengthen women’s financial security, resilience, and influence over household decisions. Lenders must now move beyond traditional products for women and adopt women-centric approaches that offer a broader range of services. A woman’s presence on a loan document should reflect her role in the decision rather than satisfy a procedural requirement. Lenders must recognize informal and home-based income more systematically, involve women directly in loan counseling, and ensure they understand repayment obligations, fees, insurance, and the risks associated with mortgaging property. The first phase of inclusion brought women onto property titles and loan documents. The next phase must recognize them as income earners, informed borrowers, and decision-makers. It must also give them a meaningful voice in how their assets are financed, used, and managed. 

Everyone sees financial fraud. It is time for institutions to stop it.

Posted on by harshita

A schoolteacher in Madhya Pradesh received a call from someone who sounded exactly like her cousin. The caller claimed to be in trouble and needed money transferred at once. The voice sounded authentic, and the teacher transferred the funds immediately.

The call was a fraud. The relative’s voice had been cloned using AI. No suspicious links, stolen passwords, or warning signs were involved. The teacher did nothing wrong. Yet, the transaction need not have proceeded unexamined. Her bank could see that it sharply deviated from her usual pattern. It was an unusually large, urgent payment to a new payee.

Meanwhile, the receiving bank held information about the recipient’s account history, including whether it resembled the rapid-in, rapid-out behaviour typical of a mule account. No single institution had the complete picture, yet together, they held sufficient signals to justify a warning, additional verification, or temporary review. Instead, the transfer went through without intervention.

Every stage of the fraud was visible to a different institution. A telecom network carried the call, and the bank processed the payment. The receiving account existed within the financial system. Each institution could see part of the risk, but no one was tasked to connect those signals or act on them.

Fraud networks have industrialized deception using AI impersonation, spoofed identities, mule accounts, and sophisticated social engineering. They exploit the gaps between institutions far more effectively than institutions cooperate with one another.

Banks remain the closest line of defence because they sit at the point of transaction. Yet the scale of exposure is significant. RBI data shows that digital payment fraud cases involving cards and internet banking run into the tens of thousands each year. MSC’s research across India, Bangladesh, and Kenya shows that 55% of low- and moderate-income respondents receive fake calls or messages that impersonate legitimate institutions. A growing share of these losses stems from authorized push payment (APP) fraud, where victims themselves authorize transfers under coercion or deception. These cases often bypass conventional fraud controls designed to detect unauthorized activity.

The warning signs, however, are often visible before victims lose their savings. Earlier this year, in Visakhapatnam, a retired nurse arrived at her bank to transfer INR 50 lakh after fraudsters who posed as cybercrime officials threatened her with arrest. She arrived at her bank to initiate the payment. There, the bank manager recognized the warning signs, alerted authorities, and stopped the payment. This intervention required neither breakthrough technology nor perfect fraud detection. It required trained people who could recognize suspicious patterns and act.

The contrast with the Madhya Pradesh teacher is striking. In both cases, victims were manipulated into authorizing a payment. In one case, the transaction proceeded without intervention. In the other, human intervention prevented a loss.

In the teacher’s case, the fraud began long before the payment was made. The cloned voice reached her through a telecom network. Caller ID spoofing and SIM-swap fraud increasingly allow criminals to impersonate banks, regulators, and law enforcement agencies. Telecom providers can often detect suspicious patterns before payments occur. India has already shown that such intervention is possible. Bharti Airtel, for example, uses AI systems to flag suspected spam calls and malicious links in real time.

Digital platforms have become another major channel for fraud distribution. Fake investment schemes, impersonation accounts, and fraudulent advertisements can reach thousands of potential victims before removal. Fraud today moves seamlessly across telecom networks, banking systems, and digital platforms, but accountability remains siloed. India, therefore, faces less of a detection problem than an accountability problem. The response requires action on three fronts.

The first step is a mandatory reimbursement framework for APP fraud backed by enforceable fraud-risk controls. In October 2024, the UK’s Payment Systems Regulator introduced rules requiring sending and receiving banks to share liability for qualifying APP fraud losses, capped at GBP 85,000 (approximately INR 90 lakh) per claim. When institutions bear part of the financial consequences, prevention becomes a business priority rather than a compliance exercise.

Second, accountability must extend beyond banks. Australia’s Scams Prevention Framework requires banks, telecommunications providers, and digital platforms to implement anti-scam controls or face financial penalties of up to AUD 50 million (INR 344 crore). Fraud prevention cannot stop at institutional boundaries because fraud crosses them.

Third, fraud detection must become collaborative. India’s MuleHunter.AI initiative demonstrates that mule account detection at the network level is technically feasible. Patterns visible to one institution can become actionable across the system before funds disappear.

No fraud-detection system can fully distinguish coercion from legitimate urgency in real time, especially in a country processing billions of low-friction digital transactions. However, that cannot justify a system where institutions face little consequence for failing to act on visible warning signs.

The Madhya Pradesh teacher could not have known the voice on the call was fake. The institutions around her knew more than she did. The question is why none of them were required to act on it.

This was first published in “Mid Day” on 3rd August 2026.

Affordable housing finance: Addressing the last-mile challenge

Posted on by harshita

India’s affordable housing challenge is often measured by the number of homes built or sanctioned. While these metrics matter, they reveal only part of the story. The more fundamental question is whether households can access formal finance to build, purchase, or improve their homes. For millions of low- and moderate-income (LMI) families, the challenge extends beyond housing availability. They also need affordable, adequate, and timely housing finance. 

Financial institutions generally consider housing loans among the safest lending products because tangible collateral backs them, and delinquency rates have remained relatively low. Affordable housing finance (AHF), however, presents a far more complex picture than conventional housing loans. Lenders must adopt a different approach for economically weaker sections (EWS) and lower-income group (LIG) households. Success depends not only on the underlying asset but also on a clear understanding of the borrower. Lenders must assess household income, seasonal income fluctuations, employment patterns, housing aspirations, social and economic constraints, financial behavior, and repayment capacity. These factors carry as much weight as product pricing and collateral when lenders design sustainable and affordable housing finance solutions. 

The need to expand access to AHF is especially important because India’s housing challenge remains significant. India’s Supreme Court recognizes access to safe and adequate housing as part of the fundamental right to life under Article 21 of the Constitution. The country continues to face a substantial urban housing deficit. Current projections estimate a shortage of 31.2 million units by 2030. Other methodologies estimate a deficit of 50 to 70 million units, as nearly 95% of this shortage affects EWS and LIG households.  

These families face housing constraints due to affordability issues and structural barriers that limit access to the formal financial system. Many earn irregular incomes through self-employment, casual labor, or informal occupations. Their incomes fluctuate across seasons, which makes conventional income assessment difficult. Many also lack clear property records, land titles, or income documentation. Limited or non-existent credit histories further reduce access to formal housing finance, even when households are both willing and able to repay. As a result, many households with genuine housing needs remain outside the reach of institutional finance. 

The evolution of India’s housing finance ecosystem partly explains this financing gap. Historically, India’s housing approach remained supply-driven and state-centric. After Independence, public agencies led housing construction, while housing finance and private sector participation received limited attention. During the liberalization era of the 1990s, housing finance companies emerged as specialized lenders and expanded housing credit. However, they primarily served middle- and upper-income households, namely prime and near-prime borrowers. These borrowers could easily provide formal income documentation, posed lower credit risk, and offered stronger commercial returns. As a result, many lower-income households continued to rely on informal borrowing, incremental self-construction, or delayed housing investments. 

Over the past decade, public policy has sought to address this imbalance. A major shift occurred in 2015 with the launch of the Pradhan Mantri Awas Yojana (PMAY). The program shifted government strategy from housing construction to a broader housing ecosystem. PMAY introduced demand-side support through the Credit-Linked Subsidy Scheme (CLSS), which enabled beneficiaries to access institutional finance with interest subsidies. The program also repositioned the government from a housing provider to an enabler of housing finance through subsidies and institutional support. 

The results have been significant. As per data from 2026, under PMAY-U and PMAY-U 2.0, 12.7 million houses have been sanctioned, while 12.0 million houses have been grounded for construction, and 9.86 million have been completed and delivered to beneficiaries. Under PMAY-Gramin Phase I and II, states have received allocations for 41.5 million houses, sanctioned 39.0 million, and completed 29.9 million. Regulatory reforms, including revisions to priority sector lending (PSL) norms, have also expanded eligibility for affordable housing loans. Yet, policy support alone cannot address the structural barriers that continue to exclude lower-income households from formal credit. 

Affordable housing has received greater policy attention, while revisions to PSL norms have expanded eligibility to reflect changing property prices across geographies. Together, these measures recognize that better housing outcomes require better access to housing finance. Yet, policy support alone cannot close the financing gap. Greater access to affordable housing finance requires solutions that address structural, behavioral, and documentation barriers, which continue to exclude many lower-income households from formal credit. 

The AHF ecosystem relies on a diverse mix of institutions. Public-sector banks account for nearly 46% of the market, followed by housing finance companies at 29% and private-sector banks at 22%. At the same time, specialized affordable housing finance companies (AHFCs) have emerged as an important segment. This shift reflects the growing demand for lenders that serve customers excluded from mainstream housing finance. 

However, greater market access requires more than conventional lending approaches. Affordable housing finance cannot rely solely on household income or collateral value. Lenders must also understand housing aspirations, affordability thresholds, financial behavior, property ownership patterns, documentation readiness, and the broader housing supply ecosystem. Traditional underwriting models often fail to assess borrowers with irregular, seasonal, or self-employment income. Street vendors, transport operators, artisans, contractors, shopkeepers, and other informal workers often demonstrate stable earning capacity over time. Yet, their cash flows rarely fit salary-based assessment models. Effective, affordable housing finance, therefore, requires lenders to complement collateral-based underwriting with cash flow assessments and a deeper understanding of household financial resilience. This approach supports prudent risk management and expands access for underserved segments. 

Documentation also presents a major challenge. Unclear property ownership records, incomplete 13-year title chain documents, procedural complexity, and lengthy verification processes prevent many creditworthy households from accessing formal housing finance. Greater credit access, therefore, requires better loan products, simpler customer journeys, streamlined documentation where feasible, and operational processes that reduce friction throughout the lending cycle. 

Affordable housing markets are also highly local. Housing demand, construction activity, income levels, employment patterns, housing loan penetration, and repayment capacity vary across districts and states. A market with a large housing shortage may not offer the strongest lending opportunity if household incomes remain unstable or local credit ecosystems lack maturity. In contrast, economically stronger regions may show lower unmet housing demand. Lenders must therefore balance housing need with credit opportunity and economic stability instead of relying on a single indicator. 

The decision to buy, construct, or extend a house is shaped by a complex interplay of household needs, aspirations, family dynamics, and financial capacity. Once this decision is made, households must determine how to finance, as housing finance is usually their largest lifetime investment. At this stage, understanding borrowers becomes equally important. Trust in financial institutions, awareness of formal financial products, confidence in their ability to repay, previous borrowing experiences, and intra-household decision-making all influence whether households seek formal finance, the type of financing they choose, and the terms they are willing to accept. Gender roles, social networks, interactions with intermediaries, and perceptions of lenders also influence borrowing behavior. Treating LMI households as a single customer segment overlooks these vital differences. Evidence-based product design should therefore combine quantitative market analysis with a qualitative understanding of customer behavior. Market size estimates, affordability analysis, demographic profiles, and housing demand projections provide a strong foundation for eligibility assessments. These insights become more valuable when lenders also understand borrower motivations, financial decision-making, documentation challenges, and local market conditions. Together, these perspectives support stronger product design, underwriting frameworks, operational processes, and customer engagement strategies. 

These complexities have reshaped credit assessment across the sector. AHFCs now use AI-powered analytics and alternative data sources to evaluate informal sector borrowers. Lenders assess utility payment records, mobile usage patterns, and social data to evaluate customers without traditional credit histories. This approach expands market reach while improving risk management in underserved segments. Market leaders also adopt hybrid “phygital” distribution models that combine digital capabilities with physical presence. They establish micro-branches in Tier-II and Tier-III cities to deepen market penetration while maintaining personal interactions. Physical presence remains essential for accurate credit assessment and relationship-building, where digital literacy and formal documentation are limited. This model allows lenders to scale without compromising underwriting quality. 

The implications of the affordable housing finance ecosystem extend beyond individual lenders. As India continues to urbanize and housing demand grows, financial institutions must adopt business models that balance commercial sustainability with a deeper understanding of customer realities. Products designed only around institutional processes may fail to serve households whose financial lives fall outside conventional lending models. In contrast, institutions that understand informal income patterns, housing journeys, local markets, and customer behavior are more likely to develop scalable and inclusive products. India’s affordable housing challenge also presents one of the country’s largest opportunities for financial inclusion.India’s affordable housing challenge extends beyond building more homes. It also requires households to access the finance needed to purchase, construct, or improve them. However, affordable housing finance requires more than sanctioning additional loans. It will succeed when formal finance reaches families historically excluded from the financial system and reflects the realities of their lives.  

Lenders must move beyond collateral and compliance and adopt evidence-based product design, customer-centric underwriting, and operational models that reflect the realities of LMI households. Such an approach enables affordable housing finance to bridge the gap between housing need and home ownership while ensuring commercial viability and social meaningfulness. 

Design principles for effective digital financial capability

Posted on by Srijan Paul

MSC’s (MicroSave Consulting) digital financial capability (DFC) framework offers a practical approach to designing financial capability policies, programs, and digital solutions. Its five design principles seek to understand target segments, create innovative content, balance delivery channels, coordinate stakeholders, and embed impact-oriented monitoring and evaluation. This framework shows how governments, regulators, and financial service providers can move beyond awareness campaigns to achieve measurable behavior change, safer digital financial use, and stronger consumer protection.