Entity Centric Learning™ Technology

The most accurate data matching technology.

ENTITY CENTRIC LEARNING™ technology is the highly accurate data and record matching methodology with real time learning used by the SENZING® AI to resolve new records against existing entities.

Entity Centric Learning Excels at Discovery and Insight
Entity Centric Learning technology excels at uncovering matches and relationships that conventional methods miss. As more data is received, the AI learns and increases accuracy, discovers hidden connections, and provides a more comprehensive view of entities – even when data is inconsistent or incomplete.

Unlike traditional record-to-record matching, Entity Centric Learning technology can find matches and relationships that other methods simply can’t identify.

Senzing Entity Centric Learning Technology

Improves data matching and record matching accuracy

Identifies hidden connections and new opportunities

Essential for successful bad guy hunting missions

Entity Centric Learning Functionality

Senzing Entity Centric Learning technology treats resolved records as a single holistic entity. The methodology assembles every name, address, phone and other attribute variations of each entity without any tuning required. As more records are received, the system learns nicknames, alternative addresses, common typographical errors and other insights – including intentionally fabricated information – related to an entity.

The Senzing Entity Centric Learning AI compares inbound records to existing resolved entities, providing users with a complete view of every record that matches the same real-world entity, as well as possible matches and possibly related matches. As more data about an entity is received, the system learns in real time and creates more comprehensive entity views or performs self-correction in real time.

Entity Centric Learning Capabilities

Entity Centric Matching

Compares each incoming record to existing holistic entities to determine if a record matches or not.

Ambiguous Conditions

If a record could match two or more entities equally, it’s considered ambiguous until proven otherwise.

Real Time Self-Correction

The system evaluates if new data impacts earlier decisions and instantly makes corrections if needed.

Senzing Entity Centric Learning Benefits

Senzing entity resolution enables your organization to quickly improve financial and compliance operations, fortify fraud and insider threat detection, and drive impactful marketing and customer experience initiatives. By harnessing the power of Entity Centric Learning data and record matching, you can unlock new levels of precision and efficiency in understanding and managing your data.

Get higher quality and more accurate matching

Learn about hidden connections and relationships

Experience holistic 360-degree views of entities

Detect business and marketing opportunities

Identify savvy criminals and other bad actors

Improve outcomes with real time self-correction