Comparing ML Algorithms on Financial Fraud Detection
Chung Min Tae, Phan Duy Hung · 2019
The problem of Financial Fraud has reached an alarming scale nowadays. Losses due to the fraud are reaching billions of dollars every year. To reduce it, decision systems that use efficient fraud detection algorithms should be invented. With the support of modern technologies, these systems are able to manage to analyze the information and to create a prediction feature model. However, the invention of these systems is not a trivial matter but a quite challenging task due to the huge amount of different and unbalanced data. Moreover, it is not clear which machine learning algorithm should be implemented. Therefore, our research is conducted to answer the question: which is the most suitable algorithm for the dataset in this research, especially when dealing with the large amount of uncleaned data.