Eliminating Blind Spots in Identity Verification (IDV)
The bank struggled with sequential vendor onboarding, limited fraud scenario testing, and reliance on polished demos that masked real risks. Without synthetic datasets, multiple test environments, and coordinated user journeys, they couldn’t evidence which provider could reduce false positives, cut drop-offs, or meet compliance at scale.
Outcomes
30%
False Positives Reduced
25%
User Drop Off Rates Reduced
45%
Verification Time Cut
95%
Regional ID Coverage Expanded
Technology Vendors Suited to Evaluation
Business Problem
The bank needed to validate identity verification solutions against real-world fraud risks, but the data challenge was significant. Real customer data couldn’t be exposed, yet meaningful testing required synthetic users that could mimic high-risk scenarios: mismatched names, fraudulent IDs, incorrect holograms, and regional ID coverage.
Traditional processes made this harder. Vendors had to be onboarded one at a time, driving up cost and delay, while polished demos failed to show how solutions would hold up under pressure.
Without controlled environments, scaled tests across four vendors, and multiple participants simulating realistic journeys, the bank risked ongoing false positives, customer drop-offs, and compliance gaps – with no evidence of improvements to NPS, first-contact resolution, or cost efficiency.
Challenges
- Sequential Vendor Onboarding – Each provider had to be onboarded one by one, making comparisons slow, costly, and resource-heavy.
- Reliance on Demos, Not Evidence – Decisions risked being made on vendor demos rather than rigorous, real-world validation.
- Complex Dataset Creation – The bank needed synthetic users to replicate realistic fraud scenarios (mismatched names, fraudulent IDs, incorrect holograms) without exposing customer data.
- Limited Fraud Scenario Testing – Existing processes couldn’t simulate diverse and scaled attacks to stress-test provider performance.
- Environment Constraints – Multiple controlled test environments had to be spun up and coordinated across teams.
- Participant Coordination – Multiple users had to simulate live journeys across four vendors, requiring structured orchestration.
From Idea to Evidence with NayaOne
NayaOne enabled the bank to validate four identity verification providers in parallel, inside production-like sandbox environments that mirrored legacy integrations.
- Synthetic Data & Fraud Scenarios: Complex synthetic datasets were generated to replicate real-world risks – from mismatched names and fraudulent IDs to incorrect holograms and regional document coverage – without exposing customer data.
- Parallel Environments: Multiple secure test environments were spun up so vendors could be compared side by side, rather than sequentially.
- Scaled User Journeys: Multiple participants simulated realistic onboarding flows across vendors, creating the scale needed to capture drop-offs, false positives, and escalation paths.
- Evidence Under Stress: Providers were tested not just on polished demos, but under realistic fraud attempts and volume, revealing their true accuracy, compliance, and user experience impact.
This approach gave the bank hard evidence on which vendor could deliver measurable improvements in NPS, first-contact resolution, and operational efficiency – while reducing risk and cost of poor vendor selection.
Impact Metrics
PoC Timeline Reduction
8 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
Shifted from partial demos to live, repeatable evidence under fraud and user scenarios.
KPIs
- False Positive Rate (%) – proportion of legitimate customers incorrectly flagged as fraud.
- Fraud Detection Rate (%) – ability to catch fraudulent IDs (mismatched names, incorrect holograms, synthetic identities).
- User Drop-off Rate (%) – % of customers abandoning the onboarding journey due to friction.
- First-Contact Resolution (%) – proportion of customers successfully verified on first attempt, without escalation.
- Verification Time (seconds) – average time to complete identity verification.
- Regional ID Coverage (%) – success rate of verification across local/regional identity documents.
- Compliance Alignment (%) – proportion of verifications meeting regulatory/AML/KYC requirements.
- Cost per Verification – operational cost to complete a single successful onboarding.
Validate IDV Vendors with Real-World Scenarios
Run parallel PoCs with synthetic fraud events and live-user testing – reducing cost, fraud exposure, and decision risk in weeks, not years.
