Global Bank Transforms with AI-Powered Analytics
Legacy data systems preventing real-time insights and regulatory compliance
- faster fraud detection
- 0%faster fraud detection
- reduction in false positives
- 0%reduction in false positives
- annual savings
- $0Mannual savings
01 — The brief
What was wrong
Legacy data systems preventing real-time insights and regulatory compliance
What we did about it
Implemented Azure Synapse Analytics with AI-powered fraud detection
- Sector
- Finance
- Client
- Fortune 500 Financial Institution
- Phases
- 4
- Duration
- 8 months
02 — The build
How We Built It
STEP 01 / 03
Azure Synapse Analytics Implementation
We deployed Azure Synapse Analytics as the foundation for real-time data processing and analytics.
Built with
Azure Synapse- RTReal-time Processing
- DLData Lake
03 — The sequence
Implementation Timeline
8 months, in 4 phases
- 01
M1-2
Discovery & Planning
Assessment of existing systems, data architecture design, and stakeholder alignment
- 02
M3-5
Platform Development
Azure Synapse setup, data migration, and ML model development
- 03
M6-7
Testing & Validation
Model training, accuracy testing, and compliance validation
- 04
M8
Deployment & Training
Production rollout and team training across all regions
Cloud Platform
Microsoft Azure, Azure Synapse Analytics, Azure Data Lake
AI & ML
Azure Machine Learning, Python, TensorFlow, Scikit-learn
Microsoft Azure- Azure Synapse Analytics
- Azure Data Lake
- Azure Machine Learning
Python
TensorFlow- Scikit-learn
Technology We Used
What this build actually ran on, grouped the way we scoped it.
Power BI- Azure Stream Analytics
- EHEvent Hubs
Amazon AWS
Google Cloud
Azure OpenAI
Databricks
Analytics
Power BI, Azure Stream Analytics, Event Hubs
In their words
KCloudAI's AI-powered fraud detection solution has transformed our operations. We're now detecting fraud in real-time with unprecedented accuracy, saving millions while improving customer experience.
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