AI Use Case Lab for Financial Services on AWS

Overview Most financial institutions don't struggle to come up with AI ideas. They struggle to prove which ones are worth funding. Teams sit through workshops, fill whiteboards with use cases, and still can't get sign-off because nobody can show the business value, the risk profile, or the data readiness for any single idea. Adastra, an AI consulting partner for financial services, built the AI Use Case Lab to fix that, bringing business, risk, compliance, data, and technology stakeholders together around a small number of real use cases before anyone commits to a production build.

Approach Adastra brings your business, risk, compliance, data, and technology stakeholders together around a small number of real use cases, builds working prototypes against your actual data, and tests the results with your own people before anyone commits to a production build.

Activities

• Run working sessions with business, risk, compliance, and technology stakeholders to surface and prioritize candidate AI use cases.
• Score each opportunity on business value, feasibility, data readiness, and stakeholder alignment, then select 2 to 3 for prototyping.
• Define success criteria, data requirements, architecture, and delivery guardrails for each selected use case.
• Build conversational AI and knowledge assistant prototypes for client, advisor, and contact center use cases.
• Build intelligent document processing prototypes for onboarding, disclosure review, and other document-heavy workflows.
• Prototype risk, credit, and propensity models using Amazon SageMaker and Amazon Redshift ML, and test them against real data.
• Put prototypes in front of real business users and measure results against the success criteria agreed at the start.
• Review regulatory, privacy, and explainability requirements for each use case, including auditability and human oversight.
• Recommend which use cases should scale, stop, or get re-scoped, and lay out the architecture and roadmap to back it up.

Deliverables

• 2 to 3 validated AI prototypes built against real data and real financial services workflows.
• A prioritization scorecard ranking use cases by business value, feasibility, and data readiness.
• An architecture approach and delivery guardrails for each prototype, with the AWS services used.
• Validation results from real business users, measured against the agreed success criteria.
• A clear recommendation on what to scale, stop, or re-scope.
• A roadmap with the delivery considerations needed to get to production.

Outcomes

• Make scale-or-stop decisions on evidence instead of assumptions.
• Spend less time and money on AI use cases that were never going to work technically or commercially.
• Give business and compliance teams real confidence in AI before it touches production.
• Free up advisor and client-facing time by automating the manual work behind it.
• Cut manual review and operational effort across onboarding, document, and risk workflows.
• Make risk decisions more consistent and easier to explain to a regulator.
• Walk into your next budget conversation with a recommendation you can actually defend.

Adastra: Your AWS Partner for Data, AI, and Cloud Adastra helps financial institutions figure out which AI ideas are actually worth building before they spend the money to find out. We work across banking, insurance, and wealth management, combining financial services experience with deep AWS technical expertise to validate use cases like client support, risk decisioning, and document processing before they go anywhere near production. The goal is simple: know it works before you scale it.