Batch Processing Identity Verifications: Optimizing Throughput and Cost

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Batch identity verification groups multiple requests into automated workflows, helping organizations reduce per-transaction costs, improve throughput, and perform large-scale onboarding, periodic reviews, data migrations, and compliance checks such as AML screening. Effective implementation depends on standardized input data, tailored validation rules for differing risk levels, and detailed error reporting and audit trails to support exceptions and manual review. Didit presents its AI-native, API-driven platform as a solution for both real-time and batch workflows, using modular services including ID verification, proof of address, face matching, age estimation, and sanctions screening. Its tools use OCR, automated fraud detection, workflow orchestration, webhooks, and configurable no-code flows to integrate with existing systems, while its pay-per-successful-check pricing and free core KYC offering are intended to lower adoption and operating costs.

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