Prediction of Recidivism in Thefts and Burglaries Using Machine Learning

Background/objectives: Theft and burglary are two crimes against property that have a great social impact. Their prevention drastically lowers victimization rates and the feeling of insecurity in the population. The objective of this investigation is to obtain an index that allows the prediction of repeat offenses by criminals in these types of crimes, in order to support decision-making with respect to preventative actions. Methodology: In order to obtain the index, a group of machines learning was trained, with information provided by the Criminal Analysis and Investigative Focus System (CAIFS) from the Regional Public Prosecutor’s Office in Biobío, Chile. The information provided was from thefts and burglaries committed between 2012 and 2017 in the city of Concepción. Findings/application: The results show a characterization of repeat offenders in these types of crime and a recurrence index that allows for a greater assertiveness in the prediction of recidivism than the method that is currently being used.

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