ARCHIVES

In the modern financial landscape, fraudulent activities have become increasingly sophisticated, exploiting complex relationships across accounts, transactions, and entities. Traditional fraud detection systems often analyze transactions in isolation, overlooking the contextual and relational aspects that are crucial to identifying hidden fraud rings, collusive networks, and money-laundering patterns. To address these gaps, this project proposes an advanced Fraud Detection Framework powered by LLaMA2 NLP models and Neo4j graph databases. The system utilizes LLaMA2’s natural language processing capabilities to analyze unstructured financial data, suspicious transaction narratives, and customer communications, extracting meaningful patterns and semantic cues. Coupled with Neo4j’s graph database, the solution models relationships between accounts, merchants, devices, and geolocations, enabling contextual fraud analysis through graph algorithms and relationship queries. This hybrid approach enhances detection accuracy, reduces false negatives, and provides explainable fraud insights.Traditional fraud detection systems often rely on isolated transaction monitoring and rigid rule-based models, which fail to account for the intricate nature of modern financial crimes. The need for advanced, adaptive, and contextual fraud detection systems has become more pressing than ever.