Cosmos-Fabric-BI

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  CDC pipeline

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  data insights

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  notebook for uploading data in lakehouse

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  power bi report

Inspiration With the rise of digital banking, fraud detection has become critical for safeguarding financial transactions. This solution leverages Microsoft Fabric, Azure Cosmos DB, and Change Data Capture (CDC) to detect fraudulent activities in real-time, addressing the need for swift responses to anomalies like high-value transactions or unusual login locations.

What it does This real-time fraud detection system continuously monitors transaction data in Azure Cosmos DB, instantly identifying suspicious patterns. It analyzes data streams and detects anomalies by comparing with historical and contextual information, triggering alerts for further investigation.

How we built it We used Azure Cosmos DB as the primary storage for transaction data, enabling the Change Feed to capture updates. Microsoft Fabric manages the ingestion and transformation of this data, using Eventstream and Dataflows (Gen2) or Data Factory Pipelines. With these components, real-time data is processed and displayed on Power BI dashboards, which offer fraud metrics and instant notifications.

Challenges we ran into Integrating Change Data Capture (CDC) for efficient real-time streaming, optimizing data ingestion, and ensuring low-latency analysis were challenging. Building a seamless pipeline from Cosmos DB to Power BI via Microsoft Fabric while maintaining data accuracy and performance required experimentation and tuning.

Accomplishments that we're proud of We’re proud to have successfully implemented a real-time fraud detection system using Microsoft Fabric and Azure Cosmos DB. The dashboard provides actionable insights, helping financial institutions detect fraud and mitigate risks efficiently.

What we learned We gained hands-on experience with Microsoft Fabric's Eventstream, Azure Cosmos DB's Change Feed, and Power BI integration. Implementing CDC and optimizing real-time data pipelines improved our understanding of data processing, anomaly detection, and the capabilities of Microsoft’s data stack.

What's next for Cosmos-Fabric-BI We plan to enhance the system's detection accuracy by incorporating machine learning models for advanced fraud pattern analysis. Additionally, we aim to expand this solution’s scalability to support more complex datasets and further optimize the dashboard for more granular insights and automated response mechanisms.

Built With

• cosmosdb
• microsoft-fabric
• powerbi