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Stonex Group Engineering

To build and maintain a world-class technology platform that empowers our clients to navigate global financial markets with confidence and precision

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To build and maintain a world-class technology platform that empowers our clients to navigate global financial markets with confidence and precision

Strengths

  • INFRASTRUCTURE: Robust financial technology backbone supporting $894B in client trades
  • SCALE: Global technology presence in 40+ countries with multi-asset capabilities
  • SECURITY: Industry-leading security protocols with zero major breaches reported
  • INTEGRATION: Seamless API ecosystem connecting 10+ major financial platforms
  • TEAM: Highly skilled engineering workforce with 92% retention rate

Weaknesses

  • LEGACY: Aging backend systems requiring significant modernization investment
  • AUTOMATION: Manual processes still present in 23% of core operations workflows
  • TALENT: Limited specialized engineering talent in emerging fintech disciplines
  • TECH-DEBT: Growing backlog of technical debt impacting delivery velocity
  • DATA: Fragmented data architecture limiting cross-platform analytics potential

Opportunities

  • CLOUD: Full cloud migration could reduce infrastructure costs by 28%
  • MICROSERVICES: Transition to microservices architecture for greater agility
  • DEVOPS: Enhanced CI/CD practices to reduce deployment cycle times by 40%
  • ANALYTICS: Advanced data platform to unlock predictive client insights
  • MOBILE: Expanding mobile trading capabilities to capture growing user segment

Threats

  • COMPETITION: Fintech startups deploying more agile technology solutions
  • REGULATION: Evolving compliance requirements demanding rapid tech adaptation
  • CYBER: Increasingly sophisticated financial cyber threats targeting platforms
  • TALENT-WAR: Intensifying competition for specialized engineering talent
  • TECH-PACE: Accelerating industry technology change outpacing internal capacity

Key Priorities

  • MODERNIZE: Accelerate legacy system transformation to cloud architecture
  • AUTOMATE: Implement end-to-end process automation across critical workflows
  • SECURITY: Enhance cybersecurity posture with next-gen threat protection
  • TALENT: Develop specialized engineering capability in strategic tech areas
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To build and maintain a world-class technology platform that empowers our clients to navigate global financial markets with confidence and precision

MODERNIZE

Transform our core technology for cloud-native future

  • ARCHITECTURE: Complete design of cloud-native architecture for core trading platform by EOQ with 100% exec approval
  • MIGRATION: Successfully migrate 30% of legacy application workloads to cloud infrastructure with zero downtime
  • PERFORMANCE: Achieve 40% improvement in system latency metrics across high-volume trading operations
  • MICROSERVICES: Decompose 5 critical monolithic components into microservices with 99.99% reliability
AUTOMATE

Eliminate manual processes through intelligent automation

  • DEVOPS: Implement CI/CD pipelines across 85% of development workflows reducing deployment time by 60%
  • AI-OPS: Deploy predictive monitoring across 100% of critical systems to identify issues before impact
  • TESTING: Automate 70% of regression testing scenarios achieving 40% reduction in QA cycle time
  • WORKFLOWS: Identify and automate 20 key operational workflows reducing manual effort by 65%
SECURE

Fortify our digital fortress against emerging threats

  • ASSESSMENT: Complete comprehensive security assessment of all systems with remediation of 100% critical issues
  • DETECTION: Implement next-gen threat detection reducing mean time to detection from 6 hours to 10 minutes
  • COMPLIANCE: Achieve 100% compliance with latest financial regulatory security requirements across all regions
  • TRAINING: Ensure 95% of engineering staff complete advanced security training with 90% pass rate
INNOVATE

Lead the market with transformative AI capabilities

  • PLATFORM: Launch unified enterprise AI platform supporting at least 5 cross-functional use cases
  • MODELS: Develop 3 proprietary trading AI models showing 20%+ performance improvement over baselines
  • ANALYTICS: Deploy predictive client analytics engine increasing engagement by 30% for pilot group
  • PRODUCTIVITY: Implement GenAI developer tools boosting engineering productivity by 25% across organization
METRICS
  • RELIABILITY: 99.999% platform uptime across all critical trading systems
  • PERFORMANCE: 75% reduction in 99th percentile latency during peak trading periods
  • VELOCITY: Double software delivery frequency while maintaining quality metrics
VALUES
  • Client-First Engineering
  • Technology Excellence
  • Security & Compliance
  • Continuous Innovation
  • Global Scalability
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Align the learnings

Stonex Group Engineering Retrospective

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To build and maintain a world-class technology platform that empowers our clients to navigate global financial markets with confidence and precision

What Went Well

  • PLATFORM: Core trading platform handled record transaction volume with 99.97% uptime
  • SECURITY: Zero critical security incidents despite 317% increase in threat attempts
  • DELIVERY: Completed 87% of planned technology initiatives on schedule and budget
  • INTEGRATION: Successfully integrated technology from 2 recent acquisitions on time
  • MOBILE: New mobile trading application achieved 124% of projected adoption targets

Not So Well

  • LATENCY: Peak trading periods experienced 18% higher latency than targeted SLAs
  • TALENT: Engineering attrition rate increased to 12% vs target of <8% in key roles
  • SCALABILITY: Three instances of capacity-related performance degradation noted
  • TECHNICAL-DEBT: Accumulated technical debt delayed 4 strategic feature releases
  • DEPENDENCIES: Third-party vendor outages impacted 2.3% of operating time in Q4

Learnings

  • ARCHITECTURE: Current monolithic design limits scalability during peak demands
  • MONITORING: Proactive monitoring systems missed early warning signs of issues
  • CAPACITY: Existing capacity planning models underestimated growth acceleration
  • TESTING: Load testing procedures inadequate for actual market volatility events
  • DOCUMENTATION: Insufficient knowledge transfer processes impacting team agility

Action Items

  • CLOUD: Accelerate migration of core trading components to cloud infrastructure
  • MICROSERVICES: Decompose monolithic architecture into scalable microservices
  • OBSERVABILITY: Implement comprehensive observability across entire tech stack
  • AUTOMATION: Increase CI/CD automation to reduce deployment risk and frequency
  • REDUNDANCY: Enhance failover systems to eliminate single points of failure
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To build and maintain a world-class technology platform that empowers our clients to navigate global financial markets with confidence and precision

Strengths

  • FOUNDATION: Established data science team with proven ML implementation skills
  • RESOURCES: Substantial proprietary financial data sets for AI training
  • PARTNERSHIPS: Strategic relationships with key AI technology providers
  • ADOPTION: Successfully deployed AI in risk management reducing false alerts 37%
  • INVESTMENT: Committed $45M to AI initiatives over next three years

Weaknesses

  • INTEGRATION: AI systems operate in silos without cohesive enterprise strategy
  • SKILLS: Limited advanced AI engineering talent across technology teams
  • GOVERNANCE: Inconsistent AI governance and quality assurance protocols
  • INFRASTRUCTURE: Current compute infrastructure inadequate for advanced AI
  • CULTURE: Resistance to AI adoption in certain traditional tech departments

Opportunities

  • AUTOMATION: AI-driven process automation could reduce manual tasks by 65%
  • ANALYTICS: Predictive client analytics could increase engagement by 43%
  • INTELLIGENCE: AI-enhanced trading algorithms outperforming traditional by 18%
  • PERSONALIZATION: Custom AI recommendation engines for client solutions
  • EFFICIENCY: GenAI for developer productivity could boost output by 30%

Threats

  • COMPETITION: Fintech rivals deploying more sophisticated AI capabilities
  • REGULATION: Emerging AI compliance requirements creating uncertainty
  • ETHICS: Potential reputational risks from biased AI financial systems
  • COMPLEXITY: Growing AI model complexity challenging explainability
  • DEPENDENCY: Over-reliance on third-party AI platforms limiting differentiation

Key Priorities

  • PLATFORM: Develop unified enterprise AI platform for cross-functional use
  • UPSKILL: Implement comprehensive AI engineering training program
  • GOVERNANCE: Establish robust AI governance framework and standards
  • INFRASTRUCTURE: Scale compute resources for advanced AI workloads