World Bank Group
2 MLProjects completed and implemented
12+Months one integrated remote team
World Bank approached us to research viable use cases of applied artificial intelligence and facilitate member states to make data-informed decisions. We conducted an in-depth literature review focused on applied predictive analytics in solving the pertinent problems in health and human development. We found that machine learning is mostly used in diagnostics, so we proved in a second project that ML can indeed be used for more efficient budget allocation and policy planning, not just diagnostics.
Hence we validated data availability and the predictive analytics potential within the local organizations and stakeholders, such as measuring the outcome of RMNCAH services in Bangladesh or the purposes of developing strategies for data collection, data engineering, and synthesis from different data sources.
Data for good - empowering communities
It’s no secret that data has the power to change societies. World Bank is pushing its mission to drive equality and provide support to emerging countries through machine learning.
Harnessing the power of data so no child is left behind
Data plays a crucial role in the 2030 agenda set out by the Sustainable Development Goals (SDGs). It helps us to focus policies and make better decisions. It is needed to set targets, measure progress towards those targets and to hold governments accountable to their commitments under the SDGs
World Bank Group is a unique global partnership fighting poverty worldwide through sustainable solutions. The World Bank is a vital source of financial and technical assistance to developing countries around the world. It comprises two institutions: the International Bank for Reconstruction and Development, and the International Development Association.
"The potential for big data to transform government is vast"
"This brief focuses on big data solutions with applications in service delivery, policymaking and citizen engagement – areas where big data can play a transformational role"
"What data do decision makers really use, and why?"
"When it comes to revolutions, the data revolution has certainly been less bloody than, say, those in the 18th and 19th centuries. Equally transformative? A question for historians."