NetOwl NameMatcher
Name Matching
Fuzzy name matching addresses the challenges of identifying name variants within and across languages. NetOwl offers highly accurate, fast, and scalable multilingual fuzzy name matching using an advanced machine learning and AI-based approach.
Revolutionary Machine Learning-based Approach
NetOwl’s fuzzy name matching utilizes a revolutionary machine learning and AI-based approach that maximizes recall while minimizing false positives to achieve state-of-the-art accuracy out of the box.
NetOwl’s machine learning-based approach derive intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data.
NetOwl applies different matching models optimized for each entity type (e.g., person, organization, place) as well as each name ethnicity (e.g., Arabic, Chinese, Spanish, Japanese, Korean) in order to achieve the best results. Its automatic name ethnicity identification detects the ethnic origin of a name.
NetOwl outperforms traditional name matching approaches such as Soundex, edit distance, and rule-based methods, which suffer from both precision (false positives) and recall (false negative) problems in addressing the wide variety of fuzzy name matching challenges discussed below.
Key Product Features
Highly Accurate
Achieves state-of-the-art name matching accuracy with fewer false positives and higher recall, as demonstrated by winning the MITRE Multicultural Name Matching Challenge.
Designed for Many Entity Types
Supports fuzzy name matching for multiple entity types: person, place, organization, address, vessel, vehicle, ID, etc.
Multilingual & Cross-lingual
Handles fuzzy name matching in many languages and scripts, including Arabic, Chinese, Hebrew, Japanese, Korean, Persian, Russian, and Spanish.
Customizable & Tunable
Allows straightforward parameter tuning as well as easy addition of custom rules and dictionaries to tailor results to your matching specifications.
Fast & Scalable
Performs fuzzy name matching against hundreds of millions of names with sub-second response times. Highly scalable name matching software for enterprise. Supports real-time applications.
Easy to Deploy
Easy and quick to integrate into your applications through a REST API. Supports Docker and Kubernetes.
What Name Matching Challenges does NetOwl Handle?
NetOwl addresses a wide variety of fuzzy name matching challenges including complex phenomena that standard name matching approaches would miss. For example:
- Multiple transliteration variants (Abdel Fattah el-Sisi – Abdul Fatah al-Sisi)
- Misspellings (Barack Obama – Barak Obama, TJ Maxx – TJ Max – TJMax)
- Nicknames (William – Bill – Billy, Mikhail – Misha, Eli Lilly – Lilly)
- Missing or extra name tokens (Joaquín Archivaldo Guzmán Loera – Joaquín Guzmán, PNC Financial Services Group, Inc. – PNC Financial)
- Tokenization variations (Moon Jae-in – Moon Jae In – Moon Jaein, Walmart – Wal-Mart – Wal*Mart)
- Ethnicity-specific variations (Abd al-Aziz – Abdul Aziz)
- Name order variations (Park Sol Mi – Sol Mi Park)
- Initials (John Fitzgerald Kennedy – J. F. Kennedy)
- Orthographic variations (Joaquín Guzmán – Joaquin Guzman)
- Acronyms (Bank of America – BofA, Recreational Equipment, Inc. – REI)
- Use of different transliteration standards (Xi Jinping – Hsi Chin-p’ing)
- Names in different languages (Xi Jinping – 习近平 – Си Цзиньпин – شي جين بينغ )
Multilingual and Cross-lingual Matching
NetOwl supports multiple languages and scripts, as listed below, to address global name matching requirements:
- English, Spanish, and other languages written in Latin-based alphabets
- Arabic
- Chinese (traditional and simplified)
- Russian and other languages written in Cyrillic-based alphabets
- Greek
- Hebrew
- Japanese (kanji, hiragana, katakana)
- Korean (Hangul)
- Persian (Farsi and Dari)
Additionally, NetOwl performs cross-language name matching to match a name written in one language against names written in other languages (e.g., Chinese vs. English).
What Applications is NetOwl NameMatcher Used for?
NetOwl’s fuzzy name matching product is used for many mission-critical applications such as anti-money laundering (AML) and sanctions screening. Sample applications include:
- AML, KYC, PEP screening for regulatory compliance
- Fraud Detection
- Verification of Payee (VoP)
- Homeland security (e.g., visa screening, border security)
- Counter-terrorism (e.g., terrorist watch lists)
- Customer Data Management (CDM)
Applications that require multi-field record matching (i.e., matching names plus additional record fields such as date of birth and home address) are supported by NetOwl EntityMatcher.
Name Search Use Case
The most common use case of NetOwl’s fuzzy name matching is to provide a fast and accurate search for specific names, including names of people, organizations, places, and addresses, against a set of known name records of interest, such as watchlists, sanction lists, customer databases, and so on. Some of our customers have large data sets to search against, which can be in the tens or even hundreds of millions of records.
Whether the use case is trying to search against a set of “good guy” names like customers or “bad guy” names like those on sanctions lists, NetOwl builds a proprietary index of the name records for efficient and intelligent search. Through its search API, NetOwl is able to match names that differ in any combination of the name variations outlined above. NetOwl also calculates a matching score for each search result and allows for user-specified thresholds to filter out results.
Name Comparison Use Case
In addition to the more common search use case, another core use case for NetOwl’s name matching product is to compare two names to determine instantly if they represent the same name.
Consider a payment transfer that sends money from one person to another. Typically, the sender supplies the payee’s name and bank information. For payment verification purposes, the transfer agent needs to validate that the name on the receiving account matches the payee’s name specified by the sender.
Surprisingly, the two names are often not an exact match for a variety of reasons such as nicknames, misspellings, missing components, reordering, etc. NetOwl offers a compare API that provides a matching score in a few milliseconds to determine whether the two names are good matches for each other and thus be able to validate or reject the transfer.
Name Matching Solutions
Remove tag on Small
AML, KYC, PEP
Border Security
Fraud Detection
Deploy on Premise or in the Cloud
Trusted by leading global organizations
Featured Blog Posts
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There are many applications where failing to recognize name variations of an entity can have disastrous consequences. What is Name…
What is Cross-Language Name Matching?
Name Matching is an even more complex problem when it involves multiple writing systems
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Before selecting a name matching product, here are 9 questions you may want to consider.
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