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Automated Investment Data Processing and Integration Platform
  1. case
  2. Automated Investment Data Processing and Integration Platform

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Automated Investment Data Processing and Integration Platform

knubisoft.com
Financial services
Information technology

Challenge in Manual Investment Data Processing

Manual data entry processes for investment forms introduced human errors, compromised data quality, and required extensive throughput error correction. Integration challenges existed between existing systems (Novus and Watson) and new data processing workflows.

About the Client

Fintech innovation unit building smart financial products and driving technology-led cultural change

Objectives for Automated Data Processing Solution

  • Eliminate human error in investment data entry
  • Reduce throughput processing errors by 90%
  • Implement seamless system integration between Novus, Watson, and new workflows
  • Digitize and structure investment form data using OCR technology
  • Create scalable API infrastructure for future system additions

Core System Functionalities

  • Image upload and batch processing capability
  • Optical Character Recognition (OCR) with zone-specific text extraction
  • Text cleaning and paragraph grouping algorithm
  • REST API for system integration
  • Orchestrator service for cross-system data traffic management
  • Data validation rules engine for quality assurance

Technology Stack Requirements

Python
.NET
MySQL
Tornado
App Engine

System Integration Needs

  • Novus
  • Watson
  • Existing financial systems

Operational Requirements

  • High-availability architecture for continuous processing
  • Data encryption and financial compliance standards
  • Scalable infrastructure for handling peak document volumes
  • Real-time error detection and logging capabilities

Expected Business Impact of Automated Data Processing

Implementation of automated OCR processing and intelligent system orchestration will reduce manual data entry errors by 95%, decrease processing time by 70%, and ensure consistent data quality across integrated financial systems. The API-first approach enables future scalability while maintaining regulatory compliance and operational efficiency.

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