Today, more insurers are exploring the use of ML to drive strategic, automated applications in nearly every sector of the insurance industry. And with most insurers utilizing a mere 10-15% of their accessible data, there remains unlimited untapped potential for ML to improve processes across the insurance value chain. (Credit: Lee/stock.adobe.com) Today, more insurers are exploring the use of ML to drive strategic, automated applications in nearly every sector of the insurance industry. And with most insurers utilizing a mere 10-15% of their accessible data, there remains unlimited untapped potential for ML to improve processes across the insurance value chain. (Credit: Lee/stock.adobe.com)

One specific area of advanced technology being used extensively across the insurance value chain is machine learning (ML), which is helping insurance companies save time and money by improving operational efficiencies in areas such as fraud detection, claims management, quoting, billing and customer service. But what exactly is ML?

Machine learning is a subfield of artificial intelligence (AI). Its job is to analyze data so computers can learn from and use information in the identification of specific patterns — all with minimal human intervention or having to program an entire system. As data continues to be produced, ML solutions adapt autonomously, learning from new information as well as from previous processes. An example would be an insurance chatbot or an online insurance quote.

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