A Survey of Cloud-Enabled Machine Learning in Financial Services

Abstract

The integration of cloud computing and machine learning (ML) is reshaping financial services by enabling scalable, real-time, data-driven decision-making. This survey reviews cloud-enabled machine learning (ML) developments from 2021 to 2025, with a focus on financial sector applications. Cloud services provide elastic computing, distributed training, and low-latency analytics, which are essential for processing large-scale financial datasets. Financial institutions increasingly leverage these capabilities to enhance credit scoring, fraud detection, risk forecasting, and customer personalization. Advances in deep learning, ensemble methods, federated learning, automated ML, and explainable AI (XAI) are improving model accuracy, operational transparency, and regulatory compliance. MLOps pipelines further streamline the deployment, monitoring, and lifecycle management of ML models in dynamic environments. Key challenges persist, including data privacy, integrating legacy systems, regulatory constraints, and latency-sensitive operations. This survey categorizes the literature into four themes. (i) Cloud architecture and service models tailored for ML workflows, (ii) supervised and unsupervised ML techniques applied in finance, (iii) comparative analysis of real-world use cases, and (iv) performance evaluation metrics and trade-offs. Unlike prior reviews, this review uniquely synthesizes recent trends across cloud-native ML technologies and maps their practical implications in regulated financial environments. Future directions include exploring edge-cloud coordination for time-critical tasks, building robust models against adversarial data, implementing privacy-preserving mechanisms, and establishing standardized governance frameworks. This survey serves as a comprehensive reference for researchers and practitioners seeking to leverage cloud-based machine learning (ML) in financial services.