About the Client
The client is a prominent financial services provider in the banking sector, facing challenges with fragmented data sources, limited analytics capabilities, and the absence of AI/ML-driven fraud detection.
The Challenge
The client’s existing system lacked a centralized data platform, causing fragmented data across mainframe systems, RDBMS, and Dynamics 365. This led to difficulties in providing top-notch banking services, inefficient fraud detection, and a lack of advanced analytics.
The Solution
TechWish implemented a two-phase solution using Microsoft Azure for data consolidation, analytics, and real-time fraud detection:
- Data Consolidation: Used Azure Data Factory for orchestration and Azure Databricks for advanced processing, consolidating data from mainframe, RDBMS, and Dynamics 365 into Azure Data Lake and Synapse. Enabled customer segmentation, predictive analytics, and loan default prediction. Cosmos DB provided scalable API access.
- Real-Time Fraud Detection: Streamed transaction data from mainframe via Azure Event Hubs, analyzed for fraud using Azure Stream Analytics and Machine Learning. Results were routed through Azure Service Bus, Logic Apps, and IBM Message Queue, ensuring real-time fraud decisioning.
The Result
- Data Consolidation: Unified data from various sources, providing a single view of client operations.
- Enhanced Banking Services: Improved customer satisfaction by 50% due to personalized services enabled by customer segmentation.
- Accurate Predictions: Enhanced loan default prediction accuracy by 25%.
- Real-Time Fraud Detection: 90% reduction in false positives and 95% detection of fraudulent transactions.
- Scalability: Azure solutions enabled scalable applications for future growth.
