ADVANCING ANOMALY AND FRAUD DETECTION IN BIG DATA WITH ARTIFICIAL INTELLIGENCE

Authors: Mohammed Kashif, Abdul Rahman Jibran Syed & Mubashir Ali Ahmed

ABSTRACT

The digital transformation across industries has generated unprecedented volumes of Big Data, creating opportunities for innovation while increasing vulnerability to anomalies and fraud. Traditional detection methods lack the scalability and accuracy required for such complex, high-dimensional data streams. This paper explores the role of AI—leveraging machine learning, deep learning, reinforcement learning, and hybrid approaches—in overcoming these limitations. Case studies from finance, healthcare, cybersecurity, e-commerce, and telecommunications demonstrate the effectiveness of AI models, including autoencoders, isolation forests, and neural networks, in detecting sophisticated fraud patterns. Challenges such as data imbalance, real-time processing, interpretability, and ethical concerns like privacy and bias are also addressed. The paper highlights future directions in explainable AI, federated learning, and edge-based systems to support transparent, scalable, and privacy-aware anomaly detection in Big Data environments.

Keywords: artificial intelligence (AI), fraud detection, anomaly detection, big data analytics, machine learning, deep learning, real-time monitoring, data privacy, explainable AI (XAI), imbalanced data, cybersecurity, financial fraud, federated learning, edge AI, and pattern recognition.

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