Dataiku Case Studies Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims

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	Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims Dataiku

Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims

Dataiku

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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 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.

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.

Impact #1	Enabled fraud detection teams to target actual fraud cases three times more effectively.
Impact #2	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.
Impact #3	Saved their customers a lot of money by decreasing fraudulent behaviors in the health network and excluding the fraudsters from the network.

Impact #1

Enabled fraud detection teams to target actual fraud cases three times more effectively.

Impact #2

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.

Impact #3

Saved their customers a lot of money by decreasing fraudulent behaviors in the health network and excluding the fraudsters from the network.

Benefit #1	3x more effective fraud detection
Benefit #2	Significant cost savings by decreasing fraudulent behaviors
Benefit #3	Efficiency improvement with automatic model updates and monitoring

Benefit #1

3x more effective fraud detection

Benefit #2

Significant cost savings by decreasing fraudulent behaviors

Benefit #3

Efficiency improvement with automatic model updates and monitoring

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Overview

Insurance Fraud Detection: Leverage Data to Accurately Identify Fraudulent Claims

Operational Impact

Quantitative Benefit