International Research Journal of Education and Technology

In today’s world, high dependency on internet technology has enjoyed increased credit card transactions, but credit card fraud has also accelerated as an online and offline transaction. Financial fraud is a growing concern with far-reaching consequences for the government, corporate organizations, and the finance industry. The implementation of fraudulent detection in banking datasets using deep learning algorithms. It was developed by the Python Jupyter software. Initially, the input dataset is initiated by a preprocessing technique. The process is handled by data cleaning, which helps clean the datasets and, additionally, handles the missing values. Under pre-processing, the process started with data visualization process. Next, the data splitting process handles the data set and divides it for the purpose of the regression process. Deep learning algorithms are used in this work. The deep learning technique that handles the MLP algorithm is to predict fraudulent transactions and normal transactions. The final step is predicting whether the output of identification for fraudulent transactions is achieved in the model evaluation process.

Keywords- Credit card transactions, Finance industry, Fraudulent detection, Preprocessing technique, Python Jupyter software, Data cleaning, Data visualization process, Data splitting process, MLP algorithm.