Claims analyzed
Potentially suspicious claims identified
Fraudulent claims identified
The customer is a specialty insurance company that wanted to understand trends in their data and prepare a model to identify potential fraudulent claims in their workers compensation line of business.
Microsoft SQL Server
Python
TensorFlow
Tableau
Keras
Auto Encoder
Quantiphi leveraged various types of the customer’s claims-related data, such as Claims note, Claims details, ISO match and Loss run, to train a machine learning model and build a rule/indicators based system to tag potentially fraudulent claims based on a set score. Additionally, we built an unsupervised machine learning model to find anomalies in the data. A Tableau Dashboard was also built to showcase which claims are detected as suspicious.
