An Online Predictive System for Mapping Visual Scenes of Fraudulent Behaviour - Ubani, C; Anireh, V.I.E; Nwiabu, N.D (International Journal of Computer Sciences and Engineering, 2022)
Fraudulent behaviour are suspicious activities that usually occur before a crime takes place. These suspicious activities are being carried out on a day-to-day basis in banks, supermarkets, restaurants, Bus stop, offices, residential buildings, companies e.t.c. Within the banking industry, fraudulent behaviour is when a customer or a person makes suspicious moves before committing a crime. In this paper, an online predictive system for mapping visual scene against fraudulent behaviour was developed. The dataset for this system was collected from Kaggle database. The analysis of the video clips gave a total of 427 frames, 380 was visually mapped to be of fraudulent behaviour while 47 was being mapped to be of normal behaviour. These frames were used in training a convolutional neural network for detecting fraudulent behaviour from a video clip. The proposed model was deployed to web using python and flask framework. Our result gave about 99.99%. The proposed system was compared with that of Nakib et.al. (2018). The result of Nakib et.al. (2018) gave an accuracy of about 90.2% while that of our proposed system gave 99.99%.