CASE STUDY

Elevating eKYC Accuracy and Fraud Detection Through Human-Led Verification

CLIENT:

Leading eKYC Technology Company

INDUSTRY:

Identity Verification

Process Type

KPO | Service Type: BPO

BACKGROUND: AI LIMITATIONS IN COMPLEX IDENTITY VERIFICATION

The client, a global identity verification platform, uses artificial intelligence (AI) to automate KYC checks for banks, fintechs, and crypto platforms. While highly scalable, their AI engine occasionally struggled to verify documents involving poor image quality, regional ID variations, or sophisticated forgeries.
Cases flagged for human review were piling up. Incomplete verification pipelines created delays, jeopardized SLAs, and increased risk for high-stakes clients. The platform needed trained human reviewers to supplement AI decisioning with fraud detection expertise, and to create a closed feedback loop for machine learning improvement.

MAS CALLNET SOLUTION: A HYBRID VERIFICATION MODEL FOR MODERN COMPLIANCE

Mas Callnet deployed a KPO-grade verification desk to classify, validate, and flag suspicious eKYC documents with accuracy, speed, and audit-ready documentation—while also building a human-in-the-loop model to enhance the platform’s AI learning lifecycle.