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Bank Of New York Mellon Engineering

To power financial success through innovative technology solutions that enable secure, efficient, and scalable financial services globally

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Align the strategy

Bank Of New York Mellon Engineering SWOT Analysis

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To power financial success through innovative technology solutions that enable secure, efficient, and scalable financial services globally

Strengths

  • INFRASTRUCTURE: Robust enterprise-grade financial infrastructure
  • EXPERTISE: Deep domain knowledge in financial services technology
  • SECURITY: Industry-leading cybersecurity capabilities
  • SCALE: Massive data processing capabilities handling trillions daily
  • COMPLIANCE: Strong regulatory compliance frameworks and expertise

Weaknesses

  • LEGACY: Technical debt from aging core banking systems
  • INTEGRATION: Siloed systems limiting data flow between platforms
  • TALENT: Challenges attracting top engineering talent vs fintechs
  • AGILITY: Slower deployment cycles compared to digital natives
  • INNOVATION: Conservative risk culture slowing technology adoption

Opportunities

  • API: Open banking APIs to create new revenue streams and products
  • BLOCKCHAIN: Distributed ledger technology for settlement efficiency
  • DATA: Advanced analytics to derive insights from vast data assets
  • CLOUD: Cloud migration to increase scalability and reduce costs
  • PARTNERSHIPS: Strategic fintech partnerships to accelerate innovation

Threats

  • FINTECH: Disruptive fintech competitors with modern tech stacks
  • TALENT: War for top engineering talent with big tech and startups
  • REGULATORY: Increasing technology compliance requirements
  • SECURITY: Sophisticated cyber threats targeting financial data
  • MODERNIZATION: Pressure to rapidly modernize core systems

Key Priorities

  • MODERNIZATION: Accelerate core banking platform modernization
  • API: Develop comprehensive API ecosystem for client integration
  • TALENT: Revamp tech talent strategy to attract top engineers
  • CLOUD: Execute cloud-first strategy across all new initiatives
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Align the plan

Bank Of New York Mellon Engineering OKR Plan

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To power financial success through innovative technology solutions that enable secure, efficient, and scalable financial services globally

MODERNIZE CORE

Transform our technology foundation for the digital age

  • LEGACY: Decommission 25% of identified legacy systems with complete data migration by Q4
  • CLOUD: Migrate 60% of eligible workloads to cloud platforms with 99.99% reliability
  • ARCHITECTURE: Implement microservices architecture for 4 critical service domains
  • PERFORMANCE: Reduce transaction processing latency by 40% through system optimizations
API ECOSYSTEM

Build world-class financial services API platform

  • COVERAGE: Launch 15 new API products covering 80% of core banking functions
  • ADOPTION: Achieve 100K daily API calls with 150 active enterprise clients
  • DEVELOPER: Create comprehensive developer portal with sandbox environments for all APIs
  • REVENUE: Generate $15M in new revenue from API-based product offerings
TALENT MAGNET

Become the preferred employer for top tech talent

  • HIRING: Reduce engineering position time-to-fill from 90 to 45 days
  • RETENTION: Improve technical talent retention rate from 85% to 92%
  • DEVELOPMENT: 100% of engineering staff complete 40+ hours of technical upskilling
  • DIVERSITY: Increase representation of underrepresented groups in tech roles by 15%
AI LEADERSHIP

Lead financial services with transformative AI

  • PLATFORM: Build unified AI/ML platform supporting all business units with 99.9% uptime
  • AUTOMATION: Implement AI automation for 35 high-volume operational processes
  • INSIGHTS: Deploy predictive analytics dashboards for 85% of client-facing teams
  • GOVERNANCE: Establish comprehensive AI ethics framework with 100% project compliance
METRICS
  • Digital platform adoption rate: 75%
  • System modernization index: 65/100
  • Engineering talent retention: 92%
VALUES
  • Client Focus
  • Integrity
  • Excellence
  • Innovation
  • Collaboration
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Align the learnings

Bank Of New York Mellon Engineering Retrospective

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To power financial success through innovative technology solutions that enable secure, efficient, and scalable financial services globally

What Went Well

  • REVENUE: Fee revenue increased 5% YoY driven by new service adoption
  • EFFICIENCY: Technology expense ratio improved by 180 basis points
  • DIGITAL: Digital platform transactions up 25% compared to prior year
  • SECURITY: Zero major security incidents reported during the quarter
  • CLOUD: Successfully migrated 35% of workloads to cloud environments

Not So Well

  • INTEGRATION: Post-acquisition system integration delays impacted ROI
  • TALENT: Engineering attrition rate increased to 15% from 12% YoY
  • PROJECTS: Three major tech initiatives faced 2+ month delivery delays
  • ADOPTION: Client adoption of new API services below target by 20%
  • LEGACY: Technical debt reduction targets missed by 30% this quarter

Learnings

  • AGILE: Need for improved Agile delivery methodologies enterprise-wide
  • TALENT: Remote-first policies critical for engineering talent retention
  • LEADERSHIP: Tech leadership needs closer integration with business units
  • ARCHITECTURE: Microservices approach showing better adaptability results
  • CULTURE: Innovation culture requires more executive sponsorship focus

Action Items

  • MODERNIZATION: Accelerate legacy system retirement by 15% next quarter
  • TALENT: Implement enhanced engineering career path and retention program
  • CLOUD: Increase cloud migration velocity by 25% through tooling investment
  • INNOVATION: Launch technology innovation lab with dedicated funding stream
  • METRICS: Develop comprehensive engineering efficiency dashboard by EOQ
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Drive AI transformation

Bank Of New York Mellon Engineering AI Strategy SWOT Analysis

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To power financial success through innovative technology solutions that enable secure, efficient, and scalable financial services globally

Strengths

  • DATA: Massive financial data assets for AI model training
  • INFRASTRUCTURE: Established data processing infrastructure
  • GOVERNANCE: Strong risk and compliance frameworks for AI
  • INVESTMENT: Substantial technology investment capacity
  • PARTNERSHIPS: Strategic vendor relationships with AI leaders

Weaknesses

  • ADOPTION: Conservative AI adoption compared to competitors
  • TALENT: Limited AI/ML specialized engineering talent
  • INTEGRATION: Challenges integrating AI into legacy systems
  • INNOVATION: Slower AI innovation cycle than digital natives
  • DATA: Data quality issues in disparate legacy systems

Opportunities

  • AUTOMATION: Process automation to reduce operational costs
  • INSIGHTS: AI-powered analytics for client portfolio insights
  • COMPLIANCE: Advanced AI for fraud detection and compliance
  • PERSONALIZATION: Tailored client experiences through AI
  • EFFICIENCY: Real-time transaction monitoring and processing

Threats

  • COMPETITION: AI-native fintechs gaining market share
  • TALENT: Fierce competition for AI engineering expertise
  • EXPLAINABILITY: Regulatory pressure for AI transparency
  • ETHICS: Reputational risks from AI bias or misuse
  • SECURITY: AI-powered cyber threats targeting financial data

Key Priorities

  • TALENT: Build specialized AI engineering center of excellence
  • PLATFORM: Develop unified AI/ML platform for enterprise use
  • GOVERNANCE: Establish robust AI ethics and governance framework
  • AUTOMATION: Prioritize AI for high-impact operational processes