Download PDF
Dataiku > Case Studies > Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims
Dataiku Logo

Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims

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
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.
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 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
  • Enabled fraud detection teams to target actual fraud cases three times more effectively.
  • Reduced time-to-market for similar projects by making a POC in a few weeks and then industrializing the project within a few months with a low impact on the IT team, thanks to the production-ready components of Dataiku.
  • Saved their customers a lot of money by decreasing fraudulent behaviors in the health network and excluding the fraudsters from the network.
Quantitative Benefit
  • 3x more effective fraud detection
  • Significant cost savings by decreasing fraudulent behaviors
  • Efficiency improvement with automatic model updates and monitoring

Related Case Studies.

Contact us

Let's talk!

* Required
* Required
* Required
* Invalid email address
By submitting this form, you agree that IoT ONE may contact you with insights and marketing messaging.
No thanks, I don't want to receive any marketing emails from IoT ONE.
Submit

Thank you for your message!
We will contact you soon.