Entity Resolution: Connect Same Entity Records
The same customer rarely appears the same way across every system. A customer may have different names, addresses, phone numbers, identifiers or other attributes across systems, apps, tools and software for different teams and business verticals. To a database, these can look like different records. To the business, they may all belong to the same person.
Entity resolution determines which records refer to the same real-world entity, even when the information is incomplete, inconsistent or different across sources.
What Is Entity Resolution?
Entity resolution is the process of identifying and linking records that represent the same real-world entity across different data sources. For customer data, entity resolution can determine whether "Rahul Kumar", "R. Kumar", "Rahul K." and "Rahool Kumaar" represent the same customer.
Instead of relying on a single identifier or exact string match, an entity resolution system evaluates multiple attributes and matching patterns to determine the likelihood that records belong to the same entity.
This makes entity resolution particularly useful where customer data is generated by multiple systems and captured in different formats. Without effective entity resolution, businesses can face:
- Duplicate customer records
- Inconsistent customer identities
- Fragmented customer profiles
- Repeated customer verification
- Inaccurate analytics
- Poor customer segmentation
- Slower onboarding
- Incomplete risk assessments
- Missed cross-sell opportunities
- Increased operational effort
How Does Entity Resolution Work?
1. Identify records
Locate customer records across databases, applications, products and other data sources.
2. Compare attributes
Compare relevant attributes such as name, address, date of birth, phone number and other available identifiers.
3. Determine the match
Use matching rules, algorithms and statistical or machine-learning techniques to determine whether records represent the same entity.
4. Link or consolidate
Link records belonging to the same entity and make the resulting identity available to downstream applications and processes.
Entity Resolution vs. Exact Matching
| Attribute | Record A | Record B |
|---|---|---|
| Name | Rajesh Kumar | R. Kumar |
| Phone | 9848012345 | +91 98480 12345 |
| City | Hyderabad | Hyd |
| Date of birth | 15-08-1985 | 15 Aug 1985 |
| Entity Resolution | Exact Matching |
|---|---|
| Do these records represent the same real-world entity? | Are these two values exactly the same? |
| Result: Same entity | Result: Different entity |
Benefits Of Real-Time Entity Resolution
Customer identity search in real time allows an application or user to find a customer across available enterprise data without waiting for a batch process a week or month later. This brings in many business benefits. For instance, real-time record matching in BFSI helps instantly validate customer information for quicker verification.
| Onboarding | Find out whether the applicant already exists. |
|---|---|
| Customer service | Locate the customer's relationships across products. |
| KYC | Match submitted information against existing customer records. |
| Risk | Identify known or related customer risk for compliance check. |
| Customer 360 | Connect records of the same customer. |
Real-time record matching in BFSI is particularly important because banks, insurers and other financial institutions operate across multiple products and high-volume customer interactions. It can support KYC, onboarding, fraud prevention, risk management and customer 360 use cases, such as:
- Duplicate application detection
- Fraud detection
- Payment validation
- Cross-selling
- Regulatory compliance
Why Choose Posidex for Entity Resolution?
Posidex's entity resolution technology is designed for organizations that need precision, scale and speed. Prime 360 offers real-time entity search and match, including real-time deduplication and customer-data validation.
Built on a powerful entity resolution engine, it is already used for high-volume customer-data environments. The ML-powered matching has been trained on 5 billion customer data. With the ability to distinguish true matches from non-matches, it offers least false positives. The enterprise-grade solution handles:
Resolve customer identities across applications, databases and business systems.
Handle differences in names, addresses, identifiers and other attributes.
Define matching parameters and rules based on business requirements.
Process large customer datasets without sacrificing matching performance.
Allow matching capabilities to be embedded into existing workflows.
Frequently Asked Questions About Entity Resolution
What is entity resolution?
Entity resolution is the process of identifying records from different data sources that represent the same real-world entity. For customer data, it helps organizations connect fragmented records, resolve duplicate identities and create a more accurate view of each customer.
What is the difference between entity resolution and record matching?
Record matching compares records to determine whether they are similar or represent the same entity. Entity resolution is the broader process of identifying, linking and resolving real-world identities across multiple records and data sources.
What is real-time customer deduplication?
Real-time customer deduplication identifies whether a new or existing customer record is a duplicate of an entity already present in the organization's data in real-time instead of days or weeks later. The system evaluates matching attributes and returns a result indicating whether the customer already exists or whether the record appears to represent a new entity. This is particularly valuable for:
- Loan applications
- Credit-card applications
- Insurance applications
- KYC verification
- Payment processing
What is a customer identity resolution software?
Customer identity resolution software identifies and links customer records that belong to the same person or entity across multiple systems. Enterprise solutions typically need to handle large datasets, data variations, configurable matching rules and both real-time and batch processing.
What is the difference between entity resolution and deduplication?
Deduplication focuses on identifying duplicate records. Entity resolution determines whether records represent the same real-world entity. Entity resolution can therefore be used as the intelligence layer behind customer deduplication.
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