Fraud Detection in Banking Transactions Using Machine Learning

In terms of loss of money and reputation, insecurity in banking systems has exposed us to dishonest behavior that seriously compromises clients as well as the bank. Annual financial loss from financial fraud in banks is estimated to be rather large. Early identification of this reduces fraud by means of a counter-strategies development and recovery from such losses. This work proposes a machine learning-based method to effectively contribute to fraud detection. The artificial intelligence (AI) based model will accelerate check verification to counter the counterfeits and reduce damage. In this work, we investigated several intelligent algorithms based on a public dataset in order to identify the correlation of some factors with fraudulence. Resampling the used dataset helps to reduce the high class of instability in it and analyzes the data using the suggested method for maximum accuracy.