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Reengineering Financial Processes with IDP & RPA for Enhanced Automation

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Company: Financial Services Co. | Location: US Technology: IDP, RPA, AI & ML

Project Background

A leading US-based financial services enterprise embarked on a rapid digital transformation, aiming to harness AI and ML capabilities to improve customer satisfaction, enhance workflow efficiency, and achieve error-free process automation. To meet these goals, their technology stack required re-engineering to create a scalable, intuitive system that would reduce processing time by 50-60% and ensure over 95% customer satisfaction.

Challenge

  • The current cloud PaaS architecture lacked scalability, limiting the potential of AI and ML for full-scale process automation.
  • The data architecture required critical re-engineering to ensure data integrity across key financial processes, including credit card processing, KYC, accounts management, and CRM.
  • Frequent changes in government regulations demanded extensive updates across the ecosystem, leading to significant system downtime.
  • The customer experience needed improvement by reducing transaction times and providing more efficient grievance redressal.

Perennial’s Implementation

  • Focused on selecting and fine-tuning the most accurate algorithms within Azure ML services to achieve near-perfect anomaly detection.
  • Utilized Azure Monitor Services to enable real-time data analysis of streaming data, flagging outliers before applying Azure ML algorithms.
  • Trained Azure ML algorithms using historical data to reach nearly 100% efficiency in detecting fraud.
  • Implemented Azure Power Automate and Stream Analytics for real-time cross-functional data analytics, ensuring timely detection of anomalies.
  • Deployed Azure Cognitive Services, incorporating Vision and Speech APIs for biometric verification.
  • Updated the cloud architecture to fully support AI and ML capabilities for early detection of fraud, missing data, and calculation errors during claims processing.

Key Highlights

  • Re-engineered the existing data architecture to integrate Azure Databricks, deploying relevant AI and ML algorithms for efficient data processing.
  • Utilized Azure Blob Storage to accelerate data loading and processing, alongside Azure Data Warehouse Analytics and Databricks for enhanced analytics.
  • Deployed a Fast API framework for the rapid development of Intelligent Document Processing (IDP) using Python microservices.
  • Modernized customer and internal user applications using React Native for the front end and Node.js for the back end.
  • Leveraged Robotic Process Automation (RPA) with Azure UI Path to automate workflows powered by IDP.
  • Optimized the Azure architecture for scalability, security compliance, speed, and cost-effectiveness of Azure services.
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