Predictive Analytics for Fraud Detection of Realtime Financial Data by Using Machine Learning Techniques

Financial fraud affects tremendously both the financial industry and everyday life. Fraud can reduce confidence in the industry, destabilize savings and affect the cost of living. Financial institutions use a variety of fraud prevention models to address this problem. This report presents an approach that utilizes Machine Learning techniques to build a model that detects fraudulent transactions and flags them. The approach utilizes a dataset that contains a collection of observation points on transactions and which can be useful in understanding the nature of transactions. The high accuracy results of the models are indicative of their readiness to use in a real-world setting. This performance means that the likelihood of a fraudulent case passing through is quite low. This paper, seeks to carry out comparative analysis of financial fraud detection techniques, like machine-learning techniques, who plays an important role in fraud detection, as it is often applied to extract and uncover the hidden truths behind very large quantities of data. This makes organizations adapt to high-level security and data handling technology solutions like machine learning, deep learning and predictive analytics which are efficient enough to deal with highly sensitive data, predict frauds and unwanted behavioural patterns in this data. This paper reviews the different advance technologies commonly used to deal with this type of data forms a comparison among them and suggests the most efficient and informative method to use in this sector. Through the end of the review, feature engineering and its selection of parameters for achieving better performance are discussed.