Top applications for ML in insurance

According to Accenture, the top areas for ML application include insurance advice, fraud prevention, claims processing and risk management.

Insurance advice & information The ability to assist consumers at every stage of the buying process is useful for achieving the desired goal of customer acquisition and retention through guidance. A big part of this is the use of chatbots on messaging apps, as ML algorithms analyze data to help resolve things such as claim processing inquiries as well as answering basic questions.

Fraud prevention Property & casualty fraud steals an estimated $30 billion every year from the insurance industry and occurs in nearly 10% of all property-casualty losses. ML identifies and flags potential claim situations early in the claims process, allowing insurers to promptly investigate and correctly classify whether a claim is legitimate or fraudulent.
Claims processing From the initial claim submission to reviewing coverages and scheduling on-site adjusters, the claims process is a labor-intensive and time-consuming task. By leveraging ML technology, adjusters can reduce processing time and costs. ML can also be an effective tool for insureds to use to check the status of their claim without having to contact their broker or adjuster.
Risk management ML has become a vital tool in loss prediction and for accurately establishing policy premiums. By taking data and designing algorithms that can instantly detect possible abnormal or unexpected activity, ML can help to significantly mitigate risks. An example would be usage-based insurance devices, more commonly known as telematics, which bases auto insurance rates on specific driving skills or behaviors.

Getting started with ML

As more insurers invest in ML capabilities, they need to keep in mind the requirements needed for successful implementation and usage. While every company is different, the following presents general areas of consideration when it comes to ML implementation.

Start with reliable data ML is a tool that can't effectively work its algorithm magic to the benefit of a company if it's fed outdated or incorrect data.
Begin small and work your way up In most cases, it is counterintuitive to tackle larger ML projects immediately. Starting with smaller and very specific areas where ML can be a benefit will be easier to manage and allows companies to work out kinks before investing in a much larger project.
Establish teams in various business areas — not just IT Having an umbrella of individual experts assigned to specific areas of the ML process can immediately determine how to best address and achieve the effectiveness and efficiency of a project by keeping proposed objectives at the forefront.
Be specific in your objectives What problem does your company need to solve? What's your biggest pain point? What are the top acquisition issues and/or roadblocks standing in the way of launching a new product or service? Being laser focused on the top priorities or issues allows individual business teams to apply the maximum degree of effectiveness and accuracy to a specific project that goes beyond simply monitoring online percentages or the number of website visitors.

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.

Special Reports /

Machine learning: Practical applications for the insurance industry

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