Graph analysis for insurance and financial companies
Graph analysis is an effective analytical strategy for detecting fraudulent insurance claims.
Graph analysis in insurance
Typically, identifying fraudulent insurance claims relies on business rules and peer reviews, making the process time-consuming and expensive. Business rules flag suspicious claims, which are then sent to experts, who determine whether the claim is fraudulent or not. However, these in-depth investigations are time-consuming and costly.
Insurance fraud detection models that incorporate graph analysis track the most suspicious claims, which are sent to examiners for further investigation. In this way, examiners can focus solely on claims with a high probability of fraud and not waste resources investigating non-fraudulent claims.
Graph analysis has a wide range of applications in various business areas
Ways of application
Identifying fraudulent groups that receive insurance benefits
Identification of fraudulent behavior, data enrichment
Early fraud detection
Countering concealment of source of income
Database organization, bill of materials analysis
Information structuring
Socio-demographic factors, purchase histories, etc.
Customer portrait and churn forecasting
Analyzing vulnerabilities in information systems
Hacking defense
Document clustering, extracting meaning from data
Intelligent text processing
Realized projects that solve our customers' problems
Our cases
We have created a service that effectively detects and predicts fraud at the contract conclusion stage. The product analyzes and compares a number of parameters: policyholder data, type of cargo, etc.
Recognizing and preventing insurance fraud
Scheme of cooperation
1. Discuss the idea and possibilities
2. Draw up a work plan, approve next steps
3. Mainstreaming and harmonization of results
4. Introduction of new product and monitoring of product performance
