LLM Tool-Use for KYC: Automating Document Analysis
Blog post from Didit
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Large language models with tool-use capabilities are presented as a way to modernize Know Your Customer compliance by automating document capture, data extraction, cross-referencing, anomaly detection, and risk decisions that traditionally required manual review. Building on OCR and machine-readable-zone parsing, these systems can combine identity-document checks with biometric face matching, liveness detection, IP and device analysis, proof-of-address validation, age estimation, and anti-money-laundering screening to identify inconsistencies and possible fraud such as altered documents, identity theft, spoofing, or location mismatches. The approach is described as modular, allowing organizations to select verification tools according to their regulatory and risk requirements while routing higher-risk cases for human review. Didit is highlighted as an AI-native, developer-oriented platform offering these services through APIs and a no-code console, with a free core KYC tier, pay-per-successful-check pricing, and tools intended to support scalable global identity verification.
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