Use Data to Foster Innovation in Your Insurance Business
Data has always played a central role in the insurance industry and the amount of data insurers have access to increases exponentially. Innovative data analytics technologies centered on artificial intelligence (AI) and machine learning (ML) can add more value to your business and place your company ahead of the competition.
This 28-page white paper will help you understand the potential gains of introducing ML into your business operations.
You’ll read about:
- The role of data in the insurance industry
- Use cases of ML in insurance
- Steps of ML implementation
- And how to create the most efficient ML solutions
With competition heating up for insurance companies, becoming a data-driven business seems the only reasonable solution. Read the document and discover best practices for transforming your insurance business with the help of machine learning.
FAQ
There are many use cases for artificial intelligence and machine learning in insurance. They include but are not limited to fraud detection, churn prediction, claims management, intellgent underwriting, lead management, price optimization, and others. In the white paper, read about each of these in detail.
This totally depends on the business problem the insurer is going to solve. For example, in fraud detection, the most popular machine learning algorithms to use are decision trees, random forests, logistic regression, and neural networks. For personalized marketing and sales in insurance, data scientists would likely choose clustering algorithms, regression analysis, and deep learning.
They sure do. Machine learning can help to unleash the potential of big data, and insurance companies are usually sitting on volumes of data. The 2019 LexisNexis Survey of the top 100 US insurers proved that more than half of insurance companies (66%) are investing in machine learning right now as part of their digital transformation.