Fraud Detection using Machine Learning in e-Commerce

The volume of internet users is increasingly causing transactions on e-commerce to increase as well. We observe the quantity of fraud on online transactions is increasing too. Fraud prevention in e-commerce shall be developed using machine learning, this work to analyze the suitable machine learning algorithm, the algorithm to be used is the Decision Tree, Naive Bayes, Random Forest, and Neural Network. Data to be used is still unbalance. Synthetic Minority Over-sampling Technique (SMOTE) process is to be used to create balance data. Result of evaluation using confusion matrix achieve the highest accuracy of the neural network by 96 percent, random forest is 95 percent, Naive Bayes is 95 percent, and Decision tree is 91 percent. Synthetic Minority Over-sampling Technique (SMOTE) is able to increase the average of F1-Score from 67.9 percent to 94.5 percent and the average of G-Mean from 73.5 percent to 84.6 percent.

[1]  Hossam Faris,et al.  Optimizing connection weights in neural networks using the whale optimization algorithm , 2016, Soft Computing.

[2]  Jin Li,et al.  Differentially private Naive Bayes learning over multiple data sources , 2018, Inf. Sci..

[3]  Masanori Suganuma,et al.  A genetic programming approach to designing convolutional neural network architectures , 2017, GECCO.

[4]  Safa Sadaghiyanfam,et al.  Comparing the Performances of PCA (Principle Component Analysis) and LDA (Linear Discriminant Analysis) Transformations on PAF (Paroxysmal Atrial Fibrillation) Patient Detection , 2018, ICBSP.

[5]  Rimbun Siringoringo,et al.  KLASIFIKASI DATA TIDAK SEIMBANG MENGGUNAKAN ALGORITMA SMOTE DAN k-NEAREST NEIGHBOR , 2018 .

[6]  Tri Dev Acharya,et al.  Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China) , 2018 .

[7]  Ali Ouni,et al.  Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † , 2018, Energies.

[8]  Kenneth C. Laudon,et al.  E-commerce: Business, Technology, Society , 2002 .

[9]  Fabian Ruehle.  Evolving neural networks with genetic algorithms to study the string landscape , 2017, 1706.07024.

[10]  Arti Mohanpurkar,et al.  Credit card fraud detection using Hidden Markov Model , 2011, 2011 World Congress on Information and Communication Technologies.

[11]  Shini Renjith,et al.  Detection of Fraudulent Sellers in Online Marketplaces using Support Vector Machine Approach , 2018, ArXiv.

[12]  Osmar R. Zaïane,et al.  Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[13]  David N. Barton,et al.  Selecting methods for ecosystem service assessment: a decision tree approach , 2017 .

[14]  Guanjun Liu,et al.  Refined Weighted Random Forest and Its Application to Credit Card Fraud Detection , 2018, CSoNet.

[15]  S. Siva Prakash,et al.  Credit Card Fraud Detection using Adaboost and Majority Voting , 2019 .

[16]  Youngshin Han,et al.  Data Imbalance Problem solving for SMOTE Based Oversampling: Study on Fault Detection Prediction Model in Semiconductor Manufacturing Process , 2016, ITCS 2016.

[17]  Liu Yan,et al.  Credit Card Fraud Detection using Deep Learning based on Auto-Encoder and Restricted Boltzmann Machine , 2018 .

[18]  Peter Beling,et al.  Deep learning detecting fraud in credit card transactions , 2018, 2018 Systems and Information Engineering Design Symposium (SIEDS).

[19]  Xu Chen,et al.  Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce , 2016, Decis. Support Syst..

[20]  Selvani Deepthi Kavila.  Machine Learning For Credit Card Fraud Detection System , 2018 .