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Home > Client Success > Financial Institution Unlocks AI Value with Trustworthy AI Strategy

The Client

The client is a Fortune 500 global financial institution offering consumer, corporate, and investment banking services. They operate on a multi-cloud ecosystem involving AWS, Microsoft Azure, Google Cloud, Salesforce, SAP, and ServiceNow with thousands of employees across geographies handling sensitive financial data.

The Problem

The client struggled to derive the true value of AI with diverse data formats across a multi-cloud environment. The lack of a unified AI-ready data architecture and reliable outputs posed serious roadblocks in safely deploying enterprise AI at scale.

Disparate Cloud Systems: The client’s existing data was spread across AWS, Microsoft, Google (and others), limiting AI to work with conflicting data formats and data flows.

Data Governance Risks: The client’s legacy systems lacked proper data governance, risking inaccurate predictions, and non-compliance with global financial regulations.

Expensive Data Ecosystem: Despite bearing the added expense of maintaining a cumbersome data architecture, the client had to deal with duplicate datasets and lacked a single source of truth.

Lack of Trust in AI Outputs: The leadership team lost confidence in AI due to inaccurate predictions not grounded in fact and counterproductive investments in AI that does not deliver expected results.

problems

Siloed data across AWS, Azure, Google Cloud, SAP, Salesforce, and ServiceNow

Lack of AI governance leading to poor regulatory compliance

Limited reliance on AI outcomes due to poor data quality

High maintenance costs of fragmented cloud systems

The Solution

Damco, in partnership with Cloud Lighthouse, designed a multi-cloud data strategy for the bank using their Trustworthy AI framework. This helped the client make wise AI investments in creating a simplified and safe data ecosystem built for AI—laying the foundation for scalable, governed, and responsible enterprise-grade AI adoption.

AI Strategy & Governance Roadmap Development

  • Damco’s team got buy-ins from 40+ stakeholders across technical and business functions to gather requirements on specific integrations and data flows and align on a unified AI vision.
  • Damco’s Microsoft MVP team and the bank’s stakeholders developed a step-by-step AI strategy roadmap focused on data centralization, trust, explainability, and compliance.
  • Our data experts formulated a secure, governed framework to ensure AI outputs meet the standards of international financial regulations.

Data & Application Modernization

  • Damco architected an “AI Landing Zone” by integrating Microsoft Fabric with OneLake to consolidate enterprise data (client data, financial services, and transactions) from multiple cloud systems.
  • Our data experts enabled a single source of truth by minimizing data duplication and enforcing strong access controls across their cloud platforms.

Regulatory Compliance & Governance Implementation

  • Our team engineered guardrails for AI output validation, making sure that all results are fact-based and aligned with compliance mandates (like SOX, PCI-DSS, and GDPR).
  • Damco also introduced data explainability models to prevent AI hallucinations and bolster leadership confidence in AI decision-making.

IT Audit & Infrastructure Optimization

  • Damco streamlined the client’s multi-cloud architecture, reducing operational data complexity and cutting data management costs.
  • Our team established composable frameworks and reusable patterns for repeatable AI development across departments and regions.
  • We also empowered their internal teams with AI literacy training programs to sustain long-term scalability and practice responsible AI adoption.

The Benefits

The client successfully transformed their fragmented data ecosystem into a trustworthy AI environment—safe, secure, and scalable. With a composable and explainable AI framework in place, the client is confidently utilizing data from multiple clouds for driving real value from AI with measurable ROI and reduced risk.

  • A strong data foundation built across a multi-cloud environment
  • Significant cost savings in data management and integration
  • Trustworthy AI outputs with explainability across financial use cases
  • Easy AI adoption with significant boost in confidence for new AI investments
  • AI-driven decision support across compliance, transactions, client services, and risk
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