Insurance companies that sell life, health, and property and casualty insurance are using machine learning (ML) to drive improvements in customer service, fraud detection, and operational efficiency. For example, the Azure cloud is helping insurance brands save time and effort using machine learning to assess damage in accidents, identify anomalies in billing, and more.
Here are some common use cases for ML in insurance, along with resources for getting started with ML in Azure.
Eight ML use cases to improve service, optimization, automation, and scale
- Lapse management: Identifies policies that are likely to lapse, and how to approach the insured about maintaining the policy.
- Recommendation engine: Given similar customers, discovers where individual insureds may have too much, or too little, insurance. Then, proactively help them get the right insurance for their current situation.
- Assessor assistant: Once a car has been towed to a body shop, use computer vision to help the assessor identify issues which need to be fixed. This helps accuracy, speeds an assessment, and keeps the customer informed with any repairs.
- Property analysis: Given images of a property, identifies structures on the property and any condition issues. Insurers can proactively help customers schedule repairs by identifying issues in their roofs, or suggest other coverage when new structures, like a swimming pool, are installed.
- Fraud detection: Identifies claims which are potentially fraudulent.
- Personalized offers: Improves the customer experience by offering relevant information about the coverage the insured may need based on life events, such as the birth of a child, purchase of a home or car.
- Experience studies: Uses unsupervised machine learning to discover predictors in claims activity. This information can help set assumptions and feed into activities such as pricing models, risk analyses, and other actuarial analyses.
- Scaled training: Use Azure to train your models using GPUs or thousands of CPU cores.
All of these use cases can be addressed machine learning.
Machine learning on Azure
Machine learning enables computers to learn from data through techniques that are not explicitly programmed. Insurers use artificial intelligence (AI) applications to intelligently process and act on data. Through this effort, organizations achieve more through increased speed and efficiency. Get started with these resources:
- Azure Machine Learning services enable building, deploying, and managing machine learning and AI models using any Python tools and libraries.
- Azure Data Science Virtual Machines are customized VM images on Azure, loaded with data science tools used to build intelligent applications for advanced analytics.
- Azure Machine Learning Studio which comes with many algorithms out of the box.
- Azure AI Gallery, which showcases AI and ML algorithms and use cases for them.
Recommended next steps
- Complete the Azure Machine Learning services quickstart. This gets you started with Azure Machine Learning Services.
- Scale your models with tips in the Actuarial risk analysis and financial modeling solution guide.
- Download the Machine learning algorithm cheat sheet to understand what the many algorithms do, and which is best for your scenario.
- Read How to choose algorithms for Microsoft Azure Machine Learning for more background on how to choose the right algorithms.
- Machine learning at scale AI/ML expert Paige Bailey shows the Azure ML services. Learn how to take your predictive model from development and into production. Dynamically train your model with streaming updates. Add real-time data to your models from IoT sources. Paige covers Azure DataBricks, Batch AI, KubeFlow + AKS, Stream Analytics, and Event Hubs.
- Read Welcome to Machine Learning Server for an introduction to the Microsoft Machine Learning Server.
- Review the Solution templates for Machine Learning Server for industry-specific templates, including one for insurance.
Source: Azure Updates