Duplicate Prevention System (IBR-016, Core)

Critical function that uses the Identity Verification Service to prevent duplicate beneficiary records

Cross-checks identity information, flags potential duplicates, enforces uniqueness constraints

Data Management Capability Area, Security and Privacy Capability Area

Preventing duplicate records is essential for maintaining data integrity, preventing fraud, and ensuring accurate benefit allocation. Without this capability, the IBR would be vulnerable to double-dipping, inefficient resource allocation, and compromised data quality, undermining the entire system's reliability.

• Multiple matching algorithms for different identity scenarios

• Configurable matching thresholds for different contexts

• Manual review processes for uncertain matches

• Integration with national ID systems where available

• Performance optimization for large-scale matching operations

Unified Beneficiary Data Model (IBR-020, Core)

Essential function that implements or integrates with a Unified Beneficiary Data Model to represent beneficiaries across all social protection programs

Provides standardized data structures, ensures semantic consistency, enables interoperability

Data Management Capability Area, Interoperability Capability Area

A standardized data model is fundamental to the IBR's ability to integrate information across different programs. Without this unifying structure, data inconsistencies would prevent meaningful cross-program coordination, compromise data quality, and hinder system interoperability.

• Flexibility to accommodate program-specific data requirements

• Alignment with international standards where applicable

• Forward compatibility to support system evolution

• Comprehensive documentation for implementers

• Balance between standardization and customization

Data Quality Standards (IBR-015, Optional)

Function that implements a data quality validation service that can be invoked by external systems for ensuring data standards, updated quarterly

Defines quality metrics, validates against standards, reports on data quality issues

Data Management Capability Area, Interoperability Capability Area

Initial implementations may use basic validation rules embedded in data entry processes. However, as data quality becomes more critical and external system integration increases, a formal quality validation service becomes increasingly important for maintaining ecosystem-wide data integrity.

• Multiple systems exchange data regularly

• Complex data quality requirements exist

• External systems need to validate against common standards

• Automated data quality monitoring is needed

• Compliance with formal data standards is required

Identity Data Manager (IBR-018, Optional)

Function that stores and manages verified identity data for each beneficiary

Securely stores identity credentials, manages verification status, links to external identity systems

Data Management Capability Area, Security and Privacy Capability Area

Basic systems may rely on external identity systems without storing detailed identity data. As systems mature and require more sophisticated identity management, particularly in contexts with limited national ID coverage, this function becomes increasingly valuable for ensuring reliable beneficiary identification.

• National ID systems have limited coverage or reliability

• Multiple identity credentials need to be managed

• Identity verification occurs through multiple channels

• Biometric or other advanced identity verification is used

• Privacy regulations require specialized identity data handling

Data Governance Framework (IBR-028, Optional)

Function that implements a framework for maintaining data integrity and reliability, integrating with external data management and audit systems

Enforces data policies, manages data lifecycle, monitors compliance, supports auditing

Data Management Capability Area, Security and Privacy Capability Area

Simple systems can operate with basic data management policies. However, as systems scale and data complexity increases, a formal governance framework becomes increasingly critical for maintaining data trustworthiness, compliance, and sustainable management practices.

• Complex data ecosystems involve multiple stakeholders

• Regulatory compliance requires formal governance

• Data quality issues become systemic

• Data policies need systematic enforcement

• Cross-organizational data sharing increases