Cloud-Native Data Intelligence Architecture for AI-Driven Derivatives, Accounting, and Financial Risk Management
DOI:
https://doi.org/10.64137/31079377/IJMSD-V1I2P106Keywords:
Cloud-Native Data Engineering, Financial Analytics Platforms, AI-Enabled Derivatives Processing, Fintech Data Architectures, Scalable Data Orchestration, Risk Reporting Systems, Service Mesh Architectures, Data Lineage Governance, AI-Assisted Financial Operations, Elastic Analytics PlatformsAbstract
Cloud-native data engineering aims to support an elastic data platform for business analytics, machine learning, or artificial intelligence while meeting non-functional requirements such as performance, reliability, security, compliance, and cost. An objective, evidence-based approach guides financial application development, focusing on an AI-enabled derivatives lifecycle or closed-loop process, driven by separate Request for Information and Request for Proposal stages. Although not constrained by business or product type, the research emphasizes AI-assisted processes such as derivatives pricing or risk assessment, accounting validation, and financial P&L or risk reporting, supported by an adaptable cloud-native data architecture. The orchestration of fintech applications indicates a data-centric view supported by scalable cloud-native data architectures and design patterns. System requirements for scalable orchestration identify constraints formalizing the stage-gate pipeline model into a data-analytics outsourcing model. The data-analytics outsourcing model aligns cloud-native data-analytics engineering with central governance and federated execution, clarifying data ingestion and lineage. Further, the outline encourages a service-mesh pattern for fintech applications, further detailed through an AI-enabled derivatives, accounting, and risk lifecycle description.
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