A rank-based measure to prioritise cyber risks
Emanuela Raffinetti, Paolo Giudici · Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) · 2019
A very crucial issue when dealing with the use of statistical and machine learning methods in Fintech applications is the construction of predictive accuracy diagnostics able to mitigate the risk of taking wrong actions.The motivation of our proposal is to further develop a more general measure, based on the distance between the observed response variable values and the same observed values ranked according to the corresponding values predicted by a given model.The measure, that we call Rank Graduation index (RG), can be used regardless of the nature of the response variable and is found quite effective in its application to cyber risk measurement.