Open Access Open Access  Restricted Access Subscription or Fee Access

Detect Fraud Bank Checks with Convolutional Neural Network Processing Algorithm

Ying Bai, Dali Wang

Abstract


Abstract

The purpose of this study is to design and build a fraud check detection system with a Convolutional Neural Network (CNN) algorithm to quickly and easily detect a fraud or altered bank check in real time. A MATLAB Deep Learning Toolbox with related CNN algorithm is used to assist this study to quickly detect any fraud or altered bank check when it is deposited and scanned by a scanner in any ATM in US. The testing and validation processes have been performed to confirm the effectiveness and correctness of this detecting system. The current correct detecting rate is 97.5% for checks deposited via ATMs.

 

Keywords: Altered check inspections, convolutional neural network processing, fraud bank check detections, image signal processing, image detections


Keywords


Fraud bank check detections, altered check inspections, convolutional neural network processing, image signal processing and detection.

Full Text:

PDF


DOI: http://dx.doi.org/10.37591%2Fjoipprp.v7i3.2587

Refbacks

  • There are currently no refbacks.