Saniya Ansar

Bio: Saniya Ansar is an academic researcher from World Bank. The author has contributed to research in topics: Financial inclusion & Financial services. The author has an hindex of 4, co-authored 7 publications receiving 662 citations.

The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution

Abstract: The Global Findex Database 2017 presents key findings from the Global Findex database, with detailed insight into how adults in more than 140 economies access accounts, make payments, save, borrow, and manage risk. As the data show, each economy has its own successes, challenges, and opportunities when it comes to financial inclusion. A growing body of research demonstrates the impact of country advances on significant priorities such as reducing poverty, hunger, and gender inequality.

The Global Findex Database 2017: Measuring Financial Inclusion and Opportunities to Expand Access to and Use of Financial Services

Abstract: The Global Findex database is the world's most comprehensive set of data on how people make payments, save money, borrow and manage risk. Launched in 2011, it includes more than 100 financial inclu...

Making it easier to apply for a bank account: a study of the Indian market

Abstract: This paper draws on new individual-level survey data from India to study the costs of opening an account and the efficiency of the account application process. The data show a recent increase in account ownership, especially by women and poor adults. The data also suggest that India's flagship financial inclusion program, the Jan Dhan Yojana scheme, has made it easier to get an account, through lower costs and greater ease of applying. Yet despite the scheme's initial successes, people who wish to apply for an account continue to incur a range of costs. The survey results suggest several recommendations that could improve the account application process and increase ownership and usage of accounts.

Global Findex Database 2017 [La base de datos Global Findex 2017]

Abstract: The Global Findex database is the world's most comprehensive set of data on how people make payments, save money, borrow and manage risk. Launched in 2011, it includes more than 100 financial inclusion indicators in a format allowing users to compare access to financial services among adults worldwide -- including by gender, age and household income. This third edition of the database was compiled in 2017 using nationally representative surveys in more than 140 developing and high-income countries. The database includes updated indicators on access to and use of formal and informal financial services. It features additional data on Fintech and digital financial services, including the use of mobile phones and internet technology to conduct financial transactions. Global Findex data is utilized to track progress toward the World Bank's goal of Universal Financial Access by 2020 and the United Nation's Sustainable Development Goals. The data also is a source for the G20 Financial Inclusion Indicators and a benchmark for policymakers seeking to expand access to and use of financial services. Lastly, this report discusses opportunities to expand access to financial services among the unbanked, and ways to promote greater use of digital financial services among the underbanked.

On the Rise of FinTechs – Credit Scoring using Digital Footprints

Abstract: We analyze the information content of the digital footprint – information that people leave online simply by accessing or registering on a website – for predicting consumer default. Using more than 250,000 observations, we show that even simple, easily accessible variables from the digital footprint equal or exceed the information content of credit bureau scores. Furthermore, the discriminatory power for unscorable customers is very similar to that of scorable customers. Our results have potentially wide implications for financial intermediaries’ business models, for access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the digital sphere.

Does financial inclusion reduce poverty and income inequality in developing countries? A panel data analysis

Abstract: Financial inclusion is a key element of social inclusion, particularly useful in combating poverty and income inequality by opening blocked advancement opportunities for disadvantaged segments of the population. This study intends to investigate the impact of financial inclusion on reducing poverty and income inequality, and the determinants and conditional effects thereof in 116 developing countries. The analysis is carried out using an unbalanced annual panel data for the period of 2004–2016. For this purpose, we construct a novel index of financial inclusion using a broad set of financial sector outreach indicators, finding that per capita income, ratio of internet users, age dependency ratio, inflation, and income inequality significantly influence the level of financial inclusion in developing countries. Furthermore, the results provide robust evidence that financial inclusion significantly reduces poverty rates and income inequality in developing countries. The findings are in favor of further promoting access to and usage of formal financial services by marginalized segments of the population in order to maximize society’s overall welfare.

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics.

Abstract: Anti-money laundering (AML) regulations play a critical role in safeguarding financial systems, but bear high costs for institutions and drive financial exclusion for those on the socioeconomic and international margins. The advent of cryptocurrency has introduced an intriguing paradox: pseudonymity allows criminals to hide in plain sight, but open data gives more power to investigators and enables the crowdsourcing of forensic analysis. Meanwhile advances in learning algorithms show great promise for the AML toolkit. In this workshop tutorial, we motivate the opportunity to reconcile the cause of safety with that of financial inclusion. We contribute the Elliptic Data Set, a time series graph of over 200K Bitcoin transactions (nodes), 234K directed payment flows (edges), and 166 node features, including ones based on non-public data; to our knowledge, this is the largest labelled transaction data set publicly available in any cryptocurrency. We share results from a binary classification task predicting illicit transactions using variations of Logistic Regression (LR), Random Forest (RF), Multilayer Perceptrons (MLP), and Graph Convolutional Networks (GCN), with GCN being of special interest as an emergent new method for capturing relational information. The results show the superiority of Random Forest (RF), but also invite algorithmic work to combine the respective powers of RF and graph methods. Lastly, we consider visualization for analysis and explainability, which is difficult given the size and dynamism of real-world transaction graphs, and we offer a simple prototype capable of navigating the graph and observing model performance on illicit activity over time. With this tutorial and data set, we hope to a) invite feedback in support of our ongoing inquiry, and b) inspire others to work on this societally important challenge.

Questioning mobility as a service: Unanticipated implications for society and governance

Abstract: In this paper we focus on the development of a new service model for accessing transport, namely Mobility as a Service (MaaS) and present one of the first critical analyses of the rhetoric surrounding the concept. One central assumption of one prevalent MaaS conceptualization is that transport services are bundled into service packages for monthly payment, as in the telecommunication or media service sectors. Various other forms of MaaS are being developed but all tend to offer door-to-door multi-modal mobility services, brokered via digital platforms connecting users and service operators. By drawing on literature concerned with socio-technical transitions, we address two multi-layered questions. First, to what extent can the MaaS promises (to citizens and cities) be delivered, and what are the unanticipated societal implications that could arise from a wholesale adoption of MaaS in relation to key issues such as wellbeing, emissions and social inclusion? Second, what are de facto challenges for urban governance if the packaged services model of MaaS is widely adopted, and what are the recommended responses? To address these questions, we begin by considering the evolution of intelligent transport systems that underpin the current vision of MaaS and highlight how the new business model could provide a mechanism to make MaaS truly disruptive. We then identify a set of plausible unanticipated societal effects that have implications for urban planning and transport governance. This is followed by a critical assessment of the persuasive rhetoric around MaaS that makes grand promises about efficiency, choice and freedom. Our conclusion is that the range of possible unanticipated consequences carries risks that require public intervention (i.e. steering) for reasons of both efficiency and equity.