Data Solutions
AI transformation in banking succeeds not through technology alone, but through disciplined frameworks that align trust, compliance, and business value. True data capability is as much cultural and ethical as it is technical.
Data Capability Framework for AI/ML in Banking
Challenge
A major APAC-based universal bank sought to operationalise artificial intelligence (AI) and machine learning (ML) at scale. While the organisation had made significant investments in data and analytics, its fragmented architecture, unclear governance, and limited cross-functional coordination were impeding progress.
The bank required a comprehensive Data Capability Framework to unify data, governance, risk, and delivery practices — ensuring that AI/ML initiatives were trusted, explainable, and value-aligned across the enterprise.
Solution
Harmonic developed a comprehensive AI/ML Data Capability Framework tailored to the banking sector’s regulatory and operational landscape. The framework balanced foundational enablers (data governance, architecture, and risk management) with AI-specific accelerators (ModelOps, use case factories, and decision intelligence integration).
The approach included eight core pillars:
Data Governance & Ethics – Establishing data stewardship, lineage, and responsible AI controls aligned with APRA CPS 230, CPS 234, and Basel IV.
Data Architecture & Platforms – Designing a modular, cloud-native data lakehouse integrating ML feature stores and API-enabled consumption layers.
Data Engineering & Pipelines – Building automated, high-quality ETL and streaming data flows supporting real-time analytics.
AI/ML Infrastructure & ModelOps – Embedding governance, CI/CD, and model monitoring to transition from prototypes to production-grade AI.
Advanced Analytics & Use Case Factory – Creating a centralised use case catalogue and ROI-driven prioritisation model for scalable delivery.
Security, Privacy & Risk Management – Implementing zero-trust principles, PII protection enclaves, and AI risk controls.
Talent & Culture – Launching data literacy programs, agile AI squads, and communities of practice to embed AI fluency.
Business Integration & Decisioning – Embedding AI insights directly into frontline systems with human-in-the-loop decisioning models.
An optional ninth pillar, External Data Ecosystem, expanded the framework to encompass third-party data sourcing, CDR integration, and ecosystem partnerships.
Key Deliverables
End-to-end Data Capability Framework blueprint for AI/ML in banking.
Governance and Risk Model integrating ethical AI and compliance safeguards.
Technology Reference Architecture for scalable data and ModelOps infrastructure.
Use Case Factory Operating Model enabling prioritised, repeatable AI delivery.
Change Enablement Roadmap to drive cultural adoption and skill uplift.
Client Benefits
The bank achieved a holistic and actionable blueprint for AI/ML enablement across business lines, linking data excellence with responsible innovation. Key benefits included:
Clarity and alignment across data, risk, and business domains.
Faster time-to-market for AI/ML initiatives through standardised processes.
Stronger regulatory and ethical guardrails around data use.
Enhanced business confidence and adoption through explainable, transparent AI.
A clear pathway to transition from pilot projects to enterprise-scale decision intelligence.