THE DATA
The following table compares the research findings to highlight which combination of models provides the highest prediction accuracy.
PREDICTION ACCURACY (%) FRAUD NON- FRAUD TOTAL
STUDY
METHOD/ TECHNIQUE
APPLICATION
Aleskerov et al (1997) Bell and Carcello (2000) Brause et al (1999)
ANNs
Credit Card
n/a
n/a
n/a
Financial Reporting
55
96
87
Statistical
Data mining and ANNs
Credit Card
n/a
n/a
n/a
Bolton and Hand (2002)
Clustering
Credit Card
n/a
n/a
n/a
Calderon and Green (1994) Dorronsoro et al (1997)
Financial Reporting
20
89
Statistical
n/a
ANNs
Credit Card
n/a
n/a
n/a
Ezawa and Norton (1996)
BNNs
Credit Card
n/a
n/a
n/a
Ghosh and Reilly (1994)
ANNs
Credit Card
n/a
n/a
n/a
Financial Reporting
68
74
72
Green and Choi (1997)
ANNs
Leonard (1995)
Expert System
Credit Card
n/a
n/a
n/a
Financial Reporting
35
86
76
Lin et al (2003)
FNNs
Quash and Sriganesh (2007) Zaslavsky and Strizkak (2006)
ANNs (Self-Organising Maps)
Credit Card
n/a
n/a
n/a
ANNs
Credit Card
n/a
n/a
n/a
Summary of the most notable investigations into the use of Artificial Intelligence at mitigating fraud.
The greatest challenge when talking about artificial intelligence/machine learning is actually in understanding what data sets we are looking at, and what model/combination of models to apply. Amazon’s Machine Learning offering is one example of an automated process which analyses the data and automatically selects the best model to use in the scenario. Other big players who have similar offerings are IBM, Google and Microsoft.
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