Harnessing big data pipelines and GenAI for financial risk prediction: A cloud-centric data engineering approach

Big Data pipelines and Generative Artificial Intelligence (GenAI) have enabled new approaches to financial risk prediction. This paper deals with the Cloud-centric data engineering framework, where massive Big Data technologies are merged with GenAI to allow a more accurate, faster, and dependable financial risk assessment. The proposed concept utilizes distributed computing paradigms to acquire, process, and analyze high-velocity financial data sourced from multiple environments, including transactional datasets, market feeds, and social sentiment data. Due to the usage of GenAI within this framework, this system can detect complex patterns, simulate various stress scenarios, and provide insightful early warnings, which the conventional models did not highlight. The discussion also involves Cloud-centric designs to guarantee proper elasticity and fault tolerance with seamless integration into the modern DevOps toolchains.
In this case, the outcome is capable of reactive analytics and adaptive model deployment on a massive scale. The contributions are highlighted by the development of dynamic preprocessing, feature, and model selection steps for Big Data engineering and GenAI on the Apache Spark, Kafka, and Kubernetes frameworks. The validation process is associated with the experimental demonstration of the superior early warning signal detection and loss avoidance rate. The resulting system might be viewed as a novel approach that merges the capabilities of Big Data engineering and GenAI in the Cloud setup to form a practical step for proactiveness and data-drivenness in the given field, which is particularly important with the current complexity and velocity of financial data.