TrustScore combines identity verification with credit, employment, insurance, and social data into one number your team can act on.
The scoring engine is transparent by design. Today it's built for testing and workflow design, not production lending decisions.
Score Breakdown
TrustScore pulls from every connected data source and traces each factor back to real evidence.
Document type, verification outcome, and confidence from the identity check.
Payment behaviour, balances, and credit utilization from TransUnion.
Job duration, employer type, and contribution consistency from RSSB.
Coverage duration and payment regularity from health insurance records.
Ubudehe classification and program participation. Especially useful for thin-file citizens.
Synthetic scoring engine
A traditional credit score is one signal from one source. TrustScore combines five data sources into one explainable number.
Every score is explainable. Every factor is traceable. No black boxes.
People without formal credit history still get a meaningful score from employment, insurance, and social data.
The engine is transparent and synthetic today so your team can validate workflows before going live.
Every factor maps back to real evidence so your team can justify decisions and review thresholds.
Identity, credit, employment, insurance, and social protection all contribute when connected.
A trained model comes later, after live data quality and fairness standards are in place.
The current engine is explicit about its scope. Fairness tooling ships with the trained-model phase.
Your team sets the thresholds. These ranges are a starting point for sandbox testing.