Project Name
Removed 62M Duplicate Welfare Records in 6 Months, Protecting Benefits for 218M Citizens
A national government ministry administering social welfare programmes for over one billion citizens had accumulated 280 million beneficiary records across eight schemes, with an estimated 23% identified as duplicates, ghost entries, or deceased-citizen holdovers. A statutory audit deadline required a verified clean registry before the next benefit disbursement cycle. Ksolves was engaged to design, deploy, and operationalise a full entity resolution and Master Data Management platform from scratch, producing a verified 218 million citizen golden registry within an eleven-month legislative window without interrupting a single legitimate benefit payment.
The client came to Ksolves with six structural problems, each carrying direct citizen welfare consequences if handled incorrectly:
- Massive Scale with Millisecond Citizen Impact: A 0.1% error across 280 million records would wrongly affect 280,000 citizens, making every algorithmic decision a human welfare risk, not just a technical one.
- Multi-Script, Multi-Language Name Chaos: Citizen names existed in Hindi, regional Indic scripts, and Anglicised transliterations, often all three for the same person, rendering standard phonetic blocking algorithms useless without a custom Indic encoding layer.
- No Single Authoritative Identifier Across All Schemes: 31% of records lacked a valid national ID due to pre-biometric enrolment or data entry errors, forcing the pipeline to triangulate identity across multiple weak identifiers rather than a single primary key.
- Deceased and Ghost Records Inflating Headcount: An estimated 9 million deceased citizens remained active beneficiaries because civil registration data had never been cross-referenced against the welfare registry, allowing fraudulent payments to continue unchecked.
- Irreversible Consequence of False Positives: Incorrectly merging two different citizens would strip a living person of their benefit entitlement, requiring a mandatory Human-in-the-Loop review tier and a 48-hour citizen grievance resolution SLA.
- No Existing Data Engineering Infrastructure: The ministry had no data lake, no streaming infrastructure, and no ML capability, requiring Ksolves to design, deploy, and operationalise the entire pipeline, including infrastructure, governance, and caseworker training within eleven months.
Ksolves designed a government-grade deduplication platform on four principles: citizen safety first, transparency by design, speed without sacrifice, and zero-disruption delivery. Delivery ran across four phases from infrastructure build and profiling through standardisation, ML resolution, and human review to go-live across eleven months.
- Indic-Aware Standardisation Pipeline on Apache Spark: Names from six Indic scripts were transliterated to normalised Latin using Unicode CLDR rules with dual phonetic encoding (Soundex plus a custom Indic consonant-cluster encoder). Dates of birth, NID formats, and address fields were normalised across all eight source systems into a clean, schema-unified record.
- Multi-Key Blocking with 99.4% Search-Space Reduction: A four-key blocking strategy using NID prefix, DOB plus district code, phonetic name bucket, and bank account suffix partitioned 280 million records into candidate groups across 120 Spark workers, reducing the comparison space by 99.4% while retaining 99.8% recall of true duplicates.
- Probabilistic ML Matching with Biometric Hard-Confirm: A gradient-boosting ensemble trained on 300,000 labelled citizen pairs with 22 features achieved a precision of 96.8%, recall of 97.2%, and F1 of 0.969, with biometric hash equality against the national fingerprint and iris registry used as a deterministic hard-confirm override.
- Human-in-the-Loop MDM Stewardship with Citizen Grievance Workflow: Approximately 4.2 million below-threshold candidate merges were routed to a caseworker review portal, with active-benefit citizens prioritised. A citizen grievance workflow enforced a government-mandated 48-hour resolution SLA through automated escalation.
- Apache NiFi Ingestion with PII Tokenisation and CRVS Death-Flag Integration: Apache NiFi orchestrated secure ETL from all eight agency systems with SHA-256 PII tokenisation and schema validation at ingestion. Every record was cross-referenced against the Civil Registration and Vital Statistics database to flag and suppress approximately 9 million deceased-citizen records before the ML stage.
Technology Stack
| Category | Technology |
|---|---|
| Processing | Apache Spark |
| Integration | Apache NiFi |
| Streaming | Apache Kafka |
| AI/ML | Machine Learning (Gradient Boosting) |
| Database | Apache Cassandra |
| Platform | Master Data Management |
Within the eleven-month legislative window, the platform delivered five outcomes that directly protected citizen welfare and satisfied statutory compliance requirements:
- 62 Million Duplicate Records Removed: Golden Registry at 218 Million Verified Citizens: The welfare registry was reduced from 280 million raw records to 218 million verified unique beneficiaries, confirming elimination of over 62 million duplicate or invalid entries within the legislative deadline.
- 8.7 Million Ghost and Deceased Records Suppressed Before Disbursement: CRVS death-flag integration via NiFi identified and suppressed approximately 8.7 million deceased-citizen records before the ML stage, blocking associated disbursements from the next payment cycle.
- Full-Corpus Resolution Time Cut from 30 Days to 6 Hours: The Spark distributed pipeline completed a full 280 million record pass in under six hours across 120 auto-scaled workers, replacing a sequential SQL approach requiring 30 or more days per run.
- Citizen Grievance Resolution SLA Met at 48 Hours: The MDM portal enforced a 48-hour resolution SLA through automated escalation, with 94% of disputes resolved within the window during the pilot phase and a full decision audit log retained for statutory compliance.
- 40% Infrastructure Cost Reduction Through Kubernetes Auto-Scaling: Auto-scaling from 10 to 120 Spark workers during peak runs and back to idle overnight reduced total compute spend by approximately 40% against the fixed-cluster baseline.
Before this engagement, the ministry’s 280 million record welfare registry carried a 23% duplicate and ghost-record rate with no deduplication infrastructure, no CRVS integration, and no ML capability. Ksolves AI-First company has delivered a production-grade entity resolution, and MDM platform producing a 218 million record verified golden citizen registry consumed in near-real time by eight government benefit-delivery systems. The 10-year decision audit trail satisfies the Comptroller and Auditor General’s data-integrity requirements, and the clean unified registry now enables predictive fraud detection, need-based scheme targeting, and real-time eligibility verification. For government agencies managing welfare registries at scale, explore Ksolves Big Data Services.
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