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The most unique entity resolution algorithm in the world.

In today's digital world, customer information is flowing into enterprise systems through multiple channels, making it necessary for organizations to extract and assemble it to resolve customers' identities. This process requires a robust entity resolution mechanism that can handle real-time digital data and effectively piece together the puzzle of customer interactions.


Entity Resolution is crucial in various industries such as law enforcement, banking, identification number allocation, transaction grouping, and individual profile creation. It is typically integrated into software systems to facilitate entity search and matching.
Introducing PrimeMatch, an unparalleled algorithm for name matching that uses a unique mathematical model to compare strings and find the best matches.


Entity Resolution solutions typically involve matching names, fathers' names, and dates of birth based on character-based algorithms. This falls under the realm of text search and retrieval, which has garnered significant research interest. Early published work on name matching was done by the statistics community, focusing on the task of identifying duplicate records in databases, also known as record linkage. Proprietary solutions exist and are broadly classified as Exact Name Searches, Searching with Wildcards, Keying Partial Words, Text Retrieval Software, Use of Standard Name Bins, and Fuzzy Name Matching. These approaches use either phonetic coding or similarity metrics to determine a match. Phonetic codes are created for the searched text, while the database is indexed beforehand using those codes, which act as hash keys. Different types of phonetic codes, such as Soundex, NYSIS, Metaphone, Double Metaphone, Caverphone, etc., are available and utilized. Another approach involves using different types of textual similarity metrics, which calculate the similarity of two strings as a number between 0 and 1. A value of 0 means the strings are completely different, while a value of 1 indicates a perfect match. Intermediate values correspond to a partial match. The similarity metrics include Hamming distance, edit distance (also known as Levenshtein distance), n-gram indexes, Ratcliff/Obershelp pattern recognition, Jaro-Winkler similarity, and more.

Our Process

Each organization, particularly in the banking, financial services, and insurance industries, has its own framework for managing governance and risk controls.

Customer profiling is crucial during onboarding and periodically throughout the entire customer lifecycle to identify risks and effectively deal with them.

Customer profiling involves screening customers against suspicious lists, fraud lists, and negative lists. It also goes beyond individual profiling to identify and profile related entities of the customer, such as linked family members, employers, and associates, to create a risk profile of the customer.

Posidex's Prime Network solution enables organizations to have a complete 360-degree view of the customer and connected entities across all connected sources. It should be part of any early warning system that financial organizations wish to implement.



Expansion and Innovation

Without effective Fraud & Risk Mitigation processes in place, organizations may miss out on opportunities to expand or innovate due to the lack of protection measures.


Legal & Regulatory Issues

Failure to comply with legal and regulatory requirements related to fraud and risk mitigation can result in legal action, penalties, and reputational damage.


Customer trust

Customers may become demotivated and lose trust in the organization if they perceive that it is not taking adequate steps to protect against fraud and other risks.


Damage to Reputation

Fraud and other risks damage an organization's reputation in the eyes of suppliers, and other stakeholders. This can result in loss of business and decreased revenue.


Financial Loss

Organizations are falling victim to fraudsters and scammers who may steal money, data, or assets. Data breaches are the most important worry for the organizations as this results in significant financial losses and damage to the organization's reputation.


1. Customer risk scoring

Calculating the risk for each customer datapoint improves the confidence and finding right solutions for the customer. This is very useful in financial domains to personalize the financial decisions per customer.

2. Underwriting

Clean and correct incoming data to ensure a standard version for underwriting by identifying key entities that lets the lender verify and assess the risk before issuing the final approval.

3. Identity Resolution

Fraud and risk scoring can return highest quality customer data with lesser risk of breaking down. Resolve and reconcile data continuously by leveraging data assets, quality attributes and third-party references, and any other client specific-rules.

4. Customer Risk Profiling

Risk scoring can be automated and integrated into the systems for quicker and smarter decision-making applications without human intervention. Enriched data and customer profiles enable the businesses to integrate quicker intelligent real-time insights.


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