Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities
Guido de Blasio,
Alessio D'Ignazio and
No 16/20, Working Papers from Sapienza University of Rome, DISS
Using police archives, we apply machine learning algorithms to predict corruption crimes in Italian municipalities during the period 2012-2014. We correctly identify over 70% (slightly less than 80%) of the municipalities that will experience corruption episodes (an increase in corruption crimes). We show that algorithmic predictions could strengthen the ability of the 2012 Italy’s anti-corruption law to fight white-collar delinquencies.
Keywords: crime prediction; white-collar crimes; machine learning; classification trees; policy targeting (search for similar items in EconPapers)
JEL-codes: C52 D73 H70 K10 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cmp, nep-eur and nep-law
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