Mukhyamantri Ladli Behna Yojana Beneficiary Data Extraction Platform
A smart beneficiary data platform designed to automate verification and reduce fraud in welfare schemes. Empowering government agencies with reliable data extraction and 5x faster compliance reporting.
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Services
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Data Engineering & Automation
Data Validation & Verification
Audit & Traceability Solutions
Large-Scale Data Processing
Performance Optimization
Reporting & AnalyticsClient
Introduction
Mukhyamantri Ladli Behna Yojana is one of Madhya Pradesh's flagship welfare programs designed to provide financial assistance to eligible women and improve their economic independence and overall well-being. The objective of this project was to automate the extraction, validation, and reporting of beneficiary data from the official Ladli Behna Yojana portal.
Processed 26+ lakh beneficiary records, enabling accurate and efficient large-scale verification and analysis.
Challenges
Technical and operational challenges that needed to be solved to create a scalable, reliable, and accurate beneficiary data extraction system.
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Beneficiary data was distributed across multiple administrative layers, including districts, blocks, Gram Panchayats, villages, and wards.
Data needed to be collected across multiple eligibility categories and scheme phases.
Large-scale extraction required high performance while maintaining accuracy and data consistency.
Verification and traceability were critical because the information was linked to a government welfare program.
Manual collection and validation of such large datasets would have been extremely time-consuming and resource-intensive.
Stakeholders required structured reporting and consolidated insights across all covered regions.
Solutions
reverseBits built a scalable data extraction, validation, and reporting platform for large-scale welfare scheme beneficiary data.
Automated data extraction
Developed a fully automated extraction system to collect beneficiary data across 30 administrative blocks and associated regions.
Workflow automation
Implemented automated processing workflows for eligible and non-eligible applicants across multiple scheme phases.
High-performance processing
Leveraged multiprocessing techniques to accelerate data extraction and efficiently handle large-scale datasets.
Data verification
Built automated validation mechanisms to verify extracted records and improve overall data accuracy.
Auditability & traceability
Added comprehensive logging and audit capabilities to support traceability, monitoring, and issue investigation.
Reporting & insights
Generated structured reports with block-level summaries, village-wise statistics, extraction metrics, and consolidated beneficiary insights.
Impact & Benefits
The solution successfully processed 26 lakh+ beneficiary records across multiple administrative regions. Automated extraction, validation, and verification improved data accuracy while significantly reducing manual effort. Detailed reporting and audit capabilities enhanced transparency and traceability across the workflow. This enabled faster, more reliable decision-making through organized and verified beneficiary data.
Technology stack
Built with Python-based automation to support reliable data extraction, validation, reporting, and large-scale beneficiary record processing.
Python
PostgreSQL
AWS
Kafka
CeleryPythonPostgreSQLAWSKafkaCeleryNext steps
Planned roadmap initiatives to grow and scale the platform further.
Program expansion
Extend the platform to support additional government welfare schemes and beneficiary management programs.
Beneficiary intelligence
Introduce automated trend analysis and deeper beneficiary insights to support data-driven policy decisions.
Real-time monitoring
Add centralized dashboards with real-time reporting and performance monitoring capabilities.
Compliance & audit reporting
Enhance audit trails, compliance tracking, and reporting mechanisms to improve governance and accountability.
