Apache Kafka for Low Latency AI-Enhanced Data Analytics for Fraud Detection System

It is important to focus on developing a Fraud Detection System that is highly demanded by global industries, especially e-commerce, banking, financial services, and telecommunications. Monitoring, analyzing, identifying, and detecting fraudulent activities has recently become increasingly important to prevent financial data loss and protect user data and trust. The above-said functions are obtained by implementing Apache Kafka (AK) in conjunction with artificial intelligence (AI) and machine learning algorithms. It is easy to analyze the data in any distributed system with low latency to detect fraudulent activities using AI-powered detection models. Apache Kafka can provide high performance in data analytics using data pipelines, seamless ingestion, distribution, processing, and more data transmission in real-time applications while enabling AI models to process streams of data for anomaly detection. This paper proposed a Fraud Detection Architecture (FDS) using AK integrated with AI techniques to handle large volumes of data and analyze and detect anomaly actions. It also demonstrated how AK integrates Kafka streaming, Apache Spark and Kafka Flink to enable advanced AI-driven data analytics processes and large-scale pattern recognition through deep learning models. The proposed FDS architecture and the flowchart of the entire workflow are explained to help understand real-world data analysis using AK with AI-enhanced capabilities for high efficiency in fraud detection and prevention. The simulation output shows that using AK with AI-based models in real-time data analysis for fraud detection improves accuracy and speed (low latency) with scalability.