Secure AI- and LLM-Powered Cloud Platforms for Financial Fraud Analytics Using ETL Pipelines in Web Application Development
DOI:
https://doi.org/10.15662/IJEETR.2024.0603004Keywords:
financial fraud analytics, cloud computing, AI models, large language models (LLMs), ETL pipelines, web application development, secure platformsAbstract
In the modern digital economy, financial institutions face ever-increasing threats from sophisticated fraud attempts that exploit vulnerabilities in transactional systems. This research explores the design and implementation of secure cloud-based platforms that integrate artificial intelligence (AI) and large language models (LLMs) with Extract-Transform-Load (ETL) pipelines for advanced financial fraud analytics within web application ecosystems. Leveraging scalable cloud infrastructure and state-of-the-art machine learning models, this approach enables real-time detection, classification, and prediction of fraudulent activities. The proposed framework emphasizes end-to-end security, data integrity, and compliance with regulatory standards while ensuring responsiveness and interpretability of results. We discuss the system architecture, data processing workflows, model training strategies, and integration of ETL processes for seamless ingestion and transformation of heterogeneous financial data. Our evaluation demonstrates significant improvements in detection accuracy, reduced false positives, and efficient processing of high-velocity transactional streams. The study highlights advantages such as scalability, adaptability to evolving fraud patterns, and enhanced decision support, while also addressing challenges including model explainability and security trade-offs. Findings from this research offer actionable insights for developers, analysts, and decision-makers seeking to enhance fraud analytics capabilities in web-based financial platforms.
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