The past two decades have seen a rapid increase in interest in financial inclusion, both from policymakers and researchers. This paper surveys the main findings from the literature, documenting the trends over time and gaps that have arisen across regions, income levels, and gender, among others. It points out that structural, as well as policy-related, factors, such as encouraging banking competition or channeling government payments through bank accounts, play an important role, and describes the potential macro and microeconomic benefits that can be derived from greater financial inclusion. It argues that policy should aim to identify and reduce frictions holding back financial inclusion, rather than targeting specific levels of inclusion. Finally, it suggests areas for future research.

Over recent years millions of people who had been excluded from formal financial services have gained access to financial accounts. While this is a step forward, low use and inactivity keeps account dormancy rates persistently high. In light of this, the financial inclusion community is renewing its efforts to understand the role financial services play in the lives of poor people and how financial services can improve their well-being. We are asking the questions: Financial inclusion for what? What should we invest in? What are the promising welfare-enhancing solutions? Which ones should we experiment with? How do we achieve the nuance required to guide financial inclusion policy decisions that enable financial services to add value to poor people's lives? This book proposes a learning agenda based on extensive consultation with donors, researchers, and practitioners who support financial inclusion.

In 2011 the World Bank—with funding from the Bill and Melinda Gates Foundation—launched the Global Findex database, the world's most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers more than 140 economies around the world. The initial survey round was followed by a second one in 2014 and by a third in 2017. Compiled using nationally representative surveys of more than 150,000 adults age 15 and above in over 140 economies, The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution includes updated indicators on access to and use of formal and informal financial services. It has additional data on the use of financial technology (or fintech), including the use of mobile phones and the Internet to conduct financial transactions. The data reveal opportunities to expand access to financial services among people who do not have an account—the unbanked—as well as to promote greater use of digital financial services among those who do have an account. The Global Findex database has become a mainstay of global efforts to promote financial inclusion. In addition to being widely cited by scholars and development practitioners, Global Findex data are used to track progress toward the World Bank goal of Universal Financial Access by 2020 and the United Nations Sustainable Development Goals. The database, the full text of the report, and the underlying country-level data for all figures—along with the questionnaire, the survey methodology, and other relevant materials—are available at www.worldbank.org/globalfindex.

Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.

Financial literacy and financial education are not new topics, even though interest in these topics among policymakers, financial authorities, and academics continues to grow. The Routledge Handbook of Financial Literacy provides a comprehensive reference work that addresses both research perspectives and practical applications to financial education. This is the first volume to summarize the milestones of research in financial literacy from multiple perspectives to offer an overview. The book is organized into six parts. The first three parts provide a conceptual framework, which discusses what financial literacy is, how it should be measured, and explains why it represents a relevant topic and effective tool in enhancing decision-making among consumers as well as consumer protection strategies. Part IV addresses the connection between financial education and financial literacy, with chapters about financial education in school settings as well as for adults. This part includes an analysis of the role of Fintech and the use of gamification in financial education. Part V is a collection of contributions that analyze financial literacy and financial education around the world, with a focus on geographical areas including the U.S., South America, Western Europe, Eastern Europe, Asia, and Africa. This part also considers how financial literacy should be addressed in the case of Islamic finance. The concluding part of the book examines how financial literacy is related to other possible approaches to consumer finance and consumer protection, addressing the relationships between financial literacy and behavioral economics, financial well-being, and financial inclusion. This volume is an indispensable reference for scholars who are new to the topic, including undergraduate and graduate students, and for experienced researchers who wish to enrich their knowledge, policymakers seeking a broader understanding and an international perspective, and practitioners who seek knowledge of best practices as well as innovative approaches.

Asia has made significant progress in financial inclusion, but both its across-country and intra-country disparities are among the highest in the world. The gaps between the rich and the poor, rural and urban populations, and men and women remain deep. Income is the main determinant of the level of financial inclusion; but other factors, such as geography, financial sector structure, and policies, also play important roles. While some countries in the Asia-Pacific region are leaders in fintech, on average the region lags behind others in several important areas such as online (internet) purchases, electronic payments, mobile money, and mobile government transfers. This Departmental Paper aims to take stock of the development and current state of financial inclusion and shed light on policies to advance financial inclusion in the region. The research focuses on the impact of financial inclusion on economic growth, poverty reduction, and inequality, linkages between financial inclusion and macroeconomic policies, as well as structural policies that are important for improving financial inclusion. Given the increasing importance of financial technologies (fintech), the paper also provides a snapshot of the fintech landscape in the Asia-Pacific.

Will mobile money render cash less dominant over time in Africa? Can it promote financial inclusion? We shed light on these questions by exploring individual-level and nationally representative survey data for Uganda, a country in a region that pioneered mobile money in the world. We use the Propensity Score Matching method to robustly compare mobile money users and non-users across a range of indicators that capture individuals’ perceptions about cash, and the extent to which they remit, save, and borrow money. We present the first evidence that mobile money users, compared to non-users, are more likely to perceive cash as risky and less likely to prefer carrying large amounts of cash. We also confirm that mobile money users are more likely to receive and send remittances, save, and borrow. They also save and borrow larger amounts.