fraud detection via machine learning
Moreover, fraud detection via machine learning method was performed through experiments involving clustering processes. The primary clustering processes implemented were Density-Based Algorithms, XMeans, FarthestFirst, Simple, and EM. The research also accommodates other classification algorithms, such as RandomForest and SVM. Based on the clustering algorithms, the best fraud detection model was the EM algorithm that recorded 0.99862. Another useful fraud detection model under clustering algorithms was the DensityBased algorithm that recorded an average of 0.98788 on fraud detection.
In a nutshell, detecting fraud was more accurate through machine learning-based classification algorithms than through artificial neural networks. Therefore, when dealing with numerous financial transactions, it is essential to use machine learning algorithms to detect fraud. The attackers may also use other measures that would make it difficult to detect fraud. Moreover, most of the attackers succeed in committing fraud because the transactions are completed within seconds. Machine learning algorithms can detect fraudulent transactions as soon as they occur, regardless of the amount of data under consideration.
Discussion
The results have demonstrated the role of machine learning methods and artificial intelligence techniques in fraud detection. In that regard, it is essential to acknowledge that these methods have been used in real-life situations to detect fraud. The table below shows some of the methods used in various areas to detect fraud across the globe.