Anomaly Detection in Financial Transactions: A Hybrid AI and Big Data Analytics Approach
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P101Keywords:
Anomaly Detection, Financial Transactions, Hybrid AI, Big Data Analytics, Machine Learning, Fraud Detection, Real-time Monitoring, Predictive Modeling, Cybersecurity, Data MiningAbstract
Anomaly detection in financial transactions is a critical task for ensuring the security and integrity of financial systems. With the increasing volume and complexity of financial data, traditional methods are often insufficient to handle the challenges posed by sophisticated fraud and irregularities. This paper presents a hybrid approach that integrates advanced artificial intelligence (AI) techniques with big data analytics to enhance the accuracy and efficiency of anomaly detection in financial transactions. The proposed method leverages machine learning algorithms, deep learning models, and big data processing frameworks to identify and flag suspicious activities in real-time. We evaluate the performance of our hybrid approach using a large-scale dataset of financial transactions and compare it with existing methods. The results demonstrate significant improvements in detection accuracy and computational efficiency, highlighting the potential of the hybrid approach for practical deployment in financial institutions
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