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Case Study

Client is a large asset management company based out of North America.

Client Background

Client is a large asset management company based out of North America.

Client Need

SF App development for the asset management products: Annuity Conversion Environment (ACE) and Required Minimum Distribution (RMD)
  • Migrate and implement the ACE & RMD applications from their current on-premise legacy MS-Dynamics 365 environment to Salesforce Financial cloud with optimal workload distribution using Azure public cloud and MuleSoft for integration with other Client applications.

Our Solution

Architected and implemented of SF core development and eco-system. This included
  • SF UI and UX development
  • Workflow implementations
  • Event driven application integrations to ecosystem application components
  • Azure Cloud for DWH, Integration with SF and BI reports
Data Validation and Data Migration from CRM to SF
Features & Capabilities that we built:
  • User enrolment and onboarding
  • Security/Access Management
  • Opportunity Management Process
  • Activities, PEG (Portfolio Evaluation Group) and Work notes objects
  • Annuity Policy Management
  • Fee Credit Management
  • Offer Letters Integration Workflows
  • Alerts and Notifications, Dashboards
  • Azure Cloud: Data Ingestion and synchronization with SF database
  • Mulesoft integrations with legacy systems: IRIS, FIN Reporting and PEG tracker

Tools & Technologies

Salesforce Cloud, MuleSoft, PowerBI, Azure SQL MI, Azure Synapse Analytics, Azure Data Factory

Key Benefits

Two applications ACE and RMD merged into salesforce.
  • Increase user productivity
  • Enhance Management capabilities
  • Integration with Various Data sources
Refined workflow
  • Better and integrated approach view of ACE and RMD operations
  • Timely availability of data for ACE and RMD(Integration Automation)
  • More healthy cooperation between CSA and executives
Enhanced Analytics
  • Comes with new Dashboards and reports for daily business
  • The new automated and enhanced process methods (decrease data issues, which decrease data correction efforts.)
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