Fraud Classification In Financial Statements Using Machine Learning Techniques

With the blooming of technology, the financial sector has shown various number of improvements and has provided multiple e-financial services. Frauds can occur easily and quickly in data that lack safety levels. Although there are many laws that reduces the outcome of hacking, but unfortunately these laws cannot keep up with the enormous increase of hacking attacks level that are present. In this research, Bayes point machine, Random Forest, Cat Boost, Decision tree and Isolation Forest algorithms were applied on the credit card dataset, and it was found that Random Forest algorithm was outperforming well comparing to other algorithms.