AI-Driven Reconciliation Agents for Financial Accuracy and Compliance in Cloud-Native Data Pipelines

Abstract

Financial reconciliation is a critical process for ensuring accuracy, transparency, and compliance in modern financial systems. Traditional reconciliation approaches, heavily reliant on manual oversight and rule-based automation, are increasingly inadequate for the high volume, velocity, and complexity of financial data in cloud-native environments. Artificial intelligence (AI) has emerged as a powerful tool to automate anomaly detection, streamline reconciliation workflows, and support regulatory compliance. This review synthesizes the state-of-the-art in AI-driven reconciliation, with a focus on cloud-native data pipelines. We discuss important techniques such as autoencoders, adversarial models, continual learning, federated learning, and large language models (LLMs). Experimental results prove that state-of-the-art neural techniques far surpass the accuracy of conventional methods. We also address open issues with interpretability, scalability, and compliance, and outline future directions including explainable AI, blockchain integration, federated continual learning, and generative AI applications. This article seeks to offer researchers and practitioners an in-depth summary of the promise and limitations of AI in financial reconciliation, and to outline directions for the future generation of smart, reliable, and compliant reconciliation systems.