Financial Services GenAI Modernization and Governance Assessment on AWS

Financial services institutions are accelerating the adoption of generative AI across high-impact domains such as credit risk assessment, fraud detection, customer service, and regulatory reporting. However, many of these initiatives are being developed in isolation, without consistent governance frameworks or alignment with strict regulatory requirements. This creates significant exposure to compliance risks, lack of explainability, model drift, and limited auditability—especially in regulated environments where transparency, traceability, and data control are mandatory. Compass UOL helps financial institutions assess and modernize their GenAI landscape on AWS by identifying fragmentation, evaluating governance gaps, and defining a scalable and compliant architecture. This assessment provides a structured approach to deploying GenAI with appropriate controls for model usage, data access, monitoring, and regulatory alignment. By leveraging AWS-native services such as Amazon Bedrock, combined with data platforms and security capabilities, Compass UOL defines how to safely scale GenAI across financial workflows—ensuring controlled adoption, reducing regulatory risk, and improving operational efficiency. Customers leave with a clear roadmap to deploy GenAI at scale, with governance, auditability, and compliance embedded from the ground up.

Buyer Problem / Business Trigger

GenAI initiatives in credit, fraud, or customer service without governance and auditability Increasing regulatory pressure around AI explainability, model transparency, and data privacy Risk of non-compliance due to fragmented AI architectures and data usage Inefficiencies in operations due to manual or inconsistent AI adoption

Delivery Model

Discovery of GenAI use cases across financial workflows (risk, fraud, service, compliance) Assessment of data architecture, AI models, and governance maturity Definition of AWS-native GenAI architecture with governance controls Roadmap for compliant GenAI deployment and scaling

Assessment / Engagement Scope

Evaluation of GenAI use cases (credit scoring, fraud detection, customer service automation, reporting) Assessment of data environments (customer data, transactional data, risk models) Review of governance frameworks (model risk management, explainability, auditability) Identification of compliance gaps and regulatory risks Design of AWS-native architecture (Bedrock, data platforms, security and monitoring layers) Prioritization of use cases aligned to risk reduction and business impact

Expected Output / Deliverables

GenAI modernization and governance assessment report AWS reference architecture for financial services GenAI workloads Governance framework (model controls, explainability, auditability, compliance) Risk assessment with prioritized remediation actions Implementation roadmap for scaling compliant GenAI

Customer Decision Questions This offer helps the customer answer:

How can we deploy GenAI safely in regulated financial environments? What governance controls are required for AI explainability and compliance? Which AWS architecture supports scalable and auditable GenAI workloads?