Modernizing Banking for an AI Future

Artificial intelligence (AI)-based financial institutions are the wave of the future, providing the most intelligent value propositions delivered through compelling journeys and experiences. But creating such an institution requires a robust development strategy as AI-fist models can place tremendous demand on core technologies. To successfully move toward AI-first models, financial institutions would do well to learn from the experience of those already carrying out such transformations. Here are some key insights to keep in mind:

  • Keep the strategy tech forward
    • Consider the factory model to build at scale, especially in fast-evolving and critical areas of transformation
    • Consider insourcing differentiating capabilities, as determined by the eventual outcome(s) desired
  • Use modern APIs and streaming structures
    • Maintain rigorous documentation on integrations to ensure the transformation keeps moving forward
    • Identify an anchor stack but experiment with others to support faster change while still exploring alternative approaches that may provide more benefits
  • Ensure core processors and systems are up to the task
    • Maintain an automation-first and fast-release posture to keep evolving and meeting market demands
    • Consider a modern core for high-velocity areas to enable efficient rollouts of new capabilities while also enabling a modular build of financial products
  • Employ a data management approach for the AI world
    • Adopt a value-centric approach to building data platforms to take advantage of the fact that data and analytics platforms evolve over time
    • Set up a lab and factory for analytics to quickly move through test-and-learn cycles
  • Make sure your infrastructure is also intelligent
    • Define an enterprise cloud strategy
    • Established end-to-end visibility across the technology and infrastructure stack to monitor stack performance and quickly diagnose and resolve issues
  • Have appropriate cybersecurity measures
    • Identify the right perimeter design to safeguard against potential malicious attacks
    • Design robust data-categorization and data-security safeguards to avoid critical customer-data combinations and comply with national data-protection and data-residency laws



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