![](bg8.png)
0.20 0.40
0.60
0.80 1.00
RF
FD
0.95
0.77
0.91
0.76
0.95
0.87
0.98
0.77
Value
Approaches
Accuracy
Sensitivity
F-score
AUC
Figure 3: Performance.
wants to introduce a novel frequency-domain-based
model that allows a fraud detection system to operate
proactively. The results obtained are interesting, since
it is necessary to consider that the state-of-the-art
competitor taken into account (i.e., Random Forests),
in addition to using both classes of transactions to
train its model also preprocesses the dataset by us-
ing a balancing technique (i.e., SMOTE). It should be
noted that the credit card context taken into account is
only one of the possible scenarios, since the proposed
approach can be used in any context characterized by
financial electronic transactions.
A possible future work could take into account
the definition of an hybrid approach of fraud detec-
tion that combines the characteristics of the canonical
non-proactive approaches with those of our proactive
approach.
ACKNOWLEDGEMENTS
This research is partially funded by Regione Sardegna
under project Next generation Open Mobile Apps
Development (NOMAD), Pacchetti Integrati di
Agevolazione (PIA) - Industria Artigianato e Servizi -
Annualit
`
a 2013.
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