Insurance companies are known to sell life, property, and health, etc. insurance to the people. They adopt machine learning in insurance which helps in improving their customer services, detection of fraud as well as the efficiency of various operations. Azure cloud is the best way through which insurance companies can study uses of machine learning. These can be used to assess accidental damages, recognize irregularities in billing, etc.
To learn better, here are some of the use cases of machine learning in insurance mentioned below:
- Assessor Assistant – this case is used when a car is towed to a shop. The use of a computer vision aids the assessor in identifying the issues that are needed to be resolved. This case helps in accuracy, helps the customer to be well aware of any kind of repairs, and also quickens an assessment.
- Lapse management – another uses of machine learning case is lapse management. It helps in identifying the policies which are probably to lapse. Moreover, it aids in how to inform the insured regarding the maintenance of the policy.
- Fraud detection – the case identifies claims that are possibly deceitful or fraudulent.
- Recommendation engine – the case when given comparable customers can help in discovering individuals which more or less insurance. The case helps these customers to seek the right kind of insurance policy of their existing situation.
- Property analysis – on the basis of the property images, property structure’s identification as well as any sort of issues. Guarantors can assist or help the customers mend the schedules by identification of issues. Moreover, they can also advise coverage when different structures are installed, for instance, the swimming pool.
- Experience studies – uses unverified machine learning helps determine interpreters in claims activity. The whole information can be very effective in setting assumptions and work into activities like pricing models, analysis of risks, and actuarial examination.
- Personalized offers – it helps in improving the experience of a customer through appropriate information related to coverage which an individual might be in need of. It could be related to life events like the purchase of anything, or the birth of a child, etc.
- Scaled training – using azure to train the individuals or models by means of the GPUs or CPU cores.
All the above-mentioned use cases are addressed as machine learning in insurance.
Here are some of the additional resources to adopt the uses of machine learning in insurance:
- It is better to download the machine learning algorithm cheat sheet. It helps in understanding the usage of algorithms and the best suitable for each scenario.
- Try to know about the machine learning server. This is a basic summary of the machine learning server of Microsoft.
- One should also get into the right algorithm’s selection of machine learning in insurance. It is the best way to learn about the best algorithm selection.
- Review about solution templates. These templates are industry-specific for insurance.
- Machine learning in insurance at scale. This is beneficial in transforming the predictive model from growth to making.