Fraud Detection using Data-Driven approach
- URL: http://arxiv.org/abs/2009.06365v1
- Date: Tue, 8 Sep 2020 20:58:51 GMT
- Title: Fraud Detection using Data-Driven approach
- Authors: Arianit Mehana and Krenare Pireva Nuci
- Abstract summary: The first known online banking service came in 1980.
The ever increasing use of internet banking and a large number of online transactions increased fraudulent behavior also.
This research paper aims to construct an efficient fraud detection model which is adaptive to customer behavior changes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The extensive use of the internet is continuously drifting businesses to
incorporate their services in the online environment. One of the first
spectrums to embrace this evolution was the banking sector. In fact, the first
known online banking service came in 1980. It was deployed from a community
bank located in Knoxville, called the United American Bank. Since then,
internet banking has been offering ease and efficiency to costumers in
completing their daily banking tasks.
The ever increasing use of internet banking and a large number of online
transactions increased fraudulent behavior also. As if fraud increase was not
enough, the massive number of online transactions further increased the data
complexity. Modern data sources are not only complex but generated at high
speed and in real-time as well. This presents a serious problem and a definite
reason why more advanced solutions are desired to protect financial service
companies and credit cardholders.
Therefore, this research paper aims to construct an efficient fraud detection
model which is adaptive to customer behavior changes and tends to decrease
fraud manipulation, by detecting and filtering fraud in real-time. In order to
achieve this aim, a review of various methods is conducted, adding above a
personal experience working in the Banking sector, specifically in the Fraud
Detection office. Unlike the majority of reviewed methods, the proposed model
in this research paper is able to detect fraud in the moment of occurrence
using an incremental classifier. The evaluation of synthetic data, based on
fraud scenarios selected in collaboration with domain experts that replicate
typical, real-world attacks, shows that this approach correctly ranks complex
frauds. In particular, our proposal detects fraudulent behavior and anomalies
with up to 97\% detection rate while maintaining a satisfyingly low cost.
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