Accelerating Financial Innovation with Governed AI
Fast-growing fintech organisation, UAE
An Azure AI Foundry adoption framework gave a UAE fintech a production-oriented AI environment with enterprise-grade security, governance and cost control.
- Isolated development, testing and production AI environments
- SeparatedIsolated development, testing and production AI environments
- Every model release scored for accuracy, bias and data-leakage risk
- EvaluatedEvery model release scored for accuracy, bias and data-leakage risk
The challenge
What was going wrong
A rapidly growing fintech organisation in the UAE required an enterprise platform for developing, evaluating and deploying generative AI solutions securely — accelerating AI innovation across customer service, operations, compliance and internal productivity, while addressing financial-data confidentiality, model governance, regulatory expectations, data residency, cost governance and the separation between experimentation and live services.
Our approach
What we built
- 01
Designed a secure AI landing zone with isolated development, testing and production environments, private connectivity, managed identity, role-based access, secrets management, network restrictions, central logging and cost controls.
- 02
Established a governed process for the model and application lifecycle: use-case intake, model selection, prompt design, testing, evaluation, risk approval, deployment, monitoring, optimisation and retirement.
- 03
Enabled priority fintech use cases including customer-service assistance, product and policy discovery, compliance-document analysis, transaction-exception summarisation and internal knowledge assistance.
- 04
Implemented responsible AI and evaluation principles: accuracy testing, hallucination assessment, harmful-content detection, data-leakage prevention, bias review, human oversight, source validation and audit logging.
The outcome
What changed
- Faster AI solution development and better separation between innovation and production.
- Improved governance of models and prompts, and stronger security around financial data.
- Greater visibility of AI performance and usage, and reduced risk from uncontrolled AI experimentation.
- Reusable foundations for future AI applications and improved collaboration between business, security, compliance and engineering.
