Real-Time Entity Resolution At Scale

Case management systems (insurance claims systems, benefits administration, claimsprocessing, etc.) are typically forms-based and have long processing times. Learn how real-timeentity resolution – the deduplication of similar data – can drastically help with creating acomprehensive view of your data. In case management, you’ll need to understand who filed forwhich benefits so that you can accurately assess fraud cases, eligibility requirements, and manyothers.  

Confluent has transformed how the 360-degree view of a person is created as the casemanagement systems by not only matching and aggregating information about a person usingadvanced machine learning models and master data management techniques, but also toprovide new capabilities like real-time fraud monitoring and notifications. Because Kafka allowsus to decouple and stream data changes efficiently, we are now able to ingest, match and storemaster data in a matter of minutes.  

Here are some of the other benefits:  

1. Real-time notification of case processing changes: case systems are able to sendnotifications to other systems as changes are being made in real-time  

2. Using Kafka as the storage for our application telemetry data allowed us to identify datagaps  

3. We are able to keep multiple data sources synchronized using Kafka, which provides aconsistent view of an identity to our UI and API users  

4. Data quality is improved upon and enforced, allowing access to either the raw or theconfirmed data  

5. Data freshness – we can monitor how fresh our data is through monitoring consumerlag through the Confluent platform  

6. Developer quality-of-life – access to ksqlDB for SQL-centric workloads and Kafka Streamsfor more advanced use-cases allows us to solve data processing needs using the righttool for the job and that flexibility also increases the maintainability and robustness ofthe system  

7. Data Security – topic ACLs and RBACs along with active encryption (the payload itself isencrypted) ensures that the security aspect of Data Governance is securely handled8. Advanced Confluent features such as Tiered Storage give us flexibility in terms ofcheap/efficient storage  

9. Native and easy-to-integrate connectors allow us to also provide the aggregated identityinformation to the enterprise Data Lake to ensure that the enterprise data analysts areable to use the tools they’re most familiar with