PRO BTP: Analytical tools used for real-time detection of suspicious claims for medical expenses
Customer Company Size
Large Corporate
Region
- Europe
Country
- France
Product
- IBM Fraud and Abuse Management System
- IBM Counter Fraud industry solution
- IBM InfoSphere
- IBM SPSS Decision Management
- IBM WebSphere Application Server
Tech Stack
- IBM Operational Decision Manager
- IBM i2 Analyst’s Notebook
- IBM PureFlex System
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Functions
- Procurement
Use Cases
- Fraud Detection
Services
- Data Science Services
- System Integration
About The Customer
Based in Paris, PRO BTP is France’s first social security provider for the construction and civil engineering industries. The group supports nearly 3.10 million members, providing products and services that include complementary retirement, non-life and life insurance, health services, savings, leisure and social activities.
The Challenge
PRO BTP is the social protection group for French building and construction professionals. It offers members (employees, retirees, craftsmen and construction companies) services in the areas of pension and health insurance (provident, health and savings). The firm had been using a processing system that was identifying unjustified health claims only after they had been paid. To reduce system abuses and better control expenses, PRO BTP needed to detect suspicious claims before the company reimbursed health professionals.
The Solution
Teaming with PRO BTP, IBM developed a secure service platform dedicated to detecting, categorizing and fighting fraud, service abuses and errors. Thanks to a detection engine validated by experts, this service platform, named Solon, analyzes optical and dental reimbursement claims in real time so that the firm can evaluate them before payment or before establishing a charges agreement. The platform features enriched predictive models that it can use to help detect fraud networks, and it is based on self-learning technology that pools detection schemes and takes advantage of an observatory watch.
Operational Impact
Quantitative Benefit
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