How Quantexa's Entity Quality Overlinking (EQO) Model Quantifies Entity Resolution Accuracy

Why entity quality is a problem worth modelling

Entity resolution (ER) is the backbone of Quantexa's platform: it takes millions of raw records from different source systems and links the ones that refer to the same real‑world entity (a person, a business, etc.) into a single resolved entity. Records are linked when they share compounds — normalised keys built from attributes such as name + address, name + phone, or name + date of birth.

Any linkage system must navigate the following two failure modes:

1. Overlinking — a compound key pulls together records that actually describe different real‑world entities, producing a false positive merge. Per the Quantexa glossary, overlinking "occurs when a Compound Key brings together separate Records that describe different Entities … typically caused by similarities between the Records of different Entities."
2. Underlinking — the opposite failure: keys are so strict that records belonging to the same entity never get joined (a false negative), usually because of missing or mistyped data.

Note that an entity can be simultaneously over and underlinked at the same time.

This post is about the first of these. Overlinking is insidious because a single over‑merged entity can quietly corrupt everything built on top of it: network graphs, risk scores, alerting, and — in a financial‑crime context — the picture an investigator forms of who is connected to whom. If two unrelated businesses are fused into one entity, every downstream analytic inherits that mistake.

The challenge is that ER runs at enormous scale. You cannot manually inspect hundreds of millions of entities to find the bad merges. Entity Quality Overlinking (EQO) is Quantexa's answer: a machine‑learning model that assigns every resolved entity an Entity Quality Overlinking Score (EQOS) between 0 and 100, where 0 means bad quality (overlinked) and 100 means good quality (not overlinked). It lets you sort a whole population by quality and attack the worst offenders first.

What overlinking actually looks like

Before modelling anything, it helps to build visual intuition for the signal. Two entities are depicted below in terms of their records and compounds:

Two archetypes emerge:

1. Likely not overlinked (left): a dense, well‑connected entity. Most records share compounds with most other records. The entity is internally consistent — it looks like many observations of one thing.
2. Likely overlinked (right): a "stringy" structure — several dense clusters (each a genuine sub‑entity) joined by a few thin bridges. Those bridges are the tell‑tale sign that one or two coincidental compounds (a shared address, a recycled phone number) have welded distinct real‑world entities together.

What does EQO do?

EQO generates a prediction for each entity for how overlinked it is. It has primarily been tested on Businesses and Individuals, but it can be run on other entity types too.

How well does it work?

On held‑out UK data the following accuracy is achieved:

In conclusion, EQO can accurately predict whether a given entity that has been formed through Quantexa's entity resolution is overlinked or not.