| Title | Fraud Detection using Machine Learning and Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Raghavan, Pradheepan, Gayar, Neamat El |
| Conference Name | 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) |
| Date Published | dec |
| Keywords | area under the ROC curve, Artificial neural networks, Australian dataset, autoencoder, belief networks, benchmark multiple machine learning methods, Boltzmann machines, composability, convolutional neural nets, convolutional neural networks, cost of failure, credit card, Credit cards, data mining, data mining techniques, Data models, Deep belief networks, Deep Learning, deep learning methods, EU, Europe, European dataset, financial data processing, Forestry, fraud, fraud detection, German dataset, k nearest neighbor, learning (artificial intelligence), machine learning, Matthews correlation coefficient, pattern classification, pubcrawl, Random Forest, Resiliency, restricted boltzmann machine, security checks, support vector machine, Support vector machines |
| Abstract | Frauds are known to be dynamic and have no patterns, hence they are not easy to identify. Fraudsters use recent technological advancements to their advantage. They somehow bypass security checks, leading to the loss of millions of dollars. Analyzing and detecting unusual activities using data mining techniques is one way of tracing fraudulent transactions. transactions. This paper aims to benchmark multiple machine learning methods such as k-nearest neighbor (KNN), random forest and support vector machines (SVM), while the deep learning methods such as autoencoders, convolutional neural networks (CNN), restricted boltzmann machine (RBM) and deep belief networks (DBN). The datasets which will be used are the European (EU) Australian and German dataset. The Area Under the ROC Curve (AUC), Matthews Correlation Coefficient (MCC) and Cost of failure are the 3-evaluation metrics that would be used. |
| DOI | 10.1109/ICCIKE47802.2019.9004231 |
| Citation Key | raghavan_fraud_2019 |
Fraud Detection using Machine Learning and Deep Learning