The Age of Financial Frauds and using Random Forest Machine Learning to Predict Fraudulent Transactions

With the digitalization of financial processes and the rapid growth of the fintech industry, there are large volumes of data, sensitive personal information and monetary exchanges in billions, resulting in a rapid growth of cases of financial fraud. The involvement of machine learning in analyzing customer data, extraction of information and the detection of patterns leads to more efficient and accurate predictions and results, allowing businesses and analysts to make decisions and take security measures accordingly. This research study focuses on using the Random Forest Machine Learning algorithm to detect and predict fraudulent transactions using a real-world dataset. The paper discusses stepwise approaches to process the data, train the model and then use the model to make predictions. The accuracy of prediction was found to be 99.94% with this algorithm; key aspects about the accuracy and run time of the algorithm have also been highlighted. This paper reinforces how implementing machine learning models in real-world transactional platforms would act as a safety net for consumers and businesses to prevent cyber security breaches.