Evolution of Informal Credit Markets (ICMs):
From Social Collateral to Digital Footprints

Hari Srinivas
Case Study Series E-110.

Editor's Note: This document was first written in the early 1990s with the explosion of interest in microcredit and informal credit markets (ICMs). The original 1990s section is presented below as it is, for historical context, but is updated with a 2020s section below to highlight how financial delivery for low-income households have evolved over the years.

The persistent dichotomy between formal and informal financial systems has long served as a defining characteristic of the economic landscape in developing nations. In the early 1990s, the discourse surrounding informal credit markets (ICM) focused on their role as a vital, albeit high-cost, alternative to a formal banking sector that was structurally incapable of reaching the "unbankable" poor.

As the global economy enters the mid-2020s, this landscape has been dramatically reshaped by a technological paradigm shift that has digitized the very essence of informal intermediation. The fundamental mechanisms of trust, proximity, and information, which once relied exclusively on physical presence and social ties, are now being captured and quantified through mobile money, digital footprints, and artificial intelligence.

This analysis explores the continuity and transformation of informal credit markets in the 1990s and 2020s, , evaluating their performance through the lens of both historical evaluation criteria and contemporary digital metrics.

1990s

The Foundations of the Informal Credit Ecosystem: The 1990s Paradigm

During the final decade of the 20th century, the informal credit market was recognized not as a peripheral or temporary phenomenon, but as a sophisticated and resilient response to the information asymmetries and high transaction costs inherent in developing economies.1

The formal sector, comprised of commercial and state-owned banks, operated under a rigid framework that demanded physical collateral, such as land titles or significant liquid assets, which the majority of low-income households and micro-enterprises simply did not possess.3 Consequently, the informal market - a diverse array of moneylenders, traders, relatives, and community associations - became the primary source of financial liquidity for the poor.1

Characteristics and Internal Logic of Traditional Informal Credit

The internal logic of the 1990s informal credit market was predicated on the maximization of "local information." Lenders utilized their deep integration into the social and economic fabric of their communities to overcome the monitoring difficulties that deterred formal banks.1 This market was characterized by several distinctive operational features that contrasted sharply with the formal sector.

Table 1: Comparative Attributes of Credit Systems in the 1990s Era.3
Feature Informal Credit Market
(1990s)		 Formal Financial Sector
(1990s)
Loan Size Small, tailored to immediate needs Larger, minimum thresholds apply
Duration Short-term (days to months) Long-term (years)
Collateral Social ties, reputation, interlinked contracts Land, property, physical assets
Interest Rates High, reflecting risk and opportunity cost Regulated, relatively low
Processing Speed Immediate (hours to days) Slow (weeks to months)
Repayment Flexible, often tied to income cycles Rigid, monthly installments

BOX 1: Borrower Evaluation Criteria in Informal Credit Markets

Past repayment Record
Prompt repayment of previous loans remains the single most important criteria for a borrower to enhance his or her credit worthiness with the ICM supplier. Frequent changes in the terms and conditions of the loan agreement (for example, a delay in repayment or renegotiation of interest rates) would similarly affect credit worthiness.

Social Status
The social status of the borrower, such as his place of origin, language spoken, and religion (in multi-cultural countries such as India), play an important role. Place of origin is important when we realize that most low-income families in urban areas are first, second or third generation migrants, and original social ties continue to influence social status.

Acquaintance
The length of acquaintance (in terms of number of years) and the type (family or business friend) are defining factors. Timberg (1984) cite the case of a money lender in India who lent only to borrowers who had transacted with his father, or their sons.

Employment Status
As with the formal sector, employment status (income, type and place of work) is used as a criteria to evaluate borrowers. Place of work is tied up with the fact that most families stay in settlements close to the place of work (or vice versa), and "keeping an eye" on the borrower becomes easy for the supplier, and ensures repayment.

Recommendation
A person having a good relationship with an ICM supplier, such as a settlement leader, a shop keeper may make a recommendation on behalf of the borrower for a loan. This is different from a guarantee, in that it does not involve any financial committment on the part of the recommender

Collateral
Most suppliers do not need collateral, as risk aversion is ensured by other means, such as close informational ties. But there are exceptions: some money lenders require collateral for exceptionally large loans; certain people's organization (or self-help groups) provide loans to its members only when they have saved a predetermined amount as "collateral"; pawn brokers demand collateral as an intrinsic part of their operations.

Guarantee
As with formal loans, third party guarantees are also seen in the ICM to ensure repayment, particularly for large loans.

The ability of informal lenders to operate successfully without physical collateral was rooted in the concept of social collateral. Lenders knew their borrowers personally; they understood their business cycles, their family reputations, and their social standing. In many cases, credit was interlinked with other markets.

A trader might provide a loan to a farmer on the condition that the harvest is sold back to that trader at a predetermined price, effectively using the crop as collateral and ensuring a captive market for the trader's business.3

Evaluating the Performance of Informal Credit

The evaluation of informal credit systems in the 1990s followed a tripartite framework centered on Accessibility, Usefulness, and Cost. This framework allowed researchers to understand why low-income households continued to choose informal lenders even when formal options were geographically available.

Dimensions of Accessibility

Accessibility was not merely a question of physical distance but of procedural and social barriers. Formal banks often required complex documentation, literacy, and a "formal" identity that marginalized many potential users. In contrast, informal credit was accessible because it operated within the cultural and linguistic norms of the borrower.
  1. Spatial Proximity: Lenders were often neighbors or local shopkeepers, reducing travel time and costs for the borrower.2
  2. Temporal Speed: Informal loans could be secured in response to emergencies - a health crisis or a sudden business opportunity - whereas formal bank procedures were too slow to be useful for daily survival.7
  3. Procedural Simplicity: The lack of paperwork made informal credit the only option for the non-literate or those without formal legal standing.3

Usefulness and Flexibility

The usefulness of credit was measured by its alignment with the borrower's specific needs. Informal credit was uniquely "fungible," meaning it could be used for both consumption (food, medicine) and production (seeds, inventory).3 Formal banks often restricted loans to "productive" uses, ignoring the reality that for a low-income household, health and nutrition are prerequisite to production. Furthermore, informal repayment schedules were often adjusted to match the volatile cash flows of the informal economy, such as daily market sales or seasonal harvests.

The True Cost of Informal Credit

While informal interest rates were notoriously high - often exceeding 80% per annum - the "total cost" of the loan was often competitive when transaction costs were considered.3 Borrowing from a formal bank involved hidden costs: multiple trips to the city, bribe payments to officials, documentation fees, and the opportunity cost of time spent waiting for approval. For a micro-enterprise, the immediate availability of a high-interest informal loan was often more profitable than a low-interest formal loan that arrived too late to capture a seasonal market.7

Theoretical Modeling of Default and Monitoring

The survival of the informal credit market in the 1990s was theoretically explained through models of asymmetric information and limited liability. If a borrower with zero assets borrows money, they have an incentive to default because there is no physical property for the lender to seize. This can be expressed through a simple repayment condition: a borrower will repay a loan K at interest rate r only if the cost of default cK (where c represents the social or economic cost of "fudging the books" or social ostracism) outweighs the benefit of not paying.3 The condition for repayment is:

Simplifying this reveals that the lender will only lend up to a maximum leverage ratio:

In the informal market, the cost of default c is extremely high due to the social sanctions involved in small communities. If a borrower defaults, they lose their reputation, their access to future credit, and potentially their standing in the community. This "reputational capital" acted as a powerful substitute for traditional collateral, leading to the surprisingly low default rates often observed in informal credit groups like Rotating Savings and Credit Associations (ROSCAs).3

The Taxonomy of Informal Providers in the Pre-Digital Era

The informal credit market was never a monolithic entity but a complex ecosystem of diverse providers, each serving a specific niche within the informal economy.

Non-Professional Lenders: Family and Friends

In most developing countries, the first and most common source of credit was the extended family network and social circles.1 These loans were usually interest-free and motivated by reciprocal social obligations. However, their size and duration were strictly limited by the lender's own liquidity. While these loans were the most "accessible," they were often unreliable during widespread shocks (like a drought) that affected the entire community simultaneously.10

Professional Moneylenders and Pawnshops

Professional moneylenders occupied the most controversial segment of the market. They provided high-speed, unsecured credit to the poorest households who lacked even the social capital to join a ROSCA.1 While often characterized as "usurious," their rates reflected the extremely high risk of their portfolio and the high opportunity cost of their capital. Pawnshops, on the other hand, provided a bridge between the formal and informal, requiring a physical asset (gold, electronics) but offering the speed and procedural ease of an informal lender.3

Commercial Lenders: Traders and Landlords

Traders and landlords utilized credit to secure their primary business interests. In the Philippines during the 1980s and 90s, the rural informal market was dominated by rice traders who provided pre-harvest inputs on credit to ensure a steady supply of grain for their mills.9 These interlinked contracts were a form of risk management; the lender controlled the output of the borrower, significantly reducing the cost of monitoring and enforcement.3

Group-Based Associations: ROSCAs and ASCAs

Group-based savings and credit associations, such as stokvels in South Africa or paluwagan in the Philippines, represented the most structured form of informal finance. These groups relied on positive assortative matching - individuals with similar risk profiles and social backgrounds formed groups to save and lend to one another. The social pressure of the group ensured high repayment rates, often exceeding 97%.3 These organizations served as a vital safety net, allowing members to save for lump-sum expenditures like education, housing, or business equipment.1

2020s

The Digital Transformation of the 2020s: A New Horizon for Informality

As the global economy transitioned into the 2020s, the "traditional" informal credit market began to intersect with a powerful new force: digital financial services (DFS). This era is defined by the "new view" of informality, where the informal and formal sectors are no longer isolated segments but are increasingly integrated through digital platforms.13 The proliferation of mobile phones - with over three billion smartphones in use across low- and middle-income countries - has provided a new infrastructure for financial intermediation that bypasses the brick-and-mortar limitations of the 1990s.15

The Rise of Mobile Money as a Credit Infrastructure

Mobile money has evolved from a simple tool for peer-to-peer (P2P) transfers into a comprehensive platform for savings, credit, and insurance.14 By 2024, 79% of adults globally had a financial account, a surge driven largely by mobile money in regions like Sub-Saharan Africa, where 40% of adults held a mobile money account.15 The significance of mobile money lies in its ability to generate a digital trail of financial behavior. In the 1990s, an informal trader's creditworthiness was known only to their local moneylender or social circle. In the 2020s, every transaction made through a mobile wallet - airtime purchases, utility payments, customer receipts - creates a "digital footprint" that can be analyzed to assess risk.4

Table 2: The Rapid Growth of Digital Financial Inclusion in Sub-Saharan Africa.15
Metric Sub-Saharan Africa (2021) Sub-Saharan Africa (2024)
Mobile Money Account Ownership 27% 40%
Overall Account Ownership 49% 58%
Formal Savings via Mobile Money 11% 23%
Borrowing via Mobile Money (Data Emerging) 32% (Kenya)

Digital Credit Scoring: Replacing Social Collateral with AI

The most transformative aspect of the 2020s is the shift in how creditworthiness is evaluated. Traditional scoring relied on "judgmental" assessment (the 5 CsThe "5 Cs" of credit are Character, Capacity, Capital, Collateral, and Conditions - five key factors lenders use to assess a borrower's creditworthiness and ability to repay a loan. ), which effectively excluded anyone without a formal history.19 The 2020s have introduced "alternative credit scoring," which uses machine learning (ML) and artificial intelligence (AI) to analyze unstructured data.20

This alternative data includes:

  1. Transactional Data: Frequency and size of digital payments, history of airtime top-ups, and regularity of utility bill payments.21
  2. Device Intelligence: Metadata from smartphones, such as app usage patterns, geolocation consistency, and even how a user interacts with their device (e.g., typing speed, charging habits).23
  3. Social and Behavioral Signals: E-commerce transaction history, online reviews, and, in some experimental models, social media analytics to gauge personality and reliability.21

Research has shown that these digital footprints can be as accurate as traditional credit scores. A study using a German dataset found that simple digital footprint variables could predict consumer default with an accuracy (AUC) of 69.6%, outperforming the traditional credit bureau score of 68.3%.18 For "credit invisible" groups - such as gig workers, migrants, and young adults - this shift represents the difference between total exclusion and access to capital.22

The Role of Fintechs and Digital Banks

New players, such as JUMO in Africa or GCash and Maya in the Philippines, have stepped into the role once held by professional moneylenders.14 These fintechs operate as "orchestrators," providing the technology and credit scoring for traditional banks and mobile network operators to offer "nano-loans" to their customers.

In the Philippines, digital banks such as the Maya Bank have seen their assets grow to over PHP 49.9 billion by early 2025.26 These platforms offer a "one-stop shop" for low-income users, integrating payments, savings, and micro-loans into a single app. This represents a significant reduction in the transaction costs that plagued the 1990s system; a borrower in a rural village can now secure a loan in minutes without ever leaving their home, effectively solving the "spatial accessibility" problem of the previous era.17

Regional Perspectives: The Philippines and the Evolution of Digital Credit

The Philippines provides a compelling case study of how the 1990s informal credit framework has been updated for the digital age. In the 1980s and 90s, the rural Philippine market was defined by interlinked contracts between rice traders and farmers.9

By 2025, the focus has shifted to urban centers like Metro Manila, where digital lending and "Buy Now, Pay Later" (BNPL) services are becoming the primary sources of credit for the informal sector.26

The Current State of Financial Inclusion in the Philippines

The 2025 Consumer Finance and Inclusion Survey (CFIS) reveals a complex picture of financial life in the Philippines. While smartphone ownership and connectivity have expanded significantly into rural and low-income communities, account ownership actually saw a slight decline from 56% in 2021 to 50% in 2025. This is attributed to a decrease in loan-anchored accounts at microfinance NGOs and cooperatives, as borrowers have shifted toward more flexible, non-anchored digital platforms.12

Table 3: Shifting Financial Inclusion Patterns in the Philippines.12
Indicator Philippines (2021) Philippines (2025)
Formal Account Ownership 56% 50%
E-money Account Usage 36% 36% (Steady)
Outstanding Loan Incidence (Higher) 25%
Smartphone Ownership (Growing) High in urban, expanding in rural
Digital Banking Market Size - USD 540.6 Million (2024)

Despite the rise of digital banks, traditional informal borrowing remains prevalent. Filipinos still exhibit an "aversion to borrowing," with 70% of adults perceiving loans as a bad idea.12 When they do borrow, they prioritize "fast loan processing and approval," which has fueled the success of online lending platforms.12 However, this speed comes with risks; high default rates among digital borrowers - often due to poor credit management and high interest rates - continue to challenge the market's stability.27

Case Study: Maya Bank and the Hybrid Model

Maya Bank has emerged as a leader in the Philippine digital banking space by successfully merging the "social trust" of traditional banking with the "digital convenience" of fintech. By March 2025, Maya held PHPThe Philippine Peso 49.9 billion in assets and served millions of users.26

Their model uses transactional data from their e-wallet (GCash's primary competitor) to offer instant credit to micro-retailers and individuals. This effectively replicates the "local information" advantage of 1990s moneylenders but at a national scale using AI-driven scoring.26

Thailand: Navigating Household Debt and the Open Finance Future

In Thailand, the informal credit market continues to play a critical role, particularly as the formal sector struggles with a protracted recovery from the pandemic and structural challenges like demographic aging. Household debt in Thailand remained elevated at 86.8% of GDP in late 2025, a situation that has constrained formal credit growth to a mere 0.4%.30

The Push for Virtual Banking and "Your Data"

To address these constraints, the Bank of Thailand (BoT) has pivoted toward digital innovation. In October 2024, the "Your Data" initiative was launched to create an open finance ecosystem, allowing consumers and SMEs to use their non-banking data to secure credit from various providers.33 This move is intended to increase competition and drive down the cost of credit for those currently forced into the high-interest informal sector.

Thailand's strategy also includes the issuance of a tranche of virtual banking licenses, with the first winners expected to start operating by mid-2026. These digital-first banks are expected to use gamification and seamless user experiences to reach the unbanked and underbanked, who currently account for a significant portion of the population despite Thailand's relatively high level of urbanization.33

Table 4: Macroeconomic Context of Credit in Thailand.30
Economic Variable Thailand (2024/2025) Impact on Credit
GDP Growth (2025) 2.1% (Projected) Weakens demand for productive credit
Household Debt 86.8% of GDP Limits capacity for new formal borrowing
Policy Rate 1.50% (August 2025) Accommodative stance to spur growth
Informal Sector Size High (Varies by region) Acts as a shock absorber for the poor

The Persistent Relevance of Traditional Informal Providers

Even with the rapid rise of digital credit, traditional informal providers have not disappeared. Instead, they have adapted, often coexisting with or even benefiting from digital tools.

The Durability of Social Networks

In Sub-Saharan Africa and Southeast Asia, family and friends remain the most significant source of credit for low-income households.11 Digital platforms like WhatsApp and mobile money have simply made this informal borrowing faster and more efficient. A migrant worker in Bangkok can now send a loan home to their village in seconds via a mobile wallet, a transaction that once took days through informal couriers.10

The Resilience of ROSCAs in the Digital Age

Community-based savings groups like South Africa's stokvels continue to thrive because they provide something that a digital bank cannot: social support and collective identity.6 In many cases, these groups have digitized their operations, using mobile apps to track contributions and manage payouts, but the underlying mechanism of social collateral remains unchanged. In South Africa, these groups are increasingly "semi-formal," maintaining bank accounts for their collective funds while operating according to traditional informal norms.

The Dark Side of the Digital Shift: Risks and Vulnerabilities

The transition from the 1990s ICMs to the 2020s digital markets has introduced new risks for low-income users.
  1. Over-Indebtedness: The "instant" nature of digital credit makes it easy for borrowers to take out multiple small loans from different apps, leading to a debt spiral.14
  2. Algorithmic Bias: ML models can inadvertently penalize marginalized groups. For instance, women or rural dwellers may have smaller digital footprints, leading them to be scored as "riskier" simply due to a lack of data.20
  3. Privacy and Security: The collection of sensitive metadata - including geolocation and app usage - raises significant concerns about data privacy and the potential for predatory lending practices based on behavioral vulnerabilities.20
  4. The Digital Divide: While digital finance reaches many, the most vulnerable - those without smartphones, the non-literate, or those in remote areas - are often left further behind as traditional informal systems are squeezed by digital competitors.38

Synthesis: Evaluating the 2020s through the 1990s Lens

By applying the 1990s evaluation framework - Accessibility, Usefulness, and Cost - to the digital credit markets of the 2020s, we can see how much has changed and what remains constant.

Re-Evaluating Accessibility

In the 2020s, "accessibility" has been redefined. Physical distance is no longer the primary barrier; instead, digital access (smartphone ownership, data costs) and digital literacy are the new frontiers.38 While 84% of adults in low- and middle-income countries own a mobile phone, a significant portion still lacks the skills to use digital financial services safely.16 Procedural simplicity has reached an extreme, with loans approved in seconds, but this has also removed the "cooling-off" period that once allowed for more deliberate financial decisions.26

Re-Evaluating Usefulness

Digital credit is highly useful for consumption smoothing (i.e. balancing spending and saving to maintain a stable, comfortable standard of living) and small-scale trade, mirroring the flexibility of the 1990s informal market.17 However, the "one-size-fits-all" algorithms used by many fintechs can be less flexible than a local moneylender who might allow a repayment holiday during a family funeral. The rise of buy-now-pay-later has increased the usefulness of credit for retail consumption, but it may not be as effective for long-term productive investment as the interlinked trader-farmer contracts of the past.26

Re-Evaluating Cost

The "total cost" of digital credit remains a subject of intense debate. While the interest rates are often lower than traditional "street" moneylenders, they are significantly higher than formal bank rates. When transaction fees, data costs, and the risk of penalties for late payment are factored in, digital credit can be surprisingly expensive.36 However, for the millions who previously had zero access to credit, the "opportunity cost" of not having capital remains the most significant expense of all.

Implications for Future Financial Inclusion

The structural evolution of informal credit markets demonstrates that the "informal" and "formal" are not binary opposites but exist on a spectrum of intermediation. The 2020s have moved the needle toward a digitized hybridity that promises greater inclusion but also presents new systemic risks.

The Role of Regulatory Sandboxes and Open Finance

To manage these risks, governments are increasingly adopting "regulatory sandboxes," allowing fintechs to test new products in a controlled environment.37 The development of "Open Finance" frameworks, as seen in Thailand and the Philippines, is crucial for ensuring that the digital footprints generated by the poor can be used as a portable asset to secure better financial terms.12

The Enduring Power of Proximity

Ultimately, the 2020s have shown that while technology can bridge distance, it cannot entirely replace trust. The most successful financial inclusion strategies are those that blend digital efficiency with the "human touch" of traditional informal systems.

Whether it is a mobile money agent in a rural village or a digital bank that integrates with local community groups, the future of finance for the poor lies in systems that are as accessible, flexible, and socially integrated as the informal credit markets of the 1990s were for their time.

Conclusion and Strategic Outlook

The transition from social collateral to digital footprints represents one of the most significant shifts in the history of development finance. In the 1990s, the informal credit market was a necessary response to the physical and informational isolation of the poor. In the 2020s, it has become a data-driven ecosystem that offers unprecedented scale and speed. However, the core requirements of low-income borrowers - credit that is fast, flexible, and fairly priced - remain unchanged.

As we move further into the 2020s, the challenge for policymakers and innovators is to ensure that the "digital" informal market does not lose the "human" attributes that made its predecessor so essential to the survival of the world's most vulnerable populations. The integration of alternative data must be balanced with robust consumer protection and a focus on building financial capability, ensuring that digital footprints lead not just to more credit, but to greater financial resilience and shared prosperity.

Acronyms used in the document:

  • AI (Artificial Intelligence): Technologies used to automate complex tasks and analyze vast datasets for patterns, such as predicting credit risk.
  • ASCAs (Accumulating Savings and Credit Associations): Informal groups where members save regularly and the funds are lent out to members with interest, which is then shared among the group.
  • AUC (Area Under the Curve): A statistical metric used to measure the predictive accuracy of a credit scoring model.
  • BNPL (Buy Now, Pay Later): A type of short-term financing that allows consumers to make purchases and pay for them over time, often through digital apps.
  • BoT (Bank of Thailand): The central bank of Thailand.
  • CFIS (Consumer Finance and Inclusion Survey): A comprehensive survey conducted to measure financial inclusion patterns, such as the 2025 survey in the Philippines.
  • DFS (Digital Financial Services): Financial services accessed and delivered through digital channels, such as mobile phones or the internet.
  • GDP (Gross Domestic Product): The total value of goods produced and services provided in a country during one year.
  • ICM (Informal Credit Markets): Financial exchanges and lending that occur outside of formal, regulated banking systems.
  • ML (Machine Learning): A branch of AI focused on building systems that learn from data to improve their performance over time without being explicitly programmed.
  • P2P (Peer-to-Peer): Direct transactions or lending between individuals without a traditional financial intermediary.
  • PHP (Philippine Peso): The official currency of the Philippines.
  • ROSCAs (Rotating Savings and Credit Associations): Informal groups where members contribute a fixed amount at regular intervals, and the total pool is given to one member at a time in a rotating sequence.
  • SMEs (Small and Medium Enterprises): Businesses whose personnel numbers and revenues fall below certain limits; they often face the most significant barriers to formal credit.
  • USD (United States Dollar): The official currency of the United States, often used as a benchmark for international finance.

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