Big Data Analytics in Fraud Detection: Machine Learning Applications in the Finance Sector

Jai Kiran Reddy Burugulla

Abstract: The use of online banking has become ubiquitous in today's world. However, as a consequence of online banking, the threat of fraud is slowly becoming a persistent problem. A lot of reports have been filed against credit card frauds in recent years. As a result, it is important to detect these frauds in real-time. In this project, we will use machine learning to detect credit card fraud. Banking has become much more popular with the increase in mobile wallets. As a consequence of this popularity, fraud in these online transactions has also become more common. For the banking industry as well as the customers, this has become a major problem. Parallely, banks are trying to build software and systems to detect these frauds. But new kinds of fraud are starting to appear as fast as traditional fraud is being detected. They need a machine-learning-based system to detect these frauds. The complexity of the systems should not be too high otherwise it becomes impossible to run it on the server, as there are millions/billions of transactions. It is also impossible to create manual rules as it is not possible to keep up with the new emerging transaction flow patterns of the frauds. Time has come for the banking industry to switch to machine learning-based systems for credit card online transaction fraud detection. In this project, we will explore various algorithms of machine learning-based systems. Selecting the right algorithms is therefore of paramount importance to develop a reliable, efficient, and effective credit card fraud detection system. We will explore different datasets. A long list of algorithms will be explored to select the best-performing one. The data will be analyzed, and visualizations will also be produced. Various scoring and evaluation metrics will also be implemented to assess how well the model is performing.

Keywords: Big Data Analytics, Fraud Detection, Machine Learning, Financial Fraud, Predictive Analytics, Anomaly Detection, Real-Time Monitoring, Artificial Intelligence (AI), Transaction Analysis, Risk Management, Data Mining, Behavioral Analytics, Supervised Learning, Unsupervised Learning, Cybersecurity in Finance