Blockchain, big data analytics, AI and other new technologies are transforming the way of working for governments, businesses and society. The 2019 Forum will focus on the risks and opportunities of new technologies for anti-corruption & integrity.
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Fritz Schiltz

Ku Leuven
Winner of the 2019 ResearchEdge Competition

Fritz will be presenting, along with Mr. Titl and Mr. Mazrekaj, their work: "Identifying Politically Connected Firms: A Machine Learning Approach"

This article introduces machine learning techniques to identify politically connected firms. We use a unique dataset of all contracting firms from the Czech Republic. In this dataset, various forms of political connections can be determined from publicly available sources. The results indicate that over 75% of firms with political connections can be accurately identified. The model obtains this high accuracy by using only firm-level financial and industry indicators that are widely available in most countries. Compared to the logistic regression model that is commonly used to predict binary outcome variables, the proposed technique can increase the accuracy of predictions by up to 36% using the same set of variables and the same data.

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