Financial Inclusion Data
Working Group (FIDWG)

Country-level data on financial inclusion inform the national policymaking processes and are more detailed than global-level data, e.g. due to disaggregation by administrative units. Global-level data on financial inclusion are mostly used for country-to-country comparisons as well as for assessing trends on financial access around the world. Harmonization of definitions and standardization of data collection methodologies are necessary to generate useful global-level data and benchmarks. 

There are numerous freely available international databases relevant for financial inclusion. The most comprehensive global supply-side data source include the Financial Access Survey (FAS) of the IMF with supply-side data reported by Financial Policymakers and Regulators, and from the demand-side at a global scale the Global Findex of the World Bank collecting data from national surveys each 3 years in more than 144 jurisdictions as well as the World Bank Enterprise Surveys collects relevant financial inclusion data and indicators at a global scale. Several initiatives aim to reach a consensus on standardising basic indicators of financial inclusion, notably the Global Partnership for Financial Inclusion (GPFI), the OECD SME indicators, as well as the AFI Core Set and 2^nd Tier financial inclusion indicators developed by the AFI Financial Inclusion Data Working Group[1]. The main target of these initiatives is to offer a benchmark of common and basic indicators to be considered for measuring financial inclusion and to encourage countries to supplement the basic indicators for the country specific context.

Once the definition of financial inclusion is clear, it is easier to determine what kind of data is needed to measure the current state of financial inclusion, track changes over time and impacts of inclusion. Financial inclusion is a multidimensional concept. Each country is unique and should determine its own definition given the specific characteristics of the country and its financial sector. Although there is no common definition for financial inclusion there is a consensus on the basic dimensions that must be considered for measuring financial inclusion: financial inclusion indicators measure various aspects of access, usage, quality and impact. In addition, they consider specific target groups, especially SMEs, women and other vulnerable groups.

The proper application of good data is paramount to promoting evidence-based financial inclusion policy. Financial inclusion measurement is more than just numbers and statistics. FID is ultimately about people and how best to understand and communicate their financial needs and design appropriate products and services.

There is an increasing availability of high-quality supply and demand-side data within AFI members. The AFI data portal shows that over 40% of AFI members have undertaken at least one national demand-side survey within the last 3 years. These data, combined with the application of new innovative analytical and diagnostic tools and techniques has enabled policymakers and regulators to gain insights to the financial needs and behaviour of previously unserved populations, and develop targeted policy solutions.

The proper application of good data is paramount to promoting evidence-based financial inclusion policies. FID policies are not an end in itself but tools designed to allow policymakers and regulators take better informed decision-making in the different policy dimensions related to financial inclusion.

Parallel global trends include: national data frameworks and financial inclusion indicators and indices; a proper data segmentation including sex-disaggregated data, activity, age and location; measuring quality and social impact; innovative measurement tools and frameworks including the use of RegTech and Big Data; Regulatory Risk Assessments; Data reporting and dissemination; Monitoring and Evaluation principles and tools.

Fundamental and emerging topics under financial inclusion data (FID) are explored in-depth through AFI’s Financial Inclusion Data Working Group (FIDWG).

The collection and analysis of sex-disaggregated data is essential to bridging the financial inclusion gender gap, as it can both inform evidence-based financial inclusion policymaking and track the effectiveness of efforts to address barriers faced by women. Increasingly members are segmenting data to understand key aspects related to gender-led policies, and disseminate indicators to inform regulators, policymakers, and market implementors about important gaps financially impacting women in different contexts and the identify actions to reduce them.

Member institutions are increasingly making Maya Declaration commitments that fall under FID’s mandate. As of 2020, 116 commitments have been set that aim to improve the measurements and indicators for assessing financial inclusion. Form these 57 percent have already been completed.

PRIMARY THEMATIC AREA		2012	2013	2014	2015	2016	2017	2018	2019	2020
Financial Inclusion Data	Maya Declaration Targets	20	40	48	49	64	79	86	111	106
	Completed	12	26	32	32	38	43	43	50	57
	In Progress	8	14	16	17	26	36	45	61	47
	Completion Rate	60%	65%	67%	65%	59%	54%	47.7%	45%	54

• Develop common frameworks for FIDWG members to measure financial inclusion, including components and indicators
• Promote the adoption of these framework in AFI member countries and at international level
• Share lessons learned about target setting, survey methodologies, data analysis and using data to monitor and evaluate policymaking.

• Gender Inclusive Finance Data Subgroup:

After the 2014 Findex was released several initiatives for reducing the gender gap emerged increasing the demand for sex-disaggregated information. The 2017 Findex revealed an unchanged gender gap of 7 percentage points. All Global Findex indicators can be disaggregated by age, by income. However, the main issue with Global Findex is its lack of detail which is necessary for making informed policy decisions on country-level. Some regulators have made efforts of including sex-disaggregated questions in demand-side surveys or gathering relevant data from national institutions. But it becomes a challenge when the regulators don’t have the will to invest in collecting these data, which are more needed on the supply-side (the demand-side already include this component).

• Planned deliverable: Special Report on Supply-Side Inclusive Green Finance Data Collection

Inclusive Green Finance Subgroup:

An increasing number of AFI members voiced interest in scaling up peer learning on ways to develop and implement financial inclusion policies that also have positive environmental outcomes. This momentum culminated in September 2017 with the launch of the Sharm El Sheikh Accord on Financial Inclusion, Climate Change and Green Finance, endorsed by an overwhelming 94% of the AFI membership. The Accord commits AFI members to work together and with partners to identify, understand and implement financial inclusion policy solutions that also have positive outcomes for the environment, focusing on communities that are most vulnerable to climate change.24 As a preliminary step, FIDWG members agreed to work on an effort to define taxonomies: what counts as ‘inclusive green’ finance, how do we measure its impact, what is the role of climate data in financial regulation and policy making.

• Planned deliverable: Guideline Note on IGF Indicators from the Supply-side 

• RegTech Taskforce (jointly with DFSWG and GSPWG):

Any FinTech/RegTech-based approach to financial inclusion must recognize that technology is not perfect and can have unanticipated consequences. Until technologies are sufficiently advanced to police the effects of technologies, providers will need to test the outcomes of algorithmic interpretation of data continuously and retroactively. Second, technology may do exactly what developers anticipate, but the issue may be with the developers themselves. Financial history is replete with fraud and every new technology will be abused by some individuals. Third, technology is always accelerating and creating space for groups of new entrants, which makes the role of the regulator ever more challenging. In many cases, this will require regulators to respond using technology. RegTech includes automation and data-driven analysis of internal control systems (compliance, risk management, audit) and internal and external reporting. FinTech also raises broader issues of how to approach the regulation of innovation.

• Planned deliverable: Special Report on RegTech for Financial Inclusion

2022

• Regulatory and Supervisory Technologies for Financial Inclusion

2021

• Guideline Note on Demand-Side Approach to Inclusive Green Finance Data Collection
• National Financial Inclusion Strategy Monitoring and Evaluation Toolkit
• Policy Framework on MSME Data Collection: A Guide for Gender Inclusive Finance
• Data Collection Processes and Defining Micro, Small and Medium Enterprises

2020

• Guideline Note on Sex-Disaggregated Data Report Templates

2019

• Special Report: A client needs-centered approach to financial inclusion measurement
• AFI Core Set Policy Model
• Guideline Note 33: Digital Financial Services Indicators

2017

• Guideline Note 26: Sex-Disaggregated Data Toolkit
• Guideline Note 25: Leveraging Sex-Disaggregated Data

2016

• Guideline Note 24: Financial Inclusion Data Tracking and Measurement – GIS Mapping to Inform Policymaking
• Guideline Note 22: Indicators of the Quality Dimension of Financial Inclusion
• Guideline Note 18: An Index to Measure the Progress of Financial Inclusion

2015

• Guideline Note 16: SME Financial Inclusion Indicators Base Set

2014

• The Use of Financial Inclusion Data: Country Case Study of the Philippines
• The Use of Financial Inclusion Data: Country Case Study of Bangladesh
• The Use of Financial Inclusion Data: Country Case Study of Burundi
• The Use of Financial Inclusion Data: Country Case Study of Mexico
• The Use of Financial Inclusion Data: Country Case Study of Peru
• The Use of Financial Inclusion Data: Country Case Study of South Africa

2013

• Guideline Note 11: MFS Indicators for Measuring Access and Usage
• Guideline Note 10: Financial Inclusion Data Tracking and Measurement: emand-Side Surveys to Inform Policymaking
• Guideline Note 04: Measuring Financial Inclusion: Core Set of Financial Inclusion Indicators

2011

• Measuring Financial Inclusion: Core Indicators for Financial Inclusion
• Case Study: Measuring financial inclusion in Mexico: CNBV’s approach to obtaining better data for decision-makers

2010

• Policy Paper: Financial inclusion measurement for regulators: Survey design and implementation