Financial Services Data-to-Intelligence Assessment on AWS

Financial institutions face increasing pressure to turn large volumes of data into auditable, timely, and actionable decisions across risk, compliance, fraud prevention, and customer intelligence. However, fragmented data environments, legacy architectures, and inconsistent governance models delay critical initiatives and increase operational costs.

The Financial Services Data-to-Intelligence Assessment on AWS by Compass UOL provides a structured approach to evaluate the current data landscape, identify technical and regulatory gaps, and define a prioritized roadmap for modernization on AWS. This assessment evaluates data pipelines, governance frameworks, security controls, and analytical workloads, aligning them with AWS best practices for scalable and regulated environments. It also identifies opportunities to adopt modern architectures such as data lakes and lakehouses using services like Amazon S3, AWS Glue, Amazon Redshift, and Amazon Kinesis.

Additionally, the assessment explores readiness for advanced analytics and AI/GenAI use cases using Amazon Bedrock, ensuring that financial data can be used securely and in alignment with compliance requirements. The outcome is a clear, prioritized roadmap that reduces regulatory risk, improves operational efficiency, and enables faster, data-driven decision-making. Buyer Problem / Business Trigger

Data initiatives delayed due to legacy systems or fragmented data sources Regulatory pressure (e.g., BCBS 239, IFRS, local compliance) with limited data governance maturity High operational costs in inefficient or duplicated data pipelines Difficulty operationalizing analytics and AI in production environments Need to monetize data or improve customer intelligence capabilities

Delivery Model

Current state discovery across data, architecture, and governance Stakeholder workshops (risk, compliance, IT, data teams) AWS-aligned technical and regulatory assessment Roadmap definition with prioritized initiatives and target architecture

Assessment / Engagement Scope

Inventory of data sources, pipelines, and analytical workloads Data governance, quality, and catalog maturity assessment Evaluation of current architecture against AWS best practices Identification of priority use cases (fraud detection, credit analytics, customer insights) Security, privacy, and regulatory compliance review AI/GenAI readiness assessment for regulated environments

Expected Output / Deliverables

Data-to-Intelligence maturity assessment report Target AWS architecture (high-level and recommended patterns) Prioritized roadmap (quick wins vs. strategic initiatives) Business impact estimation (cost, risk reduction, operational efficiency)

Customer Decision Questions This offer helps the customer answer:

Does our current data architecture support near real-time and auditable decision-making? Which data initiatives deliver the fastest business impact with controlled risk? How can we align data governance with regulatory requirements on AWS? Are we ready to safely adopt AI/GenAI using our internal data?