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Insurance Industry

Fortify Risk & Fraud Detection with Entity Resolution

Detecting and preventing insurance risk and fraud is more challenging than ever. Poor data quality, data errors and sophisticated fraud schemes cost U.S. insurers billions annually. Monitoring claims for fraud and adopting new technologies to keep up with clever fraudsters and data risk is a continuous struggle that can impact an insurer’s profits and reputation.

The Senzing® API solves this problem by quickly and accurately resolving entities and discovering relationships and hidden networks. Senzing is a crucial piece of the puzzle to reduce risk, identify fraudsters, prevent losses and improve compliance. Whether you’re a life, health or P&C insurer, the Senzing API improves the performance of your risk and fraud systems.  

Senzing is also a valuable tool to add to your system when resolving external data sources (such as data from credit bureaus) with internal data to facilitate and improve underwriting.

Senzing entity resolution for fraud and risk detection

 Senzing inline imgdot left insurance industry -Fast & Easy Implementation

Senzing is a developer-focused, easy-to-deploy API for real time entity resolution. It provides better, faster, more cost-effective entity resolution that fits within an insurer’s existing risk and fraud detection, underwriting, and claims processing systems and architecture.

Example: Claims Processing Architecture

Senzing integration architecture insurance claims

The Senzing API easily plugs into your application, data fabric, pipeline or service.

Create Complete 360 Views of Claims & Entities 

Senzing resolves and links members, providers and brokers to enable advanced link analysis, social network analysis and better underwriting accuracy:

 Advanced entity resolution relationship link analysis Advanced Link Analysis

Find connections between entities (individuals, businesses, assets) that may suggest organized fraud rings.

Senzing identifies non-obvious, hidden and obfuscated relationships and provides 360-degree views for faster and more effective fraud prevention.

 Entity resolution social network analysis Social Network Analysis

Examine relationships between claimants to identify collusion in fraudulent schemes.

If you use ESRI geospatial analytics, Senzing can help identify locations of claims and high-risk areas with unusual geographic claims patterns.

 Entity resolution data accuracy Better Underwriting Accuracy

Resolve 3rd-party data (credit reports, public records, social media) and get more insights into potential fraud.

With Senzing, suspicious new and existing customers are flagged before processing, so you can make more informed decisions faster.

“Senzing entity resolution delivers better accuracy and fewer false positives than any other option, including other commercial products we looked at.”

Gurshish Dang
Head of Enterprise Data Management

Verisk senzing entity resolution
READ VERISK CASE STUDY

Detects All Types of Insurance Fraud

 Icon png 78 insurance industry -Property & Casualty Claims Fraud

 Icon png 7 insurance industry -Policyholder Fraud

 Icon png 37 insurance industry -Insurance Claims & Transaction Fraud

 Icon png 38 insurance industry -Application Fraud

 Identity verification entity resolutionIdentity Theft & Synthetic Identity Fraud

 Icon png 99 insurance industry -Agent/Broker Fraud

Supercharge Your Insurance Fraud Detection  

The Senzing API ingests data from your claims management system and then feeds resolved and connected data to your claims fraud detection system to generate alerts. By resolving entities related to a claim and discovering connections between claims, policyholders and third parties, Senzing provides data insights to your fraud system that it uses to:

  • Detect Duplicate Claims

  • Discover Fraud Rings

  • Identify Cross-Policy and Cross-Insurer Fraud

  • Prevent Ghost Brokering

  • Detect Multiple Claims for the Same Incident

  • Reduce False Positives

  • Reduce Financial Losses 

  • Improve Investigation Efficiency  

  • Identify Synthetic Identities

Enabling Better, Faster & More Affordable
Risk & Fraud Detection 

The Senzing API makes it easy for developers to add the world’s most advanced entity resolution with relationship awareness to their enterprise systems. You can deploy the Senzing API on-prem or in the cloud. It comes pre-tuned and pre-trained and actively learns to deliver highly accurate results immediately.

 Minimize operational complexity Enhance Efficiency in Fraud Investigations:

By identifying and consolidating duplicate customer profiles and incorrect data, Senzing resolves data to provide investigators with clear, actionable insights to resolve cases faster.

 Icon png 85 insurance industry - Reduce Financial Losses from Risk Fraud:

Senzing relationship awareness reveals connections between seemingly unrelated entities. These connections may indicate fraud rings, fraudsters who are obfuscating their identities, or suppliers connected to countries, people or organizations you don’t want to do business with.

 Icon png 70 insurance industry - Maximize Data Quality and Accuracy:

When integrated into an insurer’s enterprise info security applications, Senzing principle based entity resolution improves data quality and accuracy, including the identification of unintentional data errors.

 Icon png 67 insurance industry - Resolve 3rd-party Data to Enhance MDM:

When using new data (like from a credit bureau) to improve an MDM system, Senzing is used to avoid creating duplicates and introducing new risks into the system. Senzing resolves dirty or messy data and reduces false positives.

 Icon png 63 insurance industry - Improve Customer Experience:

By reducing false positives in fraud detection, insurers can provide legitimate customers with smoother interactions and quicker problem resolution, leading to increased satisfaction and loyalty.

Unlocking Critical Capabilities For Insurers 

The revolutionary Senzing API utilizes advanced algorithms to match policyholder and claimant data across attributes and data sources, even despite messy data, enabling insurers to:

 Icon png 100 insurance industry - Resolve Multiple Data Sources for PII and PHI Data

Easily combine PII and PHI data with third-party data for more comprehensive fraud detection insights while maintaining strict data privacy and security standards.

 Entity resolution social network analysis Discover Relationships and Networks of Entities

Visualize complex networks of related entities to discover hidden and non-obvious relationships and networks.

 Icon png 34 insurance industry - Find and Prevent Duplicate Data and Data Collisions

Reduce the risk of duplicate policy information, inconsistent claims data, and duplicate or multiple policies by proactively identifying and avoiding data collisions.

 Icon png 28 insurance industry - Mitigate Synthetic Identity Risks

Analyze patterns and inconsistencies across multiple data sources to detect synthetic identities that would otherwise slip through traditional verification processes.

 Icon png 1 insurance industry - Detect Organized Insurance Fraud Rings

Identify complex networks of related entities that can reveal organized fraud rings that operate across different geographic areas or insurance products.

 Icon 118 insurance industry - Identify and Minimize Data Entry Errors

Find discrepancies in policyholder information across different entries or systems, ensuring accurate records and preventing issues caused by data input errors.

Verisk case study senzing entity resolution for insurance
CASE STUDY

Verisk Deploys Enterprise-Wide Entity Resolution Service

Verisk, a global leader in insurance analytics, faced challenges with multiple entity resolution systems across its organization. To standardize data governance and increase efficiency, Verisk implemented Senzing entity resolution as a service across the enterprise. 

Their initial cloud deployment loaded more than 1.6 billion records representing over 420 million unique identities and delivered sub-second response times for searches. Verisk has seen significant improvements in data accuracy and fewer false positives compared to previous entity resolution systems. Implementation was fast and efficient, using only five team members and requiring minimal data preparation.

READ THE CASE STUDY

Senzing provides us with greater savings and efficiencies while improving our analytics and reducing risk across the organization.


Gurshish Dang, Head of Enterprise Data Management, Verisk

Verisk senzing entity resolution
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