Sarcouncil Journal of Multidisciplinary

Abstract: GraphQL is increasingly used as an API layer for AI platforms because its typed schema and client-driven field selection can support efficient access to complex, interconnected data. In financial applications, these benefits must be balanced against strict requirements for low tail-latency, high throughput during burst loads, and reliable governance for auditability and risk controls. Prior research indicates that GraphQL performance and security depend strongly on workload characteristics and implementation choices, motivating optimization beyond default deployments. The proposed study models finance-oriented query patterns (feature retrieval, risk explanation, batch scoring) and evaluates a layered optimization stack that combines gateway guardrails (query cost/depth controls), resolver batching and caching, planner-guided execution, and telemetry-driven throttling and autoscaling. Benchmark-style results highlight that guardrails reduce tail risk and errors, while the full optimization stack delivers the largest gains for nested queries by limiting fan-out amplification and stabilizing p95/p99 latency. Overall, the work frames GraphQL optimization for financial AI as an integrated problem spanning API engineering, microservice observability, and AI operational integrity.