Measuring Financial Health: A Framework

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Executive Summary

Financial health is emerging as a new outcome lens that can enrich and complement financial policy and oversight. In line with the G20/Global Partnership for Financial Inclusion (GPFI) working definition, financial health refers to a state in which "people are able to smoothly manage their financial needs and obligations, cope with negative shocks, pursue aspirations and opportunities, and feel satisfied and confident about their financial lives, taking into account country circumstances (GPFI 2024). Financial sector authorities (FSAs) are primarily accountable for prudential soundness and market conduct, and in many jurisdictions, they increasingly support broader public policy objectives such as consumer protection, financial inclusion, innovation, or market development. Financial health can be understood in this context as an outcome-oriented concept that helps connect financial activity and oversight with peoples' lived experience. Financial health is not a new mandate for FSAs—rather, it offers a broader view of whether the financial sector, and its policies, are contributing to resilience and better outcomes for individuals and households, while helping to detect emerging vulnerabilities early. In this sense, financial health sits beyond the FSA's direct accountability line, orienting attention toward the broader outcomes that the financial sector should support. A growing number of FSAs are already measuring financial health and integrating it into their policy frameworks.

This paper introduces a measurement framework that gives concrete form to the G20/GPFI definition of financial well-being through six measurement constructs and 17 indicators. While designed primarily for FSAs, the framework is intended for use by any actor with an interest in understanding financial health outcomes, including researchers, funders, and financial service providers (FSPs). The constructs span the full scope of what the G20/GPFI definition implies for individuals and households. Together they give financial sector actors a shared vocabulary and a practical toolkit for incorporating financial health considerations into their respective analytical and operational work. The framework is grounded in a comprehensive review of 70 financial health or financial well-being measurement initiatives, drawing on what has already been tested in practice. It is designed around three principles: multidimensionality, use of complementary data sources, and adaptability to a range of country contexts, recognizing that measurement can broaden and deepen over time. A streamlined version of the framework was piloted with the National Bank of Rwanda (NBR) as an early proof of concept.

Financial health itself is shaped by many factors beyond the financial sector's direct control, including employment and income levels, cost of living, and broader macroeconomic conditions. These factors are sensitive to changes in underlying financial strain or hardship in a way that traditional economic indicators may not be. Data collected by FSAs are therefore well placed to contribute to broader public policy debates and equip other stakeholders, both within and outside the financial sector, to make strategic decisions. Adopting the measurement framework does not mean that FSAs become responsible for broader (or universal) financial health outcomes across society. A household's ability to generate sustained positive net cash flow is a foundation of financial health, but FSAs cannot raise incomes or make basic needs more affordable through market conduct oversight and enforcement, or by financial inclusion policy. Still, FSAs can influence financial institutions' behavior and value proposition, ensuring safe, suitable, and fair treatment to customers. Financial services, in turn, offer individuals and households strategies for bridging mismatches in the timing of inflows and expenses, protection from potentially shocks through risk-pooling mechanisms, and building capital through investment products and long-term credit.

Table E1 shows the framework: the four G20/GPFI dimensions, the six constructs, and the 17 indicators.

TABLE E1. The Financial Health Measurement Framework

Note: The symbols after each indicator name denote whether the indicator represents a positive (+) or negative (-) in relationship to the construct.
Source: Authors

FSAs can use multidimensional financial health analysis in three different ways, each connecting to a distinct analytical objective. Used diagnostically, FSAs can identify where vulnerability is concentrated by examining how outcomes vary across constructs; among sociodemographic segments such as gender, income, age, and geography; and over time. Used evaluatively, FSAs can monitor outcomes before and after identifiable interventions and, with appropriate research methods, produce evidence on the impacts of policy on household financial health, including any unintended effects. Used as an early warning tool, FSAs can monitor indicators by institution, product, and borrower segment, to potentially identify emerging consumer harm or financial stress before it appears in complaints data or prudential ratios.

These three uses of financial health analysis may not be feasible for every FSA from day one. Which use case an FSA can pursue, and to what depth, depends on what data it can access: the diagnostic use case can begin with a single well-designed survey; the evaluative and early warning use cases have greater requirements in terms of data granularity, frequency of data collection, and research design. What is feasible depends primarily on two dimensions of country context: the level of formal financial inclusion and the maturity of regulatory administrative data infrastructure. Together, these two dimensions define the four country archetypes in Chapter 5, which recommends practical measurement starting points for FSAs in different contexts.

When applied comprehensively, the framework generates a full assessment of financial health measurement for a given market. Data covering all six constructs produce a picture of household or individual financial health that no single indicator can provide on its own. For example, a household with median income may or may not be managing financially, but if you can observe that the household is simultaneously behind on loan payments, is uninsured against health shocks, has no savings, and is consistently worried and stressed about finances, you know that it is in an unambiguously vulnerable financial position. However, FSAs do not need to model all 17 indicators to get started. Partial implementation already delivers significant value.

There are two simultaneous entry points for FSAs looking to get started. The first is to map existing national and international survey instruments against the framework's constructs and indicators using the indicator profiles provided. The second is to take stock of what administrative regulatory data are already available, using the same profiles, to work through which of the 17 indicators that the existing data can support, where gaps lie, and what investments would close them. The right starting point depends on the data environment and the country context. FSAs do not need to measure all 17 indicators to begin. As experience builds, findings and lessons should be shared with the broader community of practice: the evidence base for what works across different contexts can only be developed collectively.

Introduction

This paper responds to the G20/Global Partnership for Financial Inclusion (GPFI) call by translating the 2024 working definition of financial well-being into a practical measurement framework for financial sector authorities (FSAs). It is grounded in a comprehensive review of 70 existing measurement initiatives and an initial pilot with the National Bank of Rwanda (NBR) that tested the framework's application using granular administrative regulatory data, and it is designed to work across the range of data environments and country contexts in which FSAs operate. Existing initiatives—from the OECD/International Network on Financial Education (INFE) toolkit and the Global Findex to country-level surveys in Kenya, Rwanda, and Brazil—have significantly advanced the field. What they were not designed primarily to do is serve as an operational reference for FSAs: structured around policy use cases, calibrated to LMIC data environments, and drawing explicitly on administrative regulatory data alongside surveys. This paper aims to contribute to that.

Purpose, Audience, and Scope

This paper has two primary audiences within FSAs. The first is senior leadership—governors, deputy governors, and heads of regulation and supervision—who need a concise case for why financial health measurement is relevant to their institution's mandate and what it enables analytically. The second consists of the technical teams responsible for financial inclusion analysis; consumer protection monitoring; credit and debt analysis; and research, statistics and data functions—these teams will need practical guidance on how to implement the framework. Chapters 1 through 4 are aimed at both audiences and particularly tailored to the needs of senior leaders; Chapter 5 and Annex 3 are aimed primarily at the second. Beyond FSAs, the framework can be leveraged by researchers, funders, financial sector development programs, technical assistance providers, and financial institutions seeking to measure financial health outcomes in a structured and well-informed way. Besides FSAs, the framework can be leveraged by researchers, funders, financial sector development programs, technical assistance providers, and financial institutions seeking to measure financial health outcomes in a structured and well-informed way. Box 1 sets out the G20/GPFI working definition of financial well-being on which the framework is grounded.

How to Use This Paper

This paper is organized in two parts. Chapters 1–4 address the what and why of financial health measurement and are addressed to policymakers, regulatory bodies, and supervisory teams within FSAs. Chapter 1 makes the case for why financial health measurement matters for FSAs and situates this framework among existing initiatives. Chapter 2 sets out the three guiding principles that shaped the framework's design. Chapter 3 presents the framework of six constructs and 17 indicators. Chapter 4 illustrates the diagnostic, evaluative, and early warning use cases of measurement.

Chapter 5, together with Annex 3, is the operational part of the paper: it is designed for data, research, and technical teams that are ready to define a strategy for measuring financial health using the framework as a guide. Chapter 5 helps teams identify their starting point by country archetype; assess which data platforms and use cases are within reach; and choose how to communicate findings, including whether a composite index is appropriate and, if so, methodological considerations that should be taken into account. The Conclusion reflects the practical implications, future applications, and continued evolution of the framework.

Annex 1 presents the evidence base underpinning the framework's design. Annex 2 documents lessons from early piloting with the National Bank of Rwanda (NBR) that tested the framework's application using granular administrative regulatory data. Annex 3 provides the 17 indicator profiles that form the core operational reference for teams implementing the framework; it is designed to be used alongside Chapter 5.

Chapter 1. The Case for Financial Health Measurement

Financial sector authorities have long relied on institutional indicators such as capital adequacy and leverage ratios, liquidity buffers, and non-performing loan ratios to assess the soundness and resilience of the financial system. Over time, their mandates have expanded considerably: market conduct and consumer protection, financial inclusion, or market development have gained formal recognition as core supervisory objectives alongside prudential stability. The Inclusion + Stability, Integrity, and Protection (I-SIP) framework helped FSAs consider financial inclusion, stability, integrity, and consumer protection objectives in a more integrated way (CGAP 2012). The measurement frameworks that accompanied this mandate expansion have developed largely in parallel—financial inclusion tracked through access and usage indicators, consumer protection through complaints and redress data, and household credit through prudential ratios—but without a shared, outcome-oriented view of how individuals and households are faring across all of these dimensions.

Financial health measurement offers a complementary perspective that responds to this evolution in mandates by viewing the financial system through the lens of individuals and households. It helps answer a fundamental question closely linked to multiple FSA mandates: is the financial sector delivering value for the people it serves? This question has two layers. The first is diagnostic: financial health measurement gives FSAs outcome-level data on how a population is faring financially and how outcomes vary across population segments. The second layer is evaluative: does the financial sector actually drive those outcomes, and by how much? Answering that requires linking measurement data to specific policy interventions over time through impact assessment. The framework does not itself resolve the attribution question, but it creates the data conditions under which that evaluation becomes feasible.

Financial health is also a key dimension of well-being that merits visibility through measurement. Research across low- and high-income settings shows that household finances are far more volatile than annual income figures suggest. Financial diaries studies in Bangladesh, India, Kenya, and the United States found that households routinely manage money week to week through irregular income, recurring expense shocks, and the active juggling of multiple financial tools, formal and informal. Vulnerable segments combining financial services meet what Rutherford (2000) described as the three core money-management needs of the poor: smoothing cash flow, coping with risk, and assembling usefully large lump sums (Collins et al. 2009; Zollmann 2014; Morduch and Schneider 2017). In the US context, researchers have documented how income can swing sharply from one month to the next, and when an unexpected expense hits such as from a medical bill, a repair, a loss of work, many households lack the liquid buffers needed to absorb it without borrowing or cutting back on essentials (Farrell and Greig 2017). Concrete outcomes—such as whether bills are paid on time, whether a shock can be absorbed without hardship, or whether adults can build long-term savings—represent nuances of financial life that annual income or asset snapshots do not capture. These are also outcome areas that can be influenced by financial services, often by design. In addition, research has shown that perceived financial well-being is a key predictor of overall well-being (including after controlling for income), comparable in magnitude to the combined effect of other major life domains such as job satisfaction, physical health, and relationship support (Netemeyer et al. 2018; Ngamaba et al. 2020).

The G20/GPFI theory of change (Figure 1) explicitly links FSA policy interventions through financial sector development to financial well-being outcomes. Through investments in digital public infrastructure, as well as supply-side policies and interventions, FSAs influence how financial products and services are designed and delivered, including the technologies they leverage. FSAs also design policies aimed at protecting, informing, or empowering customers, such as financial education programs or rules for pricing transparency. Together, these actions directly and indirectly shape household financial decision-making, financial behavior and welfare, and ultimately development outcomes.

FIGURE 1. Financial Well-Being: G20 Preliminary Theory of Change

Source: (G20/GPFI 2024)

While an exhaustive review is beyond the scope of this paper, a few select examples from empirical literature make this point:

• Switching retirement-plan enrollment from opt-in to opt-out increased participation from roughly half to more than four-fifths of eligible employees, with effects persisting for years (Madrian and Shea 2001).
• The spread of mobile money in Kenya is estimated to have lifted roughly two percent of households out of extreme poverty by strengthening informal risk-sharing networks (Suri and Jack 2016).
• Digital credit and instant lending have improved households' ability to absorb shocks and increased reported financial well-being, while also generating high default rates and widespread credit-bureau blacklisting among first-time and low-income borrowers (Suri, Bharadwaj, and Jack 2021; Brailovskaya, Dupas, and Robinson 2024).
• In Brazil, the expansion of payroll-deducted lending increased debt burdens among public-sector workers, reducing average consumption and increasing volatility among less financially sophisticated borrowers (Garber, Mian, Ponticelli, and Sufi 2024).

The lesson from these examples is not that financial sector policy consistently improves outcomes: the evidence is mixed. It is that the financial sector consistently shapes them. The same policy levers that can meaningfully improve financial resilience and reduce vulnerability to shocks can, if poorly designed or weakly supervised, allow debt burdens to deepen and generate harm. FSAs can help ensure that financial products and practices operating within their jurisdiction do not systematically undermine the financial resilience that households might otherwise build from their income. As the G20/GPFI theory of change makes clear, FSAs influence the landscape of retail financial services provision and innovation through several pathways, albeit to different degrees across jurisdictions.

Evidence from CGAP's Impact Pathfinder (CGAP 2026b) adds an important refinement: the relationship between financial sector development and household welfare is not uniformly positive, but rather pathway-specific and conditional. Whether financial services generate benefit or harm depends on who accesses them, how they are designed and delivered, what needs they finance or protect against, where they operate, and over what time horizon. The same product can improve financial resilience for one individual or household and deepen vulnerability for another (CGAP 2026b; Nielsen et al. 2026). Without granular outcome-level measurement, FSAs may be able to observe greater access, usage, or innovation, but still be unable to tell whether the system is improving or undermining household financial health, and for whom.

Financial health outcomes are shaped by forces that no financial sector policy can fully control. Income levels and volatility, employment precarity, housing costs, and macroeconomic conditions are often the primary determinants of whether households can meet obligations, absorb shocks, or build assets over time (OECD 2026b). In many low- and middle-income countries, a period of sustained economic growth or a single year of drought can shift population-level financial health indicators more dramatically than any supervisory intervention. FSAs should be candid about this: they do not have all the tools to resolve income inequality or macroeconomic instability. Box 2 clarifies the scope of FSA accountability, noting that measuring financial health does not make FSAs responsible for outcomes they do not control.

This limitation, however, is not a reason to stand aside. The financial sector is one of the principal channels through which economic stress is either amplified or absorbed. For example, a household facing an income shock is more likely to recover without lasting harm if it holds accessible savings, carries a sustainable debt load, and is not exposed to predatory products or opaque fees. On the other hand, when the financial system works against households, through aggressive lending, inadequate provision of insurance, exclusion from payment infrastructure, or consumer fraud, it can turn temporary hardship into entrenched financial vulnerability or distress.

In summary, multidimensional financial health measurement enables a cross-cutting, outcome-focused view of household financial lives. Knowing that a household is in debt arrears signals difficulty meeting an obligation at a single point in time. Knowing that the same household has also missed last month's rent, holds no insurance, lacks a savings buffer, and reports high financial stress reveals a qualitatively different and more severe vulnerability. Measuring financial health multidimensionally does not, in most cases, require new data collection, it requires a framework for using existing data differently. Account numbers and balances, loan repayment histories, outstanding debt, overdraft incidence, insurance coverage, and product usage patterns are visible to varying degrees, in the administrative regulatory data and national surveys that FSAs already commission or rely on. These data, however, are typically analyzed separately, rather than with a consolidated view of household outcomes. Disaggregating this picture by institution or product type also helps FSAs isolate where specific market actors—rather than broader macroeconomic forces—are contributing to financial distress, providing the evidential foundation for targeted supervisory or regulatory responses.

The framework introduced in the chapters that follow is designed to make that foundation as accessible and actionable as possible.

Chapter 2. Guiding Principles

Design choices in measurement frameworks are often implicit. This chapter makes them explicit. Three principles shaped this framework: a commitment to multidimensional measurement, a commitment to draw on complementary data sources, and an insistence on adaptability to different country contexts and data environments. Understanding them is essential for applying the framework.

How Should Financial Health be Measured?

1. Take a multidimensional approach. In a supervisory and policy context, financial health is best understood as multidimensional: no single indicator captures it adequately, and different dimensions can move in different directions at the same time. The six constructs and their associated indicators are designed to preserve visibility across those dimensions, so that FSAs can see where households are managing and where they are not, and which combinations of difficulty reveal the breadth of vulnerability across a household's financial life. Multidimensional analysis, through interrelated indicators, is the framework's primary mode and the one that makes it most useful for diagnosis and policy evaluation. For users who need a single summary figure for communication or high-level tracking, the indicators are compatible with composite index construction, but a composite is always an additional layer built on top of the multidimensional view, not a replacement for it. Section 5.3 in Chapter 5 provides guidance on composite construction and the methodological conditions that apply.

What Data Should be Used?

2. Use complementary data sources. Financial health measurement is strengthened by drawing on both administrative and demand-side survey data since each has unique, complementary strengths, as shown in Table 1.

Administrative data support more frequent measurement of quantitative, objective indicators like debt repayment, savings balances, and account activity. Where reporting infrastructure is mature, the marginal cost of collection is small. Data and analytical systems that are in place can enable efficient yearly, quarterly, or even monthly monitoring, a significant advantage over demand-side surveys for tracking change over time. When sufficiently granular data are available, more flexible, segmented analysis can be done across different population groups (e.g., gender disaggregated). However, administrative data do not capture the subjective dimensions of financial health and overlook informal financial activity and unregulated providers, thereby excluding segments of the population that remain outside the formal financial system. Using administrative data effectively also requires data infrastructure, analytical capacity, and governance arrangements; these are addressed in Chapter 5.¹ Surveys can span all six measurement constructs, including subjective experience, and can reach populations outside the formal system, shedding light on informal financial behaviors and barriers like trust and financial literacy that administrative data cannot capture and that often determine whether formal financial services are used at all. However, large-scale representative surveys can be expensive and infrequent, and are better suited to periodic stock-taking than continuous monitoring. Lighter "pulse-taking" surveys can be conducted more frequently in contexts where phone-based or online data collection is feasible and reliable.

TABLE 1. Complementary Data Sources for Financial Health Measurement

Source: Authors

How Should the Framework be Applied?

3. Adapt to the local context and build over time. The framework is a foundation, not a standard. The six constructs and 17 indicators define the full scope of what financial health covers linked to the G20/GPFI definition, but they do not prescribe which indicators every user must measure, in what sequence, or from what data source. Those choices depend on data and country context, within the boundaries set by each FSA's mandate and strategic priorities. Chapter 5 works through them by archetype. If the measurement covers only part of a construct, or if proxy indicators are used in place of direct measures, this should be stated explicitly in analysis and reporting. Transparency about coverage is essential: findings should not be presented as more comprehensive than the underlying data support. Measurement should deepen over time as data systems develop and analytical capacity grows. Data structure and granularity determine the depth of analysis that is possible; Chapter 5 (Figure 2) shows how analytical capabilities vary with data. Chapter 3 presents the details of the framework and how it was developed.

Key takeaway: Financial health measurement is most useful when it is multidimensional, drawing on both survey and administrative data, and adapted to what a given country context makes feasible. No single indicator, data source, or measurement approach is sufficient on its own. But FSAs do not need to have everything in place to begin; the framework is designed to meet them where they are.

Chapter 3. The Financial Health Measurement Framework

A growing number of FSAs are incorporating financial health measurement into their policy frameworks, recognizing their value for understanding household resilience and the real economy impacts of financial services (Iravantchi et al. 2025). Approximately 25 percent of AFI's member institutions have already measured financial health in some form, through either national surveys, administrative data, or both.

The large body of practical experience in measuring financial health, spanning international frameworks, national surveys, and private-sector initiatives, provides a foundation on which this framework builds. These initiatives span internationally focused efforts, such as the OECD/INFE toolkit, which provides guidance on measuring financial well-being through national surveys (OECD 2026a), and the Global Findex, which includes a set of questions on financial resilience and financial worry that give countries a shared demand-side baseline (World Bank 2025) to country-level ones: Kenya, Rwanda, and others have incorporated financial health questions into FinAccess and FinScope surveys and indexes (CBK, KNBS, and FSD Kenya 2024; NISR, AFR, and BNR 2024). Using macroeconomic and financial sector statistics, the IMF is developing a framework to model financial health, identify factors that correlate with financial health, and assess the impact of financial health on the economy. In addition, financial service providers (FSPs) in several markets have played an active role alongside FSAs, cofunding or co-implementing national financial health surveys and, in a growing number of cases, leading measurement efforts that have directly informed public policy. Examples include Australia, South Africa, the Netherlands, Brazil, and the Philippines, where private-sector initiatives have helped shape the regulatory conversation around financial health outcomes.

The measurement framework, presented in Table 2, is intended to complement this rich body of practice. The framework translates the four dimensions of the G20 definition—the ability to manage financial needs and obligations, to cope with shocks, to pursue aspirations, and to feel satisfied and confident—into six constructs, each of which break one dimension into a measurable component, and 17 indicators, which operationalize each construct using observable, outcome-based indicators.

TABLE 2. The Financial Health Measurement Framework

Note: The symbols after each indicator name denote whether the indicator represents a positive (+) or negative (-) in relationship to the construct.
Source: Authors

Table 3 compares this framework against three early and influential approaches that have shaped how financial health is measured. They are representative of the broader landscape of approaches. The US Consumer Financial Protection Bureau's (CFPB) psychometric scale produces a single well-being score from subjective self-assessments (CFPB 2017a); Kempson's research emphasizes the drivers and mechanisms that explain financial well-being outcomes (Kempson et al. 2017); and the Financial Health Network's (FHN's) indicator framework benchmarks individual behaviors against a normative checklist (FHN 2021, 3). Each was developed for a specific audience: financial educators, academic researchers and policymakers, and FSPs and rely primarily on demand-side surveys calibrated in high-income country settings. In design and methodology, the CGAP framework is most closely aligned with FHN's eight indicator framework: like FHN, it prioritizes objective, verifiable outcomes over self-reported perceptions and encourages complementary use of demand-side survey and regulatory administrative data. It differs in two respects: it is designed specifically for FSAs to provide a practical reference that helps FSAs understand why financial health is relevant to their work and what should be measured, and it encourages complementary use of administrative regulatory data alongside surveys. How indicators are specified is left open to enable their adaptation across diverse low- and middle-income country contexts where data infrastructure and financial system structure vary widely.

TABLE 3. How CGAP's Framework Compares to Foundational Financial Health Measurement Models

Source: Authors

3.1 How the Framework Was Developed

The framework is grounded in a comprehensive review of 70 existing financial health and financial resilience measurement initiatives spanning surveys, administrative data, composite indexes, and validated scales from across different regions and institutional contexts. These include dedicated financial health or well-being instruments and frameworks, such as the CFPB Financial Well-Being Scale (CFPB 2017), the OECD/INFE measurement toolkit (OECD 2026a), the Kenya Multidimensional Financial Health Index (FSD Kenya 2016), and the FHN's FinHealth Score (FHN 2026), as well as a range of national survey programs, FSP research, and FSA-led measurement efforts. For each initiative, the review documented the measurement constructs covered, the data sources used, and the methodological approach. The full list of sources reviewed is included in Annex 1.

In total, nearly 600 discrete survey questions and indicators were catalogued. Analyzing these at scale allowed recurrent constructs and measures to be identified across the full body of the existing work. All of the survey questionnaire items and administrative indicators used to develop and build financial health scales or indexes were included in the catalog. For broader surveys or frameworks, only those that related to the four dimensions of the G20 financial well-being working definition were retained. Questionnaire items or indicators relating to determinants or drivers of financial health—such as financial literacy, financial knowledge, and skills or items relating to broader economic well-being such as income and employment status—were excluded.

Five methodological decisions shaped the framework's design:

1. Focusing on a measurement framework rather than a broader conceptual or theoretical framework. The framework does not aim to present a comprehensive causal model or capture all potential pathways through which financial health outcomes are determined. Instead, it focuses narrowly on translating the dimensions of the G20 financial well-being definition into more tangible measurement constructs and indicators, grounded in what has already been implemented across a range of real-world measurement initiatives, rather than derived from theory alone.
2. Prioritizing existing measurement practice rather than developing new items or indicators. Rather than proposing indicators from scratch, the framework draws on what has already been used in practice. Each of the 17 indicators has been used in at least one existing measurement initiative.
3. Defining indicators primarily as observable rather than perceptions and as outcome-based rather than drivers. To the extent possible, indicators are defined as observable or verifiable events, conditions, or quantities. Also, to the extent possible, as measures that can be framed as outcomes, the end states that policies aim to influence, rather than as contributing factors or determinants. The indicators in Construct 6 (financial confidence, control, and satisfaction) are a necessary exception: that G20 dimension is inherently subjective and relies on self-reported assessments.
4. Selecting indicators for relevance to FSA's mandates and objectives. Indicators were chosen with attention to the areas over which FSAs have a mandate, objective, or responsibility, ensuring measurement feasibility and that what gets measured connects to what FSAs can act on.
5. Refining through iterative consultation with practitioners and technical experts. Two rounds of consultation with global organizations, FSAs, private-sector organizations, researchers, and other technical experts ensured that the indicators and guidance reflect practical implementation considerations and diverse country contexts.

Three indicators warrant specific explanation:

• "Active insurance coverage" (Indicator 10) and "pension or retirement account holder" (Indicator 13) can be interpreted as measures of access or usage rather than realized financial health outcomes. However, the absence of these financial instruments does signal a realized state of vulnerability: A household without health insurance facing a medical shock is exposed to a financial risk with potentially catastrophic consequences. Their inclusion reflects a deliberate choice to give greater visibility to these areas of financial inclusion policy that are critical for resilience and long-term financial well-being.
• "Financial loss due to fraud or deception" (Indicator 5) is not commonly included in existing financial health measures, given that it is a risk event that does not directly signal a household's underlying ability to manage financial obligations. However, it does represent an outcome of the usage of financial services and unexpected losses can cause harm such as by depleting savings, disrupting cash flow, or damaging credit. Unlike income shocks such as health emergencies or job loss that originate outside the financial system, fraud can occur through the financial system, under FSA supervision, and is directly addressable through conduct regulation, payment oversight, and licensing. Its inclusion reflects the growing centrality of digital financial services and the increased exposure to risks that accompanies them.

The measurement framework has not yet been widely applied across different country contexts. An initial pilot conducted jointly by CGAP and the NBR applied it to granular administrative data, providing an early proof of concept and practical lessons that are documented in Annex 2. How well the framework performs in different settings will only become clear as it is applied and as users share what they learn.

3.2 The Constructs and Indicators

Understanding why an indicator is included is as important as knowing how to measure it. Table 4 describes why each indicator is included, what it captures, and what it signals analytically. It also notes the FSA mandate areas to which each indicator is potentially most relevant, such as consumer protection, financial inclusion and literacy, or financial stability, safety, and soundness (prudential). These notes illustrate that financial health is an outcome lens that touches different mandates rather than belonging to any single one. They show where an indicator is likely to be informative, not which team is responsible for collecting the underlying data. Annex 2 builds on these definitions with data source options, construction guidance, and interpretation notes for each.

TABLE 4. Indicator Definitions and Rationale

Source: Authors

Chapter 4. Framework Use Cases

FSAs in several jurisdictions are already incorporating financial health measurement into their policy and analytical frameworks, though approaches and depth vary considerably (Iravantchi et al. 2025). The analytical value of that data lies in reading indicators together rather than in isolation. Three distinct uses of financial health measurement arise as most relevant for FSAs: diagnosing financial health and assessing how it varies across populations and over time, assessing the impact of financial policies and regulations on individuals' and households' outcomes, and detecting early warning signals of financial stress or misconduct.

Interpreting financial health data requires distinguishing between outcomes driven by factors outside the financial sector—income volatility, employment conditions, macroeconomic shocks—and outcomes that reflect how financial markets and their governance are functioning. This distinction is analytically important because it determines what FSA action is warranted. A population-wide deterioration in debt arrears following a macroeconomic downturn calls for a different response than a pattern of arrears concentrated in borrowers of a specific institution or product type, which may signal misconduct, unsuitable lending, or structural market failure. The three use cases that follow are designed with this distinction in mind: each one helps FSAs isolate where the financial sector—rather than broader economic forces—is contributing to or undermining household financial health, and what that implies for policy or regulatory action.

4.1 Diagnosing Financial Health and How It Varies Across Populations

The most immediate application of financial health measurement is diagnostic: understanding how the adult population is faring across the constructs in the framework and how financial health evolves over time. At its simplest, this means estimating the prevalence of outcomes, for example: what share of the adult population has a debt service-to-income ratio greater than half of monthly income, or what share is actively saving in long-term accounts or investing in financial assets? When data are integrated at the individual level, the share of the adult population meeting multiple conditions across more than one indicator can be estimated. These estimates give an FSA a baseline picture of the financial condition of the broader population and, when tracked over time, whether conditions are improving or deteriorating.

Prevalence estimates provide the baseline, but understanding why financial health outcomes vary across populations is what makes measurement policy-relevant. Two categories of explanatory variables are relevant in this context. The first covers variables that precede outcomes in the theory of change: financial service access and usage, and the skills, behaviors, and attitudes that influence how services are used. The second covers contextual factors: sociodemographic characteristics such as age, gender, and geography, which are foundational for disaggregation; economic factors such as income level, income variability, and employment status, which are among the strongest predictors of outcomes; social capital; and exposure to shocks. These variables can be collected alongside outcome indicators in demand-side surveys; many are also partially available in administrative data through know your customer (KYC) records and loan origination data, or by linking records across institutions (Alonso et al., forthcoming).

Combining outcome data with these other variables in a single data set opens up analytical capabilities that neither source provides alone. Research applying this kind of integrated analysis to national survey data in Australia and New Zealand, for example, found that socioeconomic factors and life events accounted for over half of the explained variation in financial well-being, while financial behaviors, such as active saving and spending restraint, accounted for around a fifth (ANZ Bank 2021). Methods such as structural equation modeling can make these relationships explicit, estimating how strongly each factor relates to financial health and through which pathways. For an FSA, this points toward the factors where intervention may yield the greatest improvement, though establishing whether those relationships are truly causal remains the task of the evaluation methods discussed in Section 4.2.

Illustrative diagnostic questions include:

• Do women show systematically lower emergency savings buffers and higher financial stress than men, and if so, is this gap widening or narrowing over time?
• On average, at what age do adults typically begin to save and accumulate assets for long-term goals, and how does this vary by income, gender, or other characteristics?
• Which population segments combine high debt-in-arrears rates with low insurance coverage and depleted savings?
• Are borrowers using instant digital loans at greater risk of higher debt service-to-income burdens than those using other loan types, controlling for income?

Key takeaway: By measuring financial health across six constructs, an FSA can assess the extent to which adults and households are financially healthy. With enough data granularity, FSAs can explore these questions by population segments to identify how outcomes vary by gender, income, age, or geography, as well as if some segments face vulnerabilities across multiple dimensions or have strengths that help mitigate or compensate for some vulnerabilities.

4.2 Assessing the Impact of Policies and Regulations on Outcomes

Where diagnostic measurement reveals what the financial health landscape looks like and how it evolves, this use case asks whether specific policy and regulatory actions are shifting financial health, and in which directions. By measuring financial health indicators before and after identifiable interventions, or by comparing affected and unaffected populations, FSAs can assess whether policies and regulatory interventions are achieving their intended effects at the customer level and whether they are generating unintended consequences. Without outcome-oriented indicators at the individual or household level, these real-world effects may go undetected.

Illustrative questions include:

• Financial inclusion: Following the rollout of simplified account-opening requirements or agent banking regulations, are newly included customer segments showing improved savings balances and reduced reliance on high-cost informal borrowing, or has expanded access primarily increased formal debt without strengthening day-to-day financial management?
• Consumer protection: After the introduction of enhanced disclosure requirements or cooling-off periods for digital credit, have digital repayment rates and debt-in-arrears levels among first-time digital borrowers improved, or do these protections appear insufficient to prevent over-indebtedness?
• Financial education and literacy: Following a national financial literacy strategy, are target populations demonstrating higher rates of long-term savings participation and improved subjective financial confidence, or do outcome indicators suggest that knowledge gains have not translated into changed financial behavior?
• Prudential supervision: Following the introduction of credit moratoria during an economic shock, did payment deferrals stabilize related financial health indicators such as access to emergency funds, debt in arrears, or debt service-to-income ratio, including among vulnerable borrowers? Did this relief translate into sustained access to credit once moratoria expired, or did institutions tighten lending standards in ways that excluded lower-income borrowers when their need was still greatest?

The evaluative logic here is distinct from the descriptive diagnostic analysis. The objective is to generate evidence about whether a specific policy action, such as a regulation enacted, a program launched, or a rule changed, caused a change in specific financial health indicators.

A key challenge is attribution. Changes in financial health indicators cannot on their own be attributed to a specific policy: incomes, prices, and credit conditions shift for many reasons simultaneously, so a simple before-and-after comparison conflates the policy's effect with everything else happening in the economy. Credible impact assessment therefore requires a counterfactual: an estimate of what would have happened to the affected population in the absence of the intervention.

Observational and quasi-experimental methods can construct this by exploiting variation in who was exposed to a policy, when, and by how much. In practice, this can take several forms: comparing borrowers on either side of a credit-score threshold to isolate the effect of receiving a high-cost loan in the UK (Gathergood, Guttman-Kenney, and Hunt 2019); building a modeled counterfactual from transaction-level data to estimate that the impact of overdraft reforms passed by the Financial Conduct Authority (FCA), which saved customers close to £1 billion (FCA 2023); treating Kenya's interest-rate cap as a natural experiment to trace unintended contractions in lending to lower-income borrowers (Alper et al. 2019); and using the staggered geographic rollout of mobile-money agents to estimate effects on household consumption and poverty (Suri and Jack 2016). The data foundation can be administrative or survey-based; what matters is that indicators are measured consistently for both exposed and comparison groups.

Key takeaway: Assessing impact is more analytically demanding than describing variation in outcomes, and it enables FSAs to understand not only where problems exist but also whether their responses are working. By tracking financial health indicators around identifiable policy interventions, FSAs can assess whether household outcomes are moving in the intended direction and whether unintended effects are emerging.

4.3 Detecting Early Warning Signals of Financial Stress or Misconduct

Supervisors with advanced granular data practices and analytical frameworks can use financial health indicators to detect risks emerging in the household sector earlier than traditional supervisory data can reveal and assess how those risks affect different consumer segments with potentially broader systemic implications, which can underpin more timely and targeted supervisory action. While the impact assessment use case is necessarily retrospective, early warning detection can help FSAs anticipate and identify issues before they become larger problems.

Illustrative questions include:

• Could rising debt in arrears or disproportionate increases in debt-service burdens among customers of specific FSP types, such as microfinance institutions or digital lenders, reflect mis-selling or irresponsible lending practices before formal complaints trigger supervisory attention?
• Do financial health indicators vary significantly across distribution channels, for example, between agent-based and branch-based customers of the same product, in ways that provide advanced warning of channel-specific conduct risks before they escalate?
• At what threshold is a debt service-to-income ratio predictive of credit repayment arrears in six months or one year?

Financial health measurement may also have applications in prudential supervision, offering a more granular and distributional lens on emerging household-sector stress. The debt service-to-income ratio has been identified as one of the most reliable aggregate early warning indicators of systemic banking crises (Drehmann and Juselius 2013), and the other indicators in this framework, including delinquency rates, savings buffers, and bills in arrears, capture related household-level dynamics. Measuring these at sufficient granularity, disaggregated by institution, product, and borrower segment, could provide a complementary and more distributional perspective on emerging stress, though the evidence base for this specific application is still developing. Several central banks are already exploring the links between household financial health data and prudential surveillance (Iravantchi et al. 2025).

Key takeaway: Tracking select financial health indicators continuously—by institution type, product, channel, and population segment—can detect early signals of consumer harm and household stress before they appear in complaints data or prudential ratios. This is most valuable as an input to supervisory prioritization decisions.

The three use cases differ in their data requirements and analytical demands. The diagnostic use case is the most accessible: it can begin with a single well-designed survey or an existing data set mapped against the framework. The evaluative and early warning use cases are more demanding, requiring greater data granularity, frequency, and research design sophistication. FSAs do not need to pursue all three simultaneously, or pursue any of them at full depth from the outset. The appropriate starting point depends on what data are already available, what analytical capacity exists, and what the FSA's immediate policy priorities are.

Two complementary data sources provide the foundation: survey-based measurement, which can span all six constructs and reach the full population including those outside the formal financial system, and administrative regulatory data, which covers the formally included population with a granularity and frequency that surveys cannot replicate. Chapter 5 helps FSAs identify their starting point by country archetype, and sets out the conditions that shape what is analytically feasible in a given context.

Chapter 5. Getting Started: Defining a Measurement Strategy

This chapter is intended for the data, research, and technical teams within FSAs and other stakeholders responsible for designing and implementing a measurement strategy. It is the operational companion to Chapters 1 through 4. Where those chapters made the case for financial health measurement, introduced the framework, and illustrated its analytical applications, this chapter helps teams work out where they are starting from, which data sources can support measurement in the short run (including how existing data sources can be aligned with the framework), and explains how measurement can deepen over time as data infrastructure and analytical capacity develop.

It is structured as follows: Section 5.1 sets the stage for measurement strategy by introducing the elements that shape what is analytically feasible in a given country context. It describes two complementary data sources—surveys and administrative regulatory data—and two data collection designs within each, distinguishing them by the analytical use cases they can support: diagnostic mapping, policy evaluation, and early warning monitoring. Section 5.2 translates this into action, providing recommendations on how to get started for each country archetype. Section 5.3 discusses composite indexes as optional summary measure of financial health. Annex 3 is the working reference for this chapter, providing a profile for each indicator covering definition, data source guidance, and interpretation.

5.1 Setting the Stage: Data Sources and Country Archetypes

Delivering on the three use cases requires a clear view of what data are available and what the data can support. Two complementary sources provide the foundation: survey data and administrative regulatory data each unlock different analytical possibilities, and together they can build a more complete portfolio of evidence than either can provide alone. Table 5 shows different data collection options within each source and shows how they connect with different use cases.

• Survey data sources: Survey data collection is the only feasible source for several indicators in the framework: the four subjective indicators (financial satisfaction, perceived control, outlook confidence, and stress), material deprivation, reliable access to emergency funds, and investment in physical or productive long-term assets. These depend on self-reporting, capturing coping strategies, and informal financial activity, or involve events (such as purchases of physical assets) that are not easily visible in regulatory administrative data.
• Administrative data sources: Administrative regulatory data offer continuous, behaviorally rich coverage of the formally included population. Since surveys hit limits with respect to the amount and precision of data that can be collected from respondents, the insights obtained from them can be complemented with quantitative data from administrative sources. For example, in some contexts, respondents may be reluctant to report outstanding debt or missed loan payments, or underreport usage of certain types of credit. Being able to leverage a purpose-built, household- or individual-level extract of credit usage, debt load, and repayment can add depth or precision when used alongside insights from surveys. In addition, large sample sizes that can be obtained when using administrative data and enable disaggregation of indicators not only by customer sociodemographics but also by type of institution, product, or channel, or all three in combination. Six of the 17 indicators (overdraft and nonsufficient-funds events, debt in arrears, debt service-to-income ratio, liquid-savings buffer, active insurance coverage, and pension or retirement account holding) are readily measurable with administrative data where the data are available, and four others can be partially constructed. Table A3 in Annex 3 maps the feasibility of constructing each indicator from both sources, including where administrative data offers greater precision than surveys.

TABLE 5. Data Platforms for Financial Health Measurement

Source: Authors

An important data design choice cuts across both survey and administrative data sources: whether to draw a new sample at each round or follow the same individuals over time. This choice, between a repeated cross-section design and a longitudinal design, has direct implications for which analytical use cases each platform can support. Box 3 sets out the key trade-offs.

Taken together, the choice of data source and data collection or integration design determines the depth of analysis that is possible. Richer and more granular data configurations enable analysis at more disaggregated levels, across more constructs, and at greater frequency. Figure 2 summarizes how analytical capabilities expand as data structure and granularity deepen.

FIGURE 2. How Data Structure and Granularity Shape Financial Health Analysis

Source: Authors

Two contextual factors determine what type of financial health measurement is analytically feasible in a country: the extent to which the adult population uses formal financial services and the maturity of the regulatory data infrastructure. The feasibility depends on whether the data collected from financial institutions is granular and accessible, and can be disaggregated for analysis. Together, these two factors define four country archetypes, set out in Table 6. Two other practical factors shape what is achievable in the short run: the budget available for new investments in data collection or infrastructure and the technical capacity available in-house. Archetypes are a starting point, not fixed labels. An FSA that is Archetype 4 today may move toward Archetype 1 over time as the data infrastructure develops and formal inclusion expands.

TABLE 6. Country Archetypes for Financial Health Measurement

Note: Country examples are illustrative. Archetype classification depends on current levels of formal financial inclusion and regulatory data infrastructure, which evolve over time.
Source: Authors

5.2 Recommendations on Getting Started with Measurement

Two recommendations apply to all country archetypes regardless of context. The first maps existing national surveys against the framework; the second establishes what can be measured using administrative regulatory data. Together, they identify which indicators can already be measured, at what level of detail and population coverage, and where the most important gaps and opportunities lie. Archetype-specific recommendations build on this foundation.

1. Conduct an assessment of existing national surveys: Map existing national survey instruments that operate in the country against the framework, as well as the indicator profiles, to assess the following:
   1. Does the survey questionnaire collectively cover the six constructs and 17 indicators in the framework?
   2. How do the questionnaire items used to construct indicators compare with existing practices?
   3. Do any survey questionnaire items align with internationally validated instruments such as the OECD/INFE and the Global Findex, to enable cross-country benchmarking?
   4. Does the survey instrument include explanatory variables needed for comprehensive diagnostic analysis, such as income, employment, financial service access and usage, financial literacy and knowledge, and exposure to shocks?
   5. Does the survey design—such as representativeness, ability to disaggregate estimates by sociodemographic groups, and frequency—facilitate the analytical use cases that are important to the FSA?
2. Where gaps or opportunities for improvement are identified, determine whether they can be addressed by refining existing instruments or adding modules in the next survey round.
3. Conduct an administrative data feasibility assessment: Table 7 classifies the 17 indicators by how readily they can be measured using administrative data. Working through this classification against existing regulatory reporting, the appropriate indicators can be constructed with the available data, level of detail, and population coverage—noting which types of FSPs are included or excluded. Where data are not available within existing regulatory databases, it should be determined whether data could be accessed from other authorities or government agencies. The indicator profiles in Annex 3 and the CGAP LENS (CGAP 2026a) can support this work. This feasibility assessment generates value even if individual indicators cannot yet be fully constructed. The NBR pilot demonstrated that attempting to build indicators from existing data reveals data quality issues, data coverage gaps, and institutional constraints that routine supervisory reporting may not reveal. The assessment itself is a diagnostic tool for the data infrastructure, and starting the measurement process can help improve the underlying data systems on which other supervisory functions depend (Alonso et al., forthcoming).

TABLE 7. A Classification of the Framework Indicators by Their Measurability with Administrative Data

Note: A more detailed version of this table is available in Annex 3.
Source: Authors

The indicator profiles in Annex 3 support both data source assessments and serve as the practical reference tool when FSAs are ready to measure a specific indicator. The archetype-specific recommendations in the following paragraphs provide guidance on where to focus first based on a user's context.

ARCHETYPE 1: HIGH FORMAL FINANCIAL INCLUSION AND ADVANCED REGULATORY DATA INFRASTRUCTURE

Archetype 1 FSAs have the foundational conditions to pursue all three use cases, making data integration—rather than data availability—the primary challenge and strategic priority. Records may exist across credit, deposit, insurance, and pension systems, but may not yet be linked at the individual level (lessons from the NBR pilot in Annex 2 apply here). In addition, one or more national surveys may be in place. If so, FSAs can assess whether collectively they cover the constructs in the framework and whether there are opportunities to align one of them more strongly with the framework or strengthen its diagnostic value by adding contextual questions that enable more comprehensive analyses. If no national surveys are in place, the FSA can consider investing in a flagship or pulse survey in ways that complement what is known from administrative data sources. Table 8 summarizes the key recommendations for Archetype 1 FSAs.

TABLE 8. Recommendations for Archetype 1 Countries

Source: Authors

The pilot conducted with the National Bank of Rwanda—an Archetype 1 context—provides practical lessons that apply directly here. Attempting to construct indicators from existing regulatory data revealed challenges that routine supervisory reporting had not revealed. Indicator construction was, in several cases, blocked not by the absence of data but by the way data were structured or encoded across reporting institutions. The feasibility assessment process itself functioned as a diagnostic tool for the data infrastructure, generating improvements that extended beyond financial health measurement to other supervisory functions. A key finding was that individual-level cross-sector data integration—linking credit, savings, insurance, and pension records via a national identifier—required governance arrangements and legal clarification even where the data technically existed. Archetype 1 FSAs should expect these integration challenges and treat their resolution as a parallel workstream alongside measurement strategy design. The lessons from Rwanda are documented fully in Annex 2.

Alongside investing in survey platforms and cross-sector administrative integration, FSAs can also invest in making their data more publicly available. This means publishing anonymized microdata for public use, releasing summary statistics broken down by geography and segments, and supporting clear pathways, such as data labs of formal application processes, through which researchers can access more granular data under controlled conditions. Doing so can help catalyze an ecosystem of third-party analysis including by academic, civil society, and private-sector stakeholders that can extend the diagnostic, evaluative, and early warning use cases well beyond what any single agency can pursue alone. See Box 4 for an example.

ARCHETYPE 2: HIGH FINANCIAL INCLUSION AND CONSTRAINED REGULATORY DATA INFRASTRUCTURE

For Archetype 2 FSAs, the priority is twofold: maximize the diagnostic value of existing survey data in the near term, while building the case for investment in granular regulatory data infrastructure over time. Population coverage in the formal financial system is sufficient to generate a national-level picture from regulatory data, but the data infrastructure needed to exploit that coverage is not yet in place. As a result, surveys are the primary vehicle for diagnostic and evaluative use cases. On the administrative data side, the starting point is identifying whatever pockets of granular data exist, such as credit registry records or mobile-money transaction data, and building from there as reporting infrastructure develops. Table 9 summarizes the key recommendations for Archetype 2 FSAs.

TABLE 9. Recommendations for Archetype 2 Countries

Source: Authors

ARCHETYPE 3: MODERATE OR LOW FINANCIAL INCLUSION AND ADVANCED REGULATORY DATA INFRASTRUCTURE

For Archetype 3 FSAs, the central challenge is that well-developed regulatory data cover only the formally included segment, which is not yet a majority of the adult population. This means the two data sources serve different populations and analytical purposes. Administrative data can support detailed analysis of the formally included, while surveys are essential for population-level measurement and for capturing those outside the formal system. Therefore, the priority is to invest in survey capacity while making the most of what administrative data can already offer. Table 10 summarizes the key recommendations for Archetype 3 FSAs.

TABLE 10. Recommendations for Archetype 3 Countries

Source: Authors

ARCHETYPE 4: LOW FORMAL INCLUSION AND CONSTRAINED REGULATORY DATA INFRASTRUCTURE

For Archetype 4 FSAs, the immediate priority is extracting maximum value from whatever data sources currently exist and using that process to document gaps and make the case for longer-term investment in data infrastructure. Both survey and administrative data are constrained. Regulatory data are limited to published aggregates, and survey coverage may depend on an international or donor-funded survey program. This makes it essential to understand what is currently available, what gaps exist, and what investments would do the most to expand measurement capability over time. Table 11 summarizes the key recommendations for Archetype 4 FSAs.

TABLE 11. Recommendations for Archetype 4 Countries

Source: Authors

As noted above, published aggregate statistics are the most immediately accessible entry point for system-level monitoring in this archetype. While they cannot substitute for granular data, they provide a practical starting point while longer-term data infrastructure is being developed. Box 5 maps illustrative published aggregate sources against each of the 13 quantitative indicators in the framework.

The archetype recommendations above identify where to focus first. Once priorities are clear, the indicator profiles in Annex 3 provide the practical guidance needed to begin measurement for each specific indicator. Each profile is self-contained; FSAs can go directly to a single profile without needing to read the rest of the annex. All profiles follow a standard four-part structure as shown in Figure 3.

FIGURE 3. Indicator Profile Structure

Source: Authors

The profiles are designed to be used iteratively, as a starting point when building an indicator for the first time, and as an ongoing reference as measurement practice develops. For users who would like to explore creating a single summary measure of financial health from a subset of indicators, see Section 5.3 for a discussion of when and how composite indexes can be constructed.

5.3 Composite Indexes: Uses, Limitations, and Construction

Composite indexes are used to create a single summary measure from multiple individual indicators that together relate to one multidimensional concept. Indexes are common in economics, environmental science, development, finance, health, and other fields, because many concepts or phenomena, like financial health, are multidimensional in nature, and it can be useful to create a structured numeric simplification of a complex reality.

In the context of this framework, composite indexes are an optional tool users can develop for specific purposes, but they should not be viewed as the endpoint of measurement or the main object of analysis. As argued in prior sections, this framework is intended to preserve a multidimensional view of financial health and the risk of leaning primarily on an index for analysis is that it can abstract away the specificity and variation that is useful for diagnosis, supervisory action, or regulatory decision-making. This section explains when an index may be useful, how to think about selecting indicators for the index, the methodological steps involved, the importance of sensitivity testing, and examples of two financial health indexes.

USES AND LIMITATIONS

The goal of an index is to condense information from several measurable dimensions of a broad concept into one value. While there are several purposes for constructing indexes, in the context of financial health two stand out:

1. Communication: A single headline measure is easier to understand and communicate to nonspecialist audiences than a dashboard of indicators.
2. Comparison: Indexes make it easier to track changes in overall financial health over time or compare financial health across sociodemographic groups.

Against these benefits are two important challenges that have implications for the diagnostic value of indexes in the context of this framework. The first is that there is a loss of nuance when indicators are aggregated: strength in one construct (e.g., ability to pay for basic needs without significant financial strain) can mask weakness in another (e.g., ability to cope with unexpected expenses), such that two groups may have a similar average score in an index, but are different in how they are vulnerable financially. The second related challenge can occur when assessing change over time: an aggregate index for a population might remain stable over time while masking important changes in the underlying structure of vulnerability (e.g., a situation where indicators of material deprivation are falling, but indicators of debt stress are rising). The loss of information from individual indicators or constructs can mean a loss in an ability to diagnose where populations might be struggling financially or identify the reason an index changes over time. As a result, FSAs also lose the specificity that is needed to define potential follow-up actions or interventions. There are ways to overcome these challenges during index design, such as the publication of subindexes (e.g., aligned with some of the constructs in the framework: liquidity for managing basic needs, debt sustainability, financial resilience) or the use of dashboards alongside indexes. Indexes can also be designed to be decomposable. A decomposable index allows the overall score to be broken back down into its constituent dimensions, indicators, or population subgroups.

INDICATOR SELECTION

Another challenge related to index construction is deciding which indicators to use. The starting point here are the 17 indicators in the framework conceptually organized into dimensions and constructs, which can be treated as a candidate pool rather than a prescriptive list. In practice, indicator selection is partly normative (it reflects what the designer considers most important about financial health) and partly empirical. Being explicit about which criteria drive indicator selection decisions improves the index's transparency and replicability. The following are some considerations:

• Coverage across the framework's dimensions and constructs. Decide upfront whether the index targets a comprehensive view of financial health or a narrower slice (e.g., resilience to shocks) and let that purpose drive coverage and indicator selection choices.
• Balance within and across dimensions. Avoid inadvertently overweighting any dimension by stacking multiple highly correlated indicators from it. Where the framework dimensions differ in how many candidate indicators they contain, resist including all available indicators from a data-rich dimension.
• Discriminatory power. Where there are multiple candidate indicators within a dimension, prefer indicators that exhibit variation across the populations or time periods of interest. Indicators where most respondents cluster at one value add little to a composite measure designed to differentiate groups or detect change.
• Redundancy. Examine pairwise correlations among candidate indicators. Where two indicators are very highly correlated, consider retaining one or document explicitly why both are kept (e.g., each captures a distinct facet of the same construct). Note that some correlation is desirable because indicators should relate to the underlying concept, but each indicator included in an index should contribute some unique information or diagnostic value about the underlying construct or dimension.
• Data quality, reliability, and comparability. Indicators should be measured consistently with stable definitions across the units being compared (e.g., individuals or households). Indicators that do not apply to segments of the population or with high nonresponse should be treated with caution.
• Avoid mixing objective with subjective measures. While this financial health measurement framework contains both inherently subjective measures (such as financial satisfaction or worry) and inherently objective measures (such as having a liquid savings buffer equivalent to one or more month of income) combining them in an index is problematic for two reasons. First, subjective indicators are filtered through individual reference points, expectations, and cultural norms (e.g., what constitutes being “very satisfied” can vary from person to person), so aggregating them across heterogeneous individuals or groups can complicate interpretation. Second, combining subjective with objective indicators in a single composite introduces unnecessary ambiguity, since changes can reflect shifting objective conditions, shifting perceptions, or both. Where both types of information are important, the preferable approach is to keep them in separate indexes or subindexes and use analyses of the relationship between them as a source of insight.
• Iterative testing. Treat the initial selection as provisional. Conduct sensitivity analyses on the candidate index and revisit selection if results are unstable.

METHODOLOGICAL CONSIDERATIONS

Once indicators are selected, several methodological steps are necessary to combine them into a single value. The three most consequential are standardization, weighting, and aggregation, and all choices should be tested for robustness as discussed in the following paragraphs. The OECD/JRC Handbook on Constructing Composite Indicators (2008) provides a comprehensive treatment of each of these steps and is a useful methodological reference for practitioners.

Standardization

The framework's indicators, depending on how they are constructed, can be measured on different scales: Binary (e.g., whether the household has any debt in arrears), continuous (e.g., a liquid savings buffer expressed in months of income), or ordinal (e.g., self-rated financial worry). These must be transformed onto a common scale before they can be combined. Common approaches include min-max rescaling; z-scores, which express each value relative to the sample mean and standard deviation but can be affected by outliers and may be harder to interpret; goalpost normalization using fixed reference points reflecting the worst and best plausible values; and distance-to-benchmark approaches that rescale against a target, peer-group average, or frontier. Whichever method is chosen, indicators must be oriented consistently so that higher values always represent better (or worse) financial health, and where unusually high or low values on an indicator are capped to prevent them from distorting the index, and the reasoning behind those limits should be explained.

Weighting

Weights determine how much each indicator, construct, and dimension contributes to the overall score, and the choice has important consequences. Equal weighting is transparent but not assumption-free, since it implies that every indicator (or dimension) is equally important to financial health; where dimensions contain different numbers of candidate indicators, equal weighting at the indicator level effectively up-weights data-rich dimensions, which argues for a nested approach with explicit weights across dimensions. Statistical weighting, derived from principal component analysis (PCA) or factor analysis, reflects the variance structure of the data and can be defensible within a single subindex, but weights derived from one sample may not transfer, and statistical importance does not always coincide with conceptual or policy importance. Normative weighting, derived from expert judgment or stakeholder consultation, is explicit about the normative content of the choice but depends on whose judgment is elicited. Whichever approach is used, the weights should be stated explicitly, and the implications for the resulting score, particularly which dimensions effectively dominate the index, should be examined.

Aggregation

Aggregation refers to how the standardized, weighted indicators are combined into a single value. The choice of aggregation method matters because different rules embed different assumptions about whether the dimensions of financial health are substitutable. Linear (additive) aggregation, such as a weighted sum, is simple and transparent but fully compensatory, allowing weakness in one construct (e.g., debt stress) to be masked by strength in another (e.g., ability to meet basic needs). Geometric aggregation, a weighted product, is partially compensatory: low scores in any one dimension drag the overall score down more than under linear aggregation, so weakness is penalized more heavily and cannot be offset as easily. The Human Development Index moved from arithmetic to geometric aggregation in 2010 for this reason (UNDP 2010). Partially noncompensatory approaches, such as the counting methods used in the Alkire-Foster framework (Alkire and Foster 2011) underlying the global Multidimensional Poverty Index (Alkire and Santos 2010), identify deprivation in each dimension against a threshold, combine those deprivation indicators into an overall weighted deprivation count, and then apply a poverty cutoff. As a result, a household cannot escape classification as deprived simply because it performs extremely well in one area. What matters is whether it experiences simultaneous deprivation across multiple dimensions. Given that this framework emphasizes the diagnostic value of preserving multidimensional information, partially compensatory or noncompensatory aggregation may be more appropriate than linear aggregation, particularly where the index is intended to support supervisory or regulatory decisions rather than only to communicate a headline summary.

Sensitivity Analysis

Every methodological choice described above involves discretion, and reasonable analysts can produce different scores from the same indicators. Any composite index built from this framework should therefore be accompanied by sensitivity analysis that tests the effect of adding or removing individual indicators, alternative standardization methods, alternative weighting schemes, alternative aggregation functions, and alternative treatments of missing data. Indexes that produce stable results across plausible alternative specifications are more defensible; where results shift materially, this should be reported transparently. Sensitivity analysis is also iterative with indicator selection. If particular indicators or dimensions are driving instability, that is useful information for revisiting the design of the index before it is published. Sensitivity analysis results should be documented and made available alongside the composite.

TWO REFERENCE POINTS: THE FINHEALTH SCORE AND THE KENYA MULTIDIMENSIONAL FINANCIAL HEALTH INDEX (MFHI)

Two existing composite approaches in financial health are relevant reference points. The Financial Health Network's FinHealth Score is derived from an eight-question survey instrument covering four components of financial health: spending, saving, borrowing, and planning (two questions per component). Each respondent's answers are scored and combined into a single value on a 0–100 scale, which is then used to classify individuals as Financially Healthy, Coping, or Vulnerable. The FinHealth Score is estimated annually through the Financial Health Pulse, a nationally representative survey of US households (FHN 2024). The Kenya Multidimensional Financial Health Index (MFHI) draws on the methodology used for the Oxford Multidimensional Poverty Index (MPI): equal weighting across three financial health dimensions with a cutoff to identify adults that can achieve positive outcomes across all three dimensions simultaneously (FSD Kenya 2016).

Conclusion

Financial health is gaining prominence on the global policy agenda, anchored in a definition agreed by G20 members. Financial sector authorities (FSAs) have clear use cases for measuring it, but they lack guidance on how to translate the G20 definition into indicators that are actionable in their jurisdictions and useful to the range of actors who need them. That is what this paper provides: a framework to analyze and track the level of financial health, and its different dimensions, in a given population (e.g., a country), and measure changes linked to specific interventions.

The framework translates the G20/GPFI definition of financial well-being into six measurable constructs and 17 indicators, grounded in a review of 70 existing initiatives. By distinguishing between objective and subjective dimensions, combining administrative and demand-side data sources, and adapting to different country archetypes, the framework meets FSAs where they are, while providing a path toward more sophisticated analysis over time.

A growing number of FSAs are already measuring financial health, to different degrees, and the evidence base is growing. The framework presented in this paper is a starting point, not a finished product. It will improve as it is applied, tested, and refined across different data environments and institutional contexts. The use cases for FSAs outlined in this paper illustrate what becomes visible when financial health is measured intentionally: which populations are being left behind, whether policies are delivering real improvements in individual and household outcomes, and where signs of financial stress risks are building across the population before they show in complaints or prudential ratios.

The immediate priority is for FSAs to move from awareness to action using the two assessments outlined in Chapter 5: mapping existing survey instruments against the framework's constructs and conducting an administrative data feasibility assessment against the 17 indicators. Most FSAs already collect data that can be partially mapped to this framework; the value is in using it more deliberately, documenting where gaps lie, and building the analytical capacity to interpret what financial health indicators reveal. FSAs do not need to measure all 17 indicators to begin, and they do not need to wait for perfect data conditions. As experience builds, sharing findings and lessons with the broader community of practice is itself a contribution: the evidence base for what works across different country contexts, data environments, and institutional mandates can only be developed collectively.