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Using data science to inform financial inclusion policy

Using data science to inform financial inclusion policy
利用数据科学为普惠金融政策提供信息
批准号:
ES/T005947/1
负责人:
Elizabeth Bermeo Velasquez
金额:
$12.83万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --

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中文摘要
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英文摘要
The main aim of this fellowship is to help improve policy response to tackle financial exclusion, which could benefit the 1.7 billion adults around the world who have no access to banking services (Demirgüc-Kunt et al., 2018). Having access to mainstream financial services (e.g. bank accounts, credit, and insurance) allows individuals to build up savings, invest in education or health, and manage risks, which can improve the overall quality of their lives, and help reduce inequality and poverty in the society. Being excluded from the banking system, on the other hand, deprives individuals of the opportunity to make the most of their resources, take advantage of economic opportunities, and take part in their local economies (Bermeo, 2019).To contribute to tackling financial exclusion, I will inform, through my PhD findings, academic and non-academic audiences on the state of this issue across the globe; and how the regulatory, legal and policy environment can help include more people, especially women (the most unbanked adults worldwide) into the formal financial system. Firstly, I will write up an analysis of specific aspects of my PhD data into three journal articles for publication. These articles will feature pioneering multilevel analysis of the determinants of financial inclusion across countries, the drivers of the financial inclusion gender gap, and the factors that matter for financial inclusion in Peru. Through these publications, I aim at prompting debate among academics about the role of policies, laws and regulations in advancing financial inclusion. Secondly, I will present at international conferences, participate in roundtable discussions and write a blog for the general public. These activities will allow me to reach non-academic audiences and will be mainly focused on promoting my research among government policy-makers and practitioners so that they can interpret and act upon my findings. Thirdly, I will become an influential member of the Financial Inclusion Forum. This Forum, which offers an international networking platform for microfinance academics and practitioners, will provide me with the opportunity to develop professional networks to build my reputation as financial inclusion expert and improve my ability to influence the debate about the financial inclusion global agenda.Another aim of this fellowship is to maximise the impact of data on financial inclusion research and policy. Firstly, I will establish partnerships with the Financial Conduct Authority (FCA) and the Personal Finance Research Centre (PFRC) to develop a big data research project to inform policymakers on the current state of financial inclusion and capability across the UK. The aim is to prompt debate about actions needed to improve access to financial services among excluded population groups. This study will be conducted after the fellowship and will use the FCA Financial Lives Survey database. The FCA has signaled a growing interest in financial inclusion, for example publishing an Occasional Paper on access to financial services (Collard et. al., 2016); and guidance for social landlords around helping tenants find alternatives to high-cost credit (FCA, 2018). Secondly, I will undertake training in advanced machine learning to acquire the skills to carry out (following the fellowship) this big data research project. This training will provide me with the skills to analyse and take advantage of the unprecedented growth of information generated by financial institutions (e.g., transactions, product usage, and customer characteristics), with the aim of helping to inform financial inclusion policy and practice. Thirdly, I will apply for an academic research grant (possibly the ESRC Secondary Data Analysis Initiative) to support this research project.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
复杂数据下半参数转换模型及其在老年慢性病发展中的应用研究
  • 批准号:
    72101261
  • 项目类别:
    青年科学基金项目(C类)
  • 资助金额:
    30.0万元
  • 批准年份:
    2021
  • 负责人:
    孙韬
  • 依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: