A noteworthy and innovative article previously disseminated in the RBF Bulletin as a White Paper has now been published by peer-reviewed journal Plos One. In this article, authors Grover, Bauhoff and Friedman find that machine learning techniques (using a Random Forest approach) can be used to identify audit targets and thereby reduce costs of verification—a critical (and somewhat expensive) element of results-based financing.

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Author/s: Dhruv Grover, Sebastian Bauhoff, Jed Friedman
Countries: Zambia
Date of Publication: January 2019

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