How Does it work?

• Input: Enter the Name in the API.
• Output: Get Match Score: A score between 0 to 100 indicating similarity.
• Match Type: e.g., Exact Match, Partial Match, Misspelled, No Match.
• Confidence Level: e.g., High, Moderate, Low, Very Low.

Key Features:

• Phonetic Matching: Detects Name similarity based on sound, even if spelled differently (e.g Jon vs. John).
• Cultural Name Logic: It understands different name writing styles based on regions and cultures.
• Fuzzy Matching: Handles minor spelling mistakes, typos, and letter swaps.
• Score-Based Output: It provides a numerical match score for better decision making.
• Support for initials: It matches names even when only initials are provided. (e.g. A. Sharma vs. Ankit Sharma).

Use Cases

• KYC Identity Verification: Name match rejection is a common error that occurs during customer onboarding. Financial institutions can use the AI-powered Name Match API to ensure accurate verification even if the name is misspelled or abbreviated. It reduces manual review and enhances the onboarding process.

• Database Deduplication: A large database often has multiple entities for the same person due to name variations or input errors. With fuzzy and phonetic name matches, the API helps identify and merge such duplicate records, improving data accuracy.

• Fraud Detection: Detect fake IDs and documents with name manipulation. The AI-powered Name Match API detects subtle differences, such as swapped characters, missing letters, or phonetic tweaks.