Enhancing Data Engineering Pipelines for Financial Services with Retrieval-Augmented Generation (RAG) And Transformer-based ML Techniques Generative Adversarial Networks (Gans)

Abstract

Financial services increasingly rely on advanced data engineering pipelines to process and analyze vast amounts of data. This study proposes a novel approach to enhance these pipelines by integrating Retrieval-Augmented Generation (RAG), transformer-based machine learning techniques, and Generative Adversarial Networks (GANs). RAG systems enable context-aware information retrieval and efficient data synthesis, making them suitable for automating financial document analysis and customer interactions. Transformers excel at modeling complex, nonlinear financial time series data and learning long-range dependencies, which is crucial for accurate predictions. GANs address data quality issues such as scarcity, imbalance, and privacy by generating synthetic financial data, leading to improved machine learning model performance in applications like fraud detection and risk assessment. The proposed multi-layered architecture aims to improve data quality, analysis efficiency, predictive accuracy, and scalability while ensuring regulatory compliance. By combining these techniques, the study seeks to develop intelligent and flexible financial systems that enhance decision-making and streamline operations. The proposed approach has the potential to revolutionize financial data engineering by leveraging the strengths of RAG, transformers, and GANs to create a holistic model that can adapt to the ever-growing scale and complexity of financial data.