NetOwl EntityMatcher
Identity Resolution
Identity resolution, aka entity matching, addresses the challenges of identifying records that refer to the same real-world entity. NetOwl offers highly accurate entity matching using an advanced machine learning-based fuzzy name matching and record field matching.
Intelligent Identity Resolution Powered by Machine Learning
NetOwl performs identity resolution based on any combination of available entity record fields, such as name, date of birth, address, place of birth, etc. NetOwl utilizes its unique machine learning-based fuzzy name matching and proprietary indexing engine to achieve high accuracy and scalability.
NetOwl allows its users to set the importance of each field through field weights. Furthermore, more advanced field parameters offer abilities to treat certain fields specially (e.g., matching certain fields may contribute only positively or supersede the rest of fields), providing maximum flexibility to produce the best overall matching results given application-specific business rules.
In addition, NetOwl’s built-in NLP-based name parsing reduces the need for data cleansing. For example, it handles person names with honorifics or job titles, addresses with abbreviations, dates and phone numbers in different formats, etc. automatically.
Key Product Features
Accurate
Maximizes accuracy by leveraging award-winning (MITRE Challenge) name matching technology and seamlessly combining evidence from multiple entity fields.
Many Entity Types for Matching
Supports multiple field types for matching: person, place, organization, address, vessel, vehicle, date, email, nationality, phone, alphanumerics, 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 multi-field record matching against tens of millions or more records with sub-second response times. Highly scalable matching software with Docker and Kubernetes support.
Easy Integration
Easy-to-integrate multi-field fuzzy name matching product with a REST API.
State-of-the-Art Matching Accuracy
NetOwl leverages our machine learning-based multicultural, multilingual name matching product, NetOwl NameMatcher, to enable sophisticated name matching specifically trained for each entity type across different languages and to perform scalable, high-accuracy identity resolution. NetOwl handles many different types of entities including person, organization, place, address, nationality, date, phone, etc.
Accuracy of name matching of various entity types is crucial in identity resolution as the values of many important record fields are names of such entity types. For example, a person record might consist of the following fields:
| Field Name | Field Type | Field Value |
|---|---|---|
| Name | person | Robert Anderson |
| Spouse | person | Isabella Anderson |
| Employer | organization | ABC, Inc. |
Date of Birth | date | May 22, 1987 |
Place of Birth | place | Peoria, IL |
Home Address | address | 234 Broome Soho, NYC |
Multilingual and Cross-lingual Matching
NetOwl’s identity resolution software supports multiple languages and also handles matching of a name in one language against names in other languages:
- 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)
Additional languages are on the way.
Identity Resolution Applications
Identity Resolution is used for mission-critical applications in numerous domains such as:
- Anti-money Laundering (AML)
- Regulatory compliance (e.g., KYC, PEP, OFAC)
- Fraud Detection
- Electronic Health Records (EHR)
- Customer Data Management (CDM)
- Homeland security (e.g., visa screening, border control)
- Counter-terrorism (e.g., terrorist watch lists)
Multi-field Search Use Case
The most common use case of NetOwl EntityMatcher is to provide a fast and accurate search based on multiple fields of a record, typically a primary name field, which contains either person names or organization names, plus one or more fields, such as an address or a date of birth. These additional fields provide more details about the record that can be used to distinguish between potential matches that have the same or similar primary name values. These additional details are common in watchlists, sanction lists, and customer databases. 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 all fields of the records for efficient and intelligent search. Through its search API, NetOwl is able to identify the best overall matching records based on how closely each search field value is to a record’s corresponding indexed value for that field. Through field weights and other parameter settings, our customers can specify which fields are most important to the overall match and produce an overall matching score based on the customers’ business requirements.
Multi-field Comparison Use Case
In addition to the more common search use case, another core use case for NetOwl EntityMatcher is to compare two records to determine instantly how closely the two records match based on all of the supplied field values.
Consider a payment transfer that sends money from one person to another. The sender may supply not only the name of the individual they are sending the money to but also their address along with the payee’s bank account information. For payment verification purposes, the transfer agent may want to validate that the payee’s name and address on the account matches the information specified by the sender.
Surprisingly, the two names or two addresses are often not an exact match for a variety of reasons such as misspelling, nicknames, transliteration variations, abbreviations, and so on. NetOwl offers a compare API to determine if the combinations of the two or more field values are good matches for each other with a matching score within a few milliseconds to validate or reject the transfer.
Identity Resolution 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
How Identity Resolution Supports a Single View of the Customer
The myriad of ways in which customers interact with a company and the discrepancies in customer records make the job…
How Identity Resolution Helps Detect Fraud in Public Assistance Programs
Fraud often hides behind non-obvious record similarities that are hard to detect by humans alone.
Identity Resolution Helps Produce the Most Complete and Accurate Marketing Lists
Going from duplicate and incomplete records to a consolidated marketing list can be a daunting task. But it’s a necessary…
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