Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims
Company Size
1,000+
Region
- Europe
Country
- France
Product
- Dataiku Data Science Studio (DSS)
Tech Stack
- Python
- Scikit-Learn
- R
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
About The Customer
Santéclair is a subsidiary of several supplementary health insurance companies, including Allianz, Maaf-MMA, Ipeca Prévoyance, and Mutuelle Générale de la Police. They support the health care of more than 10 million beneficiaries, helping to cover optical, dental, and aural expenses as well as dietetic and orthopedic services. For more than 13 years, Santéclair has proven their expertise in risk management, benefiting more than 50 health insurance companies. They are based in Europe and operate in the financial services industry.
The Challenge
Insurance organizations are constantly exposed to fraud risks, including false claims, false billings, unnecessary procedures, staged incidents, and withholding of information. Santéclair, a subsidiary of several supplementary health insurance companies, was struggling with fraudulent reimbursements from both opticians and patients. They lacked a system that could effectively analyze the right data and adapt to increasingly sophisticated fraudsters. Instead, they relied on “if-then-else” business rules to identify likely fraud cases, which resulted in the manual audit team spending their time on too many low-risk cases. With the increase of reimbursement volume (more than 1.5M a year), they needed to improve their efficiency and productivity.
The Solution
Santéclair found a solution in Dataiku Data Science Studio (DSS) via a POC led by the IMT TeraLab platform. Eulidia, a data consulting agency, produced an algorithm using Dataiku to help the manual audit team identify more fraud by feeding them cases with a high likelihood of actually being fraudulent. The solution involved outsmarting fraudsters with advanced machine learning algorithms that continually update and automatically learn or retrain using the latest data so that any new fraud patterns are immediately identified and audited. Dataiku handles the entire workflow, from raw data to exposing the predictive model to the operational applications. The solution also involved automatically combining hundreds of variables from different datasets, including patient/prescriber history, interaction graphs, prescription characteristics, and other contextual data.
Operational Impact
Quantitative Benefit
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