Abstract:
With the significant development of communications
and computing, bank fraud is growing in its forms and amounts.
In this paper, we analyze the various forms of fraud to which are
exposed banks d data mining tools allowing its early detection
data already accumulated in a bank. We use supervised learning
methods Support Vector Machines with Spark (SVM-S) to build
models representing normal and abnormal customer behavior
and then use it to evaluate validity of new transactions. The
results obtained from databases of credit card transactions show
that these techniques are effective in the fight against banking
fraud in big data. Experiment result from the study show that
SVM-S have better prediction performance than Back
Propagation Netw orks (BPN). Besides the average prediction,
accuracy reaches a maximum when training the data ratio
arrives at 0.8.
