1 MINS READ
Industry
Capital Markets
Unlocking Exponential Future Growth in Capital Market Firms
Solution
TCS BaNCS™
TCS BaNCS™: Enabling Transformation in Banks and Financial Institutions
Highlights
- This white paper presents a strategic blueprint for establishing autonomous market infrastructure – the systems that are fundamentally designed for self-resilience and event-aware scalability.
- We build upon two foundational principles: (i) Business event-aware auto-scaling and (ii) Autonomous self-healing. These ensure system reliability, fault tolerance, and performance optimization, even under extreme load or failure scenarios.
- This approach lays the groundwork for a new class of intelligent infrastructure that is predictive, autonomous, and self-optimizing. It puts forth the assurance that clearing, settlement, and risk processes remain uninterrupted and trustworthy, even amidst unprecedented market conditions.
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Adapting to volatility
With the rapid growth of real-time trading, digital asset exchanges, and cross-border settlements, financial institutions face unprecedented demands and are challenged to deliver continuous availability, adaptive scalability, and robust resilience, all while managing the highest standards of security and regulatory compliance.
Traditional infrastructures, which relied on static capacity planning and manual interventions, are now evolving into self-managed ecosystems. Modern cloud-native deployments already leverage containerization, sophisticated monitoring, and automated scaling. The next leap, however, is toward continuously assured business-aware elasticity where infrastructure dynamically adapts to market shifts, regulatory changes, and system risks.
Some of the key advancements that are shaping the future are:
- Autonomous resilience: Infrastructure extends beyond basic failover, employing intelligence mechanisms to autonomously detect, isolate, recover and protect from disruptions across infrastructure and application layers.
- Business event-aware elasticity: Scaling decisions transition from purely technical metrics (such as CPU/memory triggers) to being driven by business-driven triggers (such as trading volume spikes and risk thresholds), ensuring greater precision and operational efficiency.
- Unified observability and predictive adaptation: Systems evolve by recognizing stress patterns and adjusting proactively, shifting from reactive recovery to anticipatory resilience with the metrics derived from the Observability platform.
- Holistic resilience engineering: Adapting practices, such as chaos testing, automated disaster recovery, and adaptive trust frameworks, fosters resilience by design, rather than as an afterthought.
By embracing autonomous resilience and event-aware elasticity, market infrastructure Institutions enable platforms that are self-sustaining and self-evolving. This strategic posture enables the continuous delivery of critical services, reduces systemic risks, and ensures instantaneous adaptation to market volatility. This is expected to usher in an era of intelligent, adaptive, and future-ready market infrastructures.
Autonomous systems
Understanding the progressive evolution of scaling and resilience strategies is essential to addressing the increasing complexity and demands of modern market infrastructures.
Business event-aware auto scaling:
Over time, scaling and resilience strategies have evolved from a traditional, infrastructure-based, and static capacity models to dynamic, business-event aware approaches that adapt to real-time market conditions and demands.
- Static era: Fixed infrastructure provisioning with basic automated failover mechanisms.
- Reactive era: Metrics-driven orchestration for auto-scaling triggered by scaling decisions from CPU and memory. While more dynamic, this approach may lag rapid surges or lead to unnecessary scaling actions.
- Proactive era: Scaling through workload patterns and health monitoring, enabling more timely and efficient resource allocation.
- Autonomous era: Scaling decisions are driven by business events, apart from workload patterns, with continuous assurance and adaptive resilience ensuring infrastructure responsiveness to real-time conditions.
Figure 1: The evolution of scalability
Autonomous resilience:
The evolution of self-healing systems has progressed from reactive fault detection to intelligent, autonomous healing where systems anticipate and address failures without the need for operator intervention.
- Reactive monitoring: Manual detection of faults through logs and alerts, with recovery dependent on operator intervention and root cause analysis done mostly post-incident.
- Automated recovery: Utilization of scripts and watchdogs for restarting failed components, basic health checks at infra and app levels, and still failure-driven rather than performance-driven.
- Predictive resilience: Continuous observation of system health, latency, and throughput, enabling early detection of anomalies before failures and automated remediation workflows triggered proactively before failures occur.
- Intelligent and autonomous healing: System anticipates failure patterns from historical performance baselines and healing decisions driven by policy and real-time system state. Root cause isolation and system balancing without the need for operator intervention.
Figure 2: The evolution of resilience
Twin strategy convergence:
While the business event-aware self-scaling ensures capacity always matches demand dynamically (not only by infra signals but also by business signals and performance thresholds), autonomous resilience allows the platform to recover and stabilize without human intervention. The latter facilitates making decisions based on policy, historical performance data, and identified failure patterns. These two streams converge, and the result is a self-governing, elastic market infrastructure that:
- Heals itself before failure propagates
- Scales in anticipation of the demand aligned with business demands and uncertainties
- Reduces operational overhead while improving reliability
- Enhances trust and competitiveness in the financial market ecosystems
The twin strategy solution
To design an autonomous, resilient infrastructure for mission-critical market systems, we build upon two foundational principles – (i) Business event-aware auto-scaling and (ii) Autonomous self-healing. These principles ensure system reliability, fault tolerance, and performance optimization, even under extreme load or failure scenarios.
Below is an in-depth detailing of these principles and their practical implementation using modern cloud-native tools and platforms.
Maintaining operational resilience while ensuring high-performance and low-latency processing is paramount in market infrastructures such as Exchanges, Clearing Houses, and Central Securities Depositories, This challenge lies in addressing system failures and predicting workload surges and mitigating performance degradation before they affect business continuity. Traditional autoscaling approaches that rely solely on CPU or memory thresholds are inadequate for this class of workloads. A more advanced architecture is required to interpret business events, predict their impact on workloads, and orchestrate scaling accordingly.
At the foundation of this architecture is a business event orchestrator. This component ingests event streams from business processes, such as surges in trade volumes, trade settlements, and corporate actions, and uses historical patterns combined with predictive models to anticipate workload surges. Unlike infrastructure-driven scaling, which reacts after a configured parameter reaching the threshold (such as CPU utilization and memory utilization) , the orchestrator proactively signals scaling needs to ensure the system is ready before demand peaks based on the identification of dynamic workload.
The orchestrator integrates with KEDA (Kubernetes Event-Driven Autoscaling), which translates both predictive signals from the orchestrator and real-time metrics from observability tools (such as Prometheus, Splunk, Dynatrace etc.) into concrete scaling actions. KEDA extends Kubernetes with the ability to auto-scale based on the event sources and external metrics, making it particularly suited for during trade risk management and real-time clearing workloads where activity is tied to market events rather than static utilization thresholds.
These workloads, hosted in Kubernetes clusters, demand both elasticity and deterministic performance. Ensuring they operate reliably requires an integrated observability layer. The observability and telemetry stack, leveraging technologies such as Prometheus, OpenTelemetry, Splunk, or Dynatrace, continuously monitors system health, latency, throughput, and error rates.
To complement observability, predictive analytics and healing engines apply rules to the telemetry data. This engine identifies early indicators such as latency creep, resource starvation, or transaction bottlenecks that can impact to performance or cause failures/disruptions in processing. Crucially, this extends self-healing beyond failure detection to include performance degradations. The system can intervene more proactively by detecting slowness and other sub-threshold anomalies.
Figure 3: The architecture of autonomous market infrastructure systems
Corrective actions
Once anomalies are detected, the remediation executor enforces corrective actions autonomously. This may include restarting unhealthy pods, rerouting flows, applying circuit breakers, provisioning additional resources/pods, or rolling back faulty deployments.
Depending on operational requirements and regulatory guidelines, the remediation executor can operate in both closed-loop automated mode and semi-automated advisory mode. Over time, this layer learns from repeated issues, enabling autonomous self-healing that evolves with the system.
Autonomous self-healing in this context is more than reactive recovery. It embodies predictive healing, where the system acts not only when something is broken down but also when trends indicate an impending performance degradation. For example, if latency grows steadily beyond a learned baseline, the healing engine may provision additional capacity, optimize resource allocation, or pre-emptively rotate pods to prevent cascading slowdowns. This approach ensures that self-healing covers the entire spectrum – from outright failures to subtle degradations - thereby enabling continuous and uninterrupted processing.
Chaos engineering will be applied systematically to validate this capability – deliberately injecting controlled faults, additional latency, resource exhaustion, and dependency failures – into both the scaling pipeline and the self-healing loop. The system will be measured on two fronts:
- Predictive scaling validation
- Generate synthetic business events simulating risk, trade settlement, and clearing surges
- Validate prediction of scale out prior to backlog or latency breach
- Measure scaling latency reduction vs purely reactive scaling
- Autonomous self-healing validation
- Inject CPU/memory pressure, process crashes, and degradation (slowness, not just failure)
- Confirm that the healing engine identifies degradation
- Validate automatic remediation (restart, reroute, failover)
Embedding chaos engineering as a validation framework makes resilience measurable and continuously improvable.
Our twin strategy, validated through chaos engineering, ensures that critical market infrastructures are prepared for future workloads and resilient against unforeseen disruptions.
Building for tomorrow
The convergence of predictive business event-aware auto scaling with autonomous self-healing is a pivotal advancement to engineer and operate critical market infrastructures.
Predictive, business agent-aware auto-scaling facilitates proactive scaling in response to increased business transaction processing demand. The self-healing frameworks extend resilience beyond failure detection, addressing performance degradation such as slowness, resource contention, or degraded throughput before they escalate to systemic risks.
By rigorously validating both through chaos engineering, we lay the foundation for a future where critical market infrastructures operate with anticipatory intelligence and continuous resilience.
This approach lays the groundwork for a new class of intelligent market infrastructures that are predictive, autonomous, and self-optimizing. For market infrastructure Institutions, it means the assurance that clearing, settlement, and risk processes remain uninterrupted and trustworthy, even amidst unprecedented market conditions. For the broader industry, it signals a paradigm shift where resilience is no longer an afterthought but an embedded, continuously evolving characteristic.
In essence, the twin strategy safeguards present-day performance and prepares the ecosystem to adapt, evolve, and thrive amidst tomorrow's uncertainties and to evolve with intelligence and thrive with them.
Citations:
- KEDA – Kubernetes Event-Driven Autoscaler
https://keda.sh/docs/ - Kubernetes Self-Healing (Probes)
https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle/#container-probes