case study

Prediction of Claims Litigation Propensity

Insurance

Business Impacts

97%

Model Accuracy

85%

Precision in identifying cases that will result in litigation

86%

Reduction in features through feature engineering

Customer Key Facts

  • Country : America
  • Size : 1000-5000
  • Industry : Insurance
  • About : The customer is a large commercial P&C insurer and a subsidiary of a fortune top 20 companies

CCAI Insights and QA Automation using Gen AI

Electronic Caregiver® is a leading innovator in advanced TeleCare services, dedicated to enhancing treatment adherence, improving health outcomes, and extending functional longevity.

Electronic Caregiver aimed to automate their contact center by analyzing key performance metrics, including key drivers, end-user sentiment, and top support topics. They also sought to implement Automated Quality Assurance (AQA) for enrollment calls to new members and care outreach calls to existing members using a generative AI solution.

 

Challenges

  • Difficulty in analysing trends and insights in the available dataset
  • Difficulty in identifying attributes from the data to successfully predict the preopensity of litigation
  • Difficulty in developing a sophisticated AI based system for claims litigation propensity prediction

Technologies Used

Colab Notebooks

Colab Notebooks

Compute Engine

Compute Engine

Cloud Storage

Cloud Storage

Power BI

Power BI

Solution

Quantiphi team prepared a sophisticated AI-based claims propensity prediction algorithm with the following steps

  • Thorough data cleaning to ensure that the data is fit for modelling.
  • Performed feature engineering; including feature creation and attribute manipulation to ensure the most important features are selected
  • Extensive ML experimentation to ensure that best model is selected for propensity prediction
  • Model Explanation to showcase model decisioning

Results

High Model Accuracy

  • Achieved 97% model accuracy in predicting litigation propensity.
  • The model demonstrated 85% precision in identifying cases likely to result in litigation

Efficient Feature Selection

  • 86% Reduction in Features: Streamlined the feature set through effective feature engineering, improving model performance and efficiency.

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