Operational risk and resilience in financial data infrastructure: a practitioner-research study of institutional risk systems and digital-asset benchmarking
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- Data-layer failures can become measurable operational-risk events.
- KRIs should cover freshness, latency, integrity, detection and recovery.
- A 443-day live case achieved 99.957% conservative output continuity.
- Tail telemetry reveals operational risks hidden by average latency.
Financial systems increasingly depend on real-time data infrastructure, whose failures can propagate into risk estimates, benchmark values, trading processes, audit trails and service continuity. This study reframes three recurring engineering problems – latency misalignment, data-normalization debt and input-layer observability gaps – as operational risk mechanisms. A structured practitioner-research design combines institutional experience with a 443-day live digital-asset benchmarking implementation and a separately collected high-resolution telemetry sample. The historical case produced 1912 269 index observations, completed 30 of 30 scheduled rebalancing events and achieved 99.957% conservative index-output continuity. Prospective telemetry comprised 8835 full calculation-cycle measurements and 2750 external-data request measurements; calculation latency had a median of 21.235 ms, a p99 of 92.700 ms and a p99.9 of 164.807 ms, while external-request latency had a median of 99.312 ms and a p99 of 755.528 ms. The paper maps observed incidents and near misses to key risk indicators, controls, tolerance thresholds and recovery mechanisms. The results support bounded, practitioner-informed design propositions rather than claims of universal architectural superiority.
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