Financial Fraud Management by Cluster Reply BHBR

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This solution aims to offer an Azure solution for banks to prevent of Money Laundering and Financing of Terrorism, following circular number 4001.

The Azure Financial Fraud solution uses Azure Data Factory to bring different data sources, facilitating data ingestion, after the data is imported into the Data Lake, there are 3 layers of data processing (Bronze, Silver and Gold), in which the customer can apply the rules of BACEN regulation 4001, to separate suspicious transactions of money laundering or financing of terrorism. The Compliance, Governance and Risk areas have this need, which IT should forward to the security or data area. (Public: Manager, Coordinator or Compliance or Governance Analyst, CIO, data architects, data scientists, data analysts). The solution is totally flexible for the client to adjust the BACEN rules and also create their own rules to select the transactions.

CHARACTERISTICS:

  • This Azure solution automatically ingests data using Azure Data Factory, processes and analyses financial data against reset financial rules using ML.
  • Generates multiple reports using Power BI required to show evidence.
  • Comply with Central Bank requests by reducing the risk of exposure and fines.
  • Having the flexibility to create your own rules to analyze behaviors through banking transactions.
  • Connect with Azure Open AI Services, where you will get predictive results.
https://store-images.s-microsoft.com/image/apps.42861.29c1bc1e-0419-40ab-9df5-207601b936a5.e983fe25-4b92-427b-93a4-8ca325ea1ecc.55ca0107-8046-45a1-b34c-7c9b377fd32f
https://store-images.s-microsoft.com/image/apps.42861.29c1bc1e-0419-40ab-9df5-207601b936a5.e983fe25-4b92-427b-93a4-8ca325ea1ecc.55ca0107-8046-45a1-b34c-7c9b377fd32f
https://store-images.s-microsoft.com/image/apps.49688.29c1bc1e-0419-40ab-9df5-207601b936a5.e983fe25-4b92-427b-93a4-8ca325ea1ecc.a5c49fbc-1d66-438b-806c-2bddcee638d7
https://store-images.s-microsoft.com/image/apps.29032.29c1bc1e-0419-40ab-9df5-207601b936a5.e983fe25-4b92-427b-93a4-8ca325ea1ecc.5321c536-01c2-406e-995b-bff135a8c61a