Can Entity Resolution Be Used to Identify Fraud?
Fraud detection has become one of the most critical challenges across industries—from banking and insurance to healthcare and government services. As fraudsters grow more sophisticated, organizations need equally advanced tools to detect hidden connections, uncover patterns, and prevent financial loss. One of the most powerful yet often underutilized techniques in this space is entity resolution.
What Is Entity Resolution?
Entity resolution (ER), sometimes called record linkage or data matching, is the process of identifying and linking records that refer to the same real-world entity across different datasets. These entities could be people, businesses, addresses, devices, or accounts.
For example:
- “John A. Smith” at one address and “J. Smith” at another may be the same individual
- Multiple insurance claims may reference slightly different versions of a provider name
- A single fraud ring may use variations of identities to avoid detection
- Problems in transliteration such as Abdullah vs Abdalla or Abdelhamid vs Abd al-Hamid can hinder both matching and searching secure
Entity resolution connects these fragmented data points into a unified view.
Why Fraud Detection Needs Entity Resolution
In reality, fraud rarely occurs in isolation. Instead, it often involves multiple identities and coordinated activities.
- Multiple identities
- Coordinated activities
- Reused contact details, devices, or addresses
- Cross-channel behavior (online, phone, in-person)
Traditional rule-based systems often miss these connections because they rely on exact matches or limited datasets. Entity resolution, by contrast, excels at finding non-obvious relationships, making it highly effective for fraud detection.
Key Ways Entity Resolution Identifies Fraud
1. Detecting Duplicate or Synthetic Identities
Fraudsters often create multiple identities using slight variations in names, addresses, or identifiers. Entity resolution can:
- Flag clusters of identities that share suspicious similarities
- Match fuzzy variations (e.g., misspellings, abbreviations and transliteration)
- Link records across systems
This is particularly useful for identifying synthetic identity fraud, where fake and real information are combined over time.
2. Uncovering Hidden Networks
Fraud rings often operate as networks rather than individuals. Entity resolution helps:
- Identify shared attributes (phone numbers, emails, IP addresses)
- Reveal complex relationships that indicate collusion
- Reveal subtle relationships that can indicate deception
- Link people, businesses, and accounts
When visualized, these connections often expose organized fraud schemes that would otherwise remain hidden.
3. Cross-Domain Data Integration
Fraud signals are often scattered across different systems:
- Claims systems
- External watchlists
- Transaction systems
- Customer databases
Entity resolution brings data together. As a result, organizations get a single, accurate view. This improves detection and reduces missed fraud cases.
4. Reducing False Positives
Overly aggressive fraud rules can flag legitimate customers, creating friction and reputational risk. Entity resolution improves precision by:
- Enabling more nuanced decision-making
- Distinguishing between similar but distinct individuals
- Providing confidence scores for matches
This balance between detection and customer experience is critical.
5. Real-Time Risk Scoring
Modern entity resolution systems can operate in near real-time, allowing organizations to:
- Trigger alerts or block actions before fraud occurs
- Assess risk at the point of transaction
- Identify suspicious linkages instantly
Industry Applications
Financial Services
- Linking mule accounts
- Detecting money laundering networks
- Identifying account takeovers
Insurance
- Linking claimants, providers, and witnesses
- Identifying duplicate claims
- Detecting staged accidents
Healthcare
- Detecting abnormal billing patterns
- Uncovering billing fraud and phantom providers
- Linking patients and providers across claims
Government and Public Sector
- Supporting law enforcement investigations
- Identifying benefit fraud
- Linking individuals across agencies
Challenges to Consider
While powerful, entity resolution is not without challenges:
- Data quality issues (incomplete, inconsistent data)
- Privacy and regulatory constraints
- Scalability with large datasets
- Balancing precision vs. recall
Successful implementations require strong governance, high-quality data pipelines, and well-tuned matching algorithms.
The Role of AI and Machine Learning
Modern entity resolution solutions increasingly incorporate AI and machine learning to:
- Improve match accuracy over time
- Learn from feedback and investigator decisions
- Adapt to new fraud patterns
These capabilities are essential in staying ahead of evolving fraud tactics. Read more about the foundation pillars of governance for AI.
Conclusion
So, can entity resolution be used to identify fraud? The answer is a resounding yes.
In fact, it is one of the most effective techniques for uncovering hidden relationships, detecting complex fraud schemes, and improving overall detection accuracy. By connecting the dots across disparate data sources, entity resolution transforms fragmented information into actionable intelligence.
Organizations that invest in robust entity resolution capabilities are better positioned to not only detect fraud—but to prevent it before it happens.
If you’re exploring fraud detection strategies, entity resolution shouldn’t be an afterthought, it should be a foundational capability. Read more about IMT’s experience in applying entity resolution to person enrollment processes for increased services delivery and potential fraud identification and important governance considerations for state agencies.
Contact sales@imt.ca or fill out our Connect Form to learn how you can take the first steps in identifying fraud in your organization while building trust and confidence in your data through context accumulation.
