Introduction

This working paper responds to a call from the G20 Global Partnership for Financial Inclusion (GPFI) by translating its 2024 working definition of financial well-being into a practical measurement framework for financial sector authorities (FSAs). Grounded in a comprehensive review of 70 existing measurement initiatives and nearly 600 discrete survey items, the framework is designed to work across diverse data environments. It moves beyond traditional access and usage metrics to provide an outcome-oriented lens that connects financial oversight with the lived experience of individuals and households. The framework was tested through a pilot with the National Bank of Rwanda (NBR) using granular administrative regulatory data.

Chapter 1. The case for financial health measurement

FSAs have traditionally relied on institutional indicators like capital adequacy and non-performing loan (NPL) ratios. However, as mandates expand to include consumer protection and financial inclusion, there is a growing need for outcome-level data. Research across various settings, including Bangladesh, India, Kenya, and the United States, shows that household finances are highly volatile. For instance, the spread of mobile money in Kenya is estimated to have lifted roughly 2 per cent of households out of extreme poverty. Conversely, poorly supervised digital credit can lead to high default rates. Financial health measurement helps FSAs determine if the financial sector is delivering actual value, acting as a buffer against economic stress rather than an amplifier of it.

Chapter 2. Guiding principles

Three core principles shaped the framework. First, it takes a multidimensional approach, recognising that no single indicator can capture financial health. Second, it promotes the use of complementary data sources, combining the monitoring frequency of administrative data with the depth of demand-side surveys. While administrative data can support monthly or quarterly monitoring, it does not capture subjective dimensions or informal activity. Third, the framework is designed for adaptability, allowing FSAs to deepen measurement as their data infrastructure and analytical capacity develop over time.

Chapter 3. The financial health measurement framework

The framework organises financial health into six measurement constructs and 17 indicators. These constructs include the ability to pay for basic needs without significant strain, the usage of financial services without incurring loss from fraud, and the maintenance of a sustainable debt load. Specific indicators include positive net cash flow, debt service-to-income ratio (DSTI), and liquid savings buffers. The framework also incorporates subjective measures, such as satisfaction with financial situation and perceived financial control. Notable additions include indicators for financial loss due to fraud or deception, reflecting the rising risks associated with digital financial services.

Chapter 4. Framework use cases

FSAs can apply the framework across three primary use cases. Used diagnostically, it identifies where vulnerability is concentrated among sociodemographic segments. Used evaluatively, it monitors outcomes before and after policy interventions; for example, the UK Financial Conduct Authority (FCA) used transaction-level data to estimate that overdraft reforms saved customers close to £1 billion. Finally, as an early warning tool, the framework monitors indicators like DSTI, which is a reliable predictor of systemic banking crises, to identify emerging household stress before it appears in complaints data or prudential ratios.

Chapter 5. Getting started: Defining a measurement strategy

Measurement feasibility depends on formal financial inclusion levels and the maturity of regulatory data infrastructure. The report categorises countries into four archetypes. Archetype 1 countries, such as Brazil and Rwanda, have high inclusion and advanced infrastructure, allowing for individual-level cross-sector data integration. In contrast, Archetype 4 countries, like Madagascar or Niger, rely primarily on published aggregate statistics and international survey programmes like the Global Findex. Recommendations for all FSAs include conducting assessments of existing national surveys and administrative data feasibility. The chapter also discusses composite indexes, noting they are useful for communication but can mask underlying vulnerabilities if used in isolation.

Conclusion

Financial health is an essential dimension of well-being that merits visibility through intentional measurement. The proposed framework provides a starting point for FSAs to track outcomes and measure changes linked to specific interventions. By distinguishing between objective and subjective dimensions, the framework enables more sophisticated analysis. FSAs do not need to measure all 17 indicators to begin, and as experience builds, sharing findings will help develop a collective evidence base for what works across different country contexts and institutional mandates.

Annex 2: Applying the framework with administrative data — Lessons from Rwanda

The pilot with the NBR, an Archetype 1 context, assessed the feasibility of constructing 13 of the 17 indicators using the bank’s Electronic Data Warehouse (EDWH), which integrates data from some 500 financial institutions. A core lesson was the “principle of learning by doing,” as attempting to construct indicators functioned as a diagnostic exercise for the data infrastructure itself. While the EDWH holds granular account-level data, the pilot found that indicator construction was sometimes blocked by how data were structured or encoded. Granularity and a national identification system for de-duplication were identified as prerequisites for this administrative data approach.