Name Matcher

Developer: see the technical specification of the REST service.

The Name Matcher compares the data sets of two people and computes scores of similarity and differences on multiple levels, including names.

The software can be customized to meet specific needs and requirements, and incorporates features such as fuzzy matching, phonetic matching, synonym matching, and multilingual support to achieve higher levels of accuracy and efficiency. 

Name Variations: Reasons Why Names Differ

Two sets of names standing for the same person can have different matching results for various reasons:

• Incomplete names: one of the names is incomplete or the full name misses one part (e.g. Angela Merkel – Angela Dorothea Merkel)
• Abbreviations, acronyms or initials: one of the parts is abbreviated (e.g. John F. Kennedy – John Fitzgerald Kennedy)
• Hypocorisms: a nickname, diminutive or short form is used instead of the official given name or surname (e.g. John Doe – Johnny Doe)
• Titles and qualifiers: a title or qualifier is added before or after the name (e.g. Robert John Downey – Robert Downey Jr.)
• Transcriptions: the transcriptions of the names can vary depending on the target language and the characters used (e.g. Іван Багряний – Ivan Bahrianyi in English, Iwan Bahrjanyj in German, Ivan Bagriany in French or Iwan Bahriany in Polish)
• Misspellings and typos: one contains a spelling mistake (e.g. Lev Tolstoy – Lev Toltsoy), typos or errors in the data entry
• Collective names and business names: a name can refer to a single person, two people (e.g. William and Jessica Wright – Jess Wright), a family (e.g. Familie Hansen – Christian Hansen), or a business entity (e.g. Alexander Kolb – Kolb Architekten)
• Cultural differences: there are naming conventions across many languages, a name can have different gender across several cultures, see Name Genderizer (e.g. Andrea – Italian male name and Andrea – German female name)
• Different versions of the same name: names may have different versions or variations, such as John and Jon or Katherine and Kathryn
• Different alphabets: the same name in Cyrillic vs Latin, or non-English extra characters are missing
• Punctuation differences: names may include different punctuation marks, such as hyphens, apostrophes, or commas
• Order of names: names may be listed in different orders, such as first name first or last name first

NameAPI Customized Matching Requirements

The Name Matcher computes a myriad of numbers to come to a result of telling whether two name pairs likely or possibly belong to the same person, based on some matching requirements:

• Fuzzy matching: match two name sets that are similar but not identical (e.g. Robert Smith – Bob Smith)
• Phonetic matching: match names that sound the same but are spelled differently across different cultures or languages (e.g. Lee – Li)
• Alias matching: match names that are commonly used as nicknames or aliases (e.g. Nick – Nicholas or Dominic)
• Synonym matching: match names that have the same meaning but are spelled differently (e.g. St. John - Saint John)
• Multilingual support: handle names from different languages and character sets
• Scalability: handle large volumes of data and scale up as needed
• Simple string difference (Levenshtein, Damerau algorithm)

Use Cases

The Name Matcher has proven its practicality in a variety of scenarios and industries. The most common use cases for a Name Matcher service are:

• Identity verification/Input validation: run background checks, credit checks, or employment screening
• Banking and finance: verify the identity of account holders and prevent fraudulent activity, such as money laundering or identity theft
• Customer relationship management/segmentation: match customer names accurately and avoid duplicate records; make documentation more specific, as well as direct marketing strategy towards a specific group of people based on culture, marital status, age, ethnicity
• Marketing and Sales: match customer names with their purchasing history
• Fraud detection: match names associated with fraudulent activity or by identifying aliases or false identities
• Education: match student names accurately and avoid duplicate records, ensuring that students receive the correct grades and academic credit
• Government and law enforcement: match names across multiple databases, such as criminal records or watchlists, to identify potential security threats or criminal activity
• Healthcare: match patient names accurately and avoid duplicate medical records, ensuring that patients receive the correct treatment and care
• Data integration: match names across multiple data sources, enabling businesses and organizations to integrate disparate data sets more effectively