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The insurance industry has always relied on data to calculate risk and come up with personalized ratings. Today, the sector is undergoing a profound digital transformation thanks to technologies such as machine learning.

Insurers are using machine learning to increase their operational efficiency, boost customer service, and even detect fraud. And there is a plethora of insurtech startups, eager to take a slice of an insurance industry.

Here are 6 ways machine learning is transforming the insurance industry.

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Note: this post has been updated, to reflect the progress of new technologies and business moves by companies mentioned in the article. 
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1. Automation and process improvement

The insurance industry is regulated by specific legal requirements. It processes thousands of claims and responds to even more customer queries. Machine learning can improve this process and automatically move claims through the system – from the initial report to analysis and contact with the customer.
In some cases, these claims may not require the work of human employees at all, allowing them to dedicate more time to more demanding claims. Insurance companies are already automating some parts of their claims process, benefiting from significant time savings and increased quality of service.

For example, Captricity has developed algorithms able to extract handwritten or typed forms into a digital form with a smashing 99.9% accuracy, helping insurers to reduce cycle times.

On February 2019 Captricity announced Captricity READ, the first AI-powered software that, according to company’s claims, outperforms humans at reading handwriting.

That is a watershed moment for digital automation. Enterprises have struggled with automation technology due to extremely low accuracy in reading handwriting and poor-quality images. (…) Captricity READ is the missing link – said Kuang Chen, founder and CEO of Captricity.

Lemonade is an insurance company that uses AI to process claims more quickly and provide customers with fast payouts using various applications, such as a chatbot.

That is one of the fastest growing company in insurance space – Lemonade raised $780 million already, and is using the funding to expand into Europe(with the Germany as their first market).

“The value proposition we’ve created resonates with young consumers in a universal way,” said Daniel Schreiber, founder and CEO of Lemonade, as quoted on TechCrunch.

Another example is the technology provided by Tractable, a startup that offers software for assessing damage and predicting repair costs to accelerate claim processing.

On April 2019, Tractable hosted webinar to showcase their technology – the company invited participants from all over the world to submit photos and evaluate the results of its visual algorithms.

2. Data insurance – more sophisticated rating algorithms

Rating serves as the foundation of insurance companies. There’s a famous saying in the insurance world: “There are no bad risks, only bad pricing.” That means companies are able to accommodate most risks as long as they find a good match in pricing.

However, many insurers still rely on traditional methods when evaluating risk. For example, when calculating property risks, they may use historical data for a specific zip code. Individual customers are often assessed using outdated indicators, such as credit score and loss history.

In this context, machine learning can offer agents new tools and methodssupporting them in classifying risks and calculating more accurate predictive pricing models that eventually reduce loss ratios.

An example of this is vehicle telematics – the combination of vehicles, computers, and wireless telecommunication technologies that facilitate the flow of information over vast networks. Here’s a case study of the Italian market which has the highest coverage of telematics-based motor policies in the world.

Another example is Zendrive, a mobile app that monitors the driving behavior of customers to potentially offer them significant discounts on car insurance premium.

Based on 60bn journeys’s data from 2018, Zendrive estimate that rate of smartphone use behind the wheel may be as high as 60% (source: The Economist).

– The current models of auto insurance – using proxy variables for responsibility like age, education, marital status, homeownership, etc. – have little direct impact on save lives on our roads – Noah Budnick, Data Practice & Policy Director at Zendrive told us.

– Zendrive wants to help communities achieve Vision Zero – the elimination of traffic deaths and injuries – and our technology can help identify the driver behaviors that are most likely to contribute to serious crashes, injuries and deaths. With this data, insurers can create policies to save lives on the roads – he added.

3. Improving underwriting

One area where machine learning can bring benefits in the process of underwriting is healthcare. Healthcare insurance provides coverage of costs incurred by disease, accident, disability, or death. This area of insurance stands to benefit a lot from data-driven approaches as the healthcare analytics market is on the rise.

Insurance companies need to provide better services and reduce their costs. They can now use machine learning-powered tools that help to consolidate insights from massive volumes of highly varied data such as insurance claims data, membership and provider data, benefits and medical records, and many others. These solutions can structure and process data to offer healthcare insurance businesses insights leading to costs reduction, higher quality of care, and fraud detection.

An example of such technology is Daisy Intelligence, which provides price suggestions for different customers based on their individual risk factors, such as age, location or even blood pressure.

On April 2019, the company has beaten 12 other startups to won the Canadian Fintech 3.0 Summit’s Future of Retail pitch battle.

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