AI-Driven Predictive Analytics: Combining Machine Learning and Big Data for Real-Time Fraud Detection

Abstract

The technology of predictive analytics enabled by AI combines the use of machine learning algorithms along with high data processing capacities to predict the outcome based not only on historical information but also on the current one. In our case, the focus is on the application of the aforementioned AI-based predictive analytics technique to identify fraud on credit cards. The problem with traditional statistical forecasting techniques is that they lack the ability to recognize complex patterns, be adaptable to changing situations, and learn to improve their results. On the other hand, our methodology is dependent on incorporating some basic procedures that are part of the data science field including input preprocessing, feature extraction, and exploratory analysis along with different models that include regression, decision trees, ensembles and neural networks. Using the predictive analytics as well as the big data technology, it is possible to handle large volumes of data both structured and unstructured that have been collected from diverse sources. like sensor data, financial operations, social media data, and user activity data. Some of the innovations in this field revolve around efficient scalability, graceful handling of data drift, and the embedding of prediction algorithms right inside the enterprise. In light of the dependence of contemporary organizations on data-based strategies, AI-assisted predictive analysis becomes a necessary technique to ensure a competitive advantage. The current study connects artificial intelligence with data science and thus bears both theoretical and practical significance.