  Credit scoring models are the primary instrument used by financial
institutions to manage credit risk. The scarcity of research on behavioral
scoring is due to the difficult data access. Financial institutions have to
maintain the privacy and security of borrowers' information refrain them from
collaborating in research initiatives. In this work, we present a methodology
that allows us to evaluate the performance of models trained with synthetic
data when they are applied to real-world data. Our results show that synthetic
data quality is increasingly poor when the number of attributes increases.
However, creditworthiness assessment models trained with synthetic data show a
reduction of 3\% of AUC and 6\% of KS when compared with models trained with
real data. These results have a significant impact since they encourage credit
risk investigation from synthetic data, making it possible to maintain
borrowers' privacy and to address problems that until now have been hampered by
the availability of information.
