NetOwl NameMatcher
Name Matching
Fuzzy name matching addresses the challenges of identifying name variants within and across languages. The winner of the MITRE Multicultural Name Matching Challenge, NetOwl offers highly accurate, fast, and scalable name matching using an advanced machine learning-based approach.
Revolutionary Machine Learning-based Approach
Traditional name matching approaches, such as Soundex, edit distance, and rule-based methods, suffer from both precision (false positives) and recall (false negative) problems in addressing the variety of fuzzy name matching challenges discussed above.
NetOwl applies an empirically driven, machine learning-based probabilistic approach to name matching challenges. It derives intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data.
NetOwl utilizes different matching models optimized for each of the entity types (e.g., person, organization, place) In addition, NetOwl performs automatic name ethnicity detection to apply the most appropriate models to names based on their name ethnicity values in order to achieve state-of-the-art accuracy.
Key Product Features
Accurate
Achieves state-of-the-art name matching accuracy as evidenced by winning the MITRE Multicultural Name Matching Challenge.
Many Entity Types for Matching
Supports fuzzy name matching for multiple entity types: person, place, organization, address, vessel, vehicle, ID, etc.
Multilingual & Cross-lingual
Handles fuzzy name matching in and across many languages and scripts.
Customizable
Allows parameter tuning as well as addition of custom rules and dictionaries to tailor results to your matching specifications.
Fast & Scalable
Performs fuzzy name matching against tens of millions or more names with sub-second response times. Highly scalable name matching software with Docker and Kubernetes support.
Easy Integration
Easy-to-integrate fuzzy name matching product with a REST API.
Multi-ethnicity Name Matching Challenges
NetOwl supports a wide variety of fuzzy name matching challenges including:
- Multiple transliteration variants of foreign names (Abdel Fattah el-Sisi – Abdul Fatah al-Sisi)
- Nicknames (William – Bill – Billy, Mikhail – Misha)
- Initials (John Fitzgerald Kennedy – J. F. Kennedy)
- Misspellings (Barack Obama – Barak Obama)
- Orthographic variations (Joaquín Guzmán – Joaquin Guzman)
- Tokenization variations (Moon Jae-in – Moon Jae In – Moon Jaein)
- Transliteration standard differences (Xi Jinping – Hsi Chin-p’ing)
- Optional name tokens (Joaquín Archivaldo Guzmán Loera – Joaquín Guzmán)
- Name order variations (Park Sol Mi – Sol Mi Park)
- Ethnicity-specific variations (Abd al-Aziz – Abdul Aziz)
- Acronyms (Bank of America – BofA)
- Alternate names (Mumbai – Bombay)
- Names in different languages (Xi Jinping – 习近平 – Си Цзиньпин – شي جين بينغ )
Multilingual and Cross-lingual Matching
NetOwl supports multiple languages and also handles matching of a name in one language against names in other languages. NetOwl currently supports the following languages, and additional languages are on the way.
- Languages written in Latin-based alphabets
- Arabic
- Chinese (traditional and simplified)
- Languages written in Cyrillic-based alphabets
- Greek
- Hebrew
- Japanese (kanji, hiragana, katakana)
- Korean (Hangul)
- Persian (Farsi and Dari)
Name Matching Applications
The NetOwl name matching tool is used for many mission-critical applications in a variety of domains where failure to match may result in not only lawsuits, fines, or financial theft, but also, in the worst case, human casualties. Example of applications include:
- Anti-money Laundering (AML)
- Regulatory compliance (e.g., KYC, PEP, OFAC)
- 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)
For applications that require matching names plus additional record fields such as date of birth, address, etc., NetOwl EntityMatcher is used instead.
Name Search Use Case
The most common use case of NetOwl Name Matcher 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. A matching score is calculated for each search result, and user-specified thresholds can be set to filter out results.For applications that require matching names plus additional record fields such as date of birth, address, etc., NetOwl EntityMatcher is used instead.
Name Comparison Use Case
In addition to the more common search use case, another core use case for NetOwl NameMatcher 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 name of the individual they are sending the money to along with the payee’s bank information. For payment verification purposes, the transfer agent needs to validate that the payee’s name on the account matches the name specified by the sender.
Surprisingly, the two names are often not an exact match for a variety of reasons as described in the name matching challenge section above. NetOwl offers a compare API to determine if the two names are good matches for each other with a matching score within a few milliseconds 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
What is Name Matching?
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
How to Choose a Fuzzy Name Matching Product
Before selecting a name matching product, here are 9 questions you may want to consider.
The Complexities of Building Your Own Name Matching System
Here are some pitfalls to consider if you are weighing whether to build your own Name Matching system
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