The business model that insurance companies follow is broken, and it is harming the industry, its agents, and its consumers. The industry is stuck offering the same products based on previous ones and drowning its employees and potential customers in an avalanche of paperwork. As a result, the policies and products they offer to clients are outdated and ill-equipped to meet the modern insurance needs of most Americans. The antiquated practices also mean that everyone involved is losing money. Not only are companies and their agents losing new business and income, but current policyholders are paying higher premiums than is necessary.
The solution is readily available, and most companies already have all the necessary pieces in place. They just don’t know how to best use them. Insurance companies have access to thousands and thousands of points of data. Data that could help them create better, more inclusive products for a wider variety of people and reduce the amount of time that many people have to wait to get insured. The data is there, ready to be analyzed, but legacy insurance companies have been hesitant to move into the digital age and instead content to rest on their laurels. Artificial intelligence and machine learning can help them make the most of the data and information they have to the benefit of everyone involved, from the consumer to the insurance agent to the president of the company.
Typically, it takes two to three weeks to get approved for life insurance. Data can help reduce the time from weeks to hours. As the system stands now, when you want to get insured, you have to go to a doctor or clinic to get a medical exam. You might also have to go to a lab where they will take your blood and a urine sample and collect lab results. Once those results are in, all of that information is sent to an underwriter who reviews it to determine what kind of policy you are eligible for and what kind of rate you should get based on your health. The coronavirus quarantines and shutdowns extended this wait time from three to four weeks to three to four months, and even now, many companies are still facing a backlog of clients who want an insurance policy but have not been able to purchase it yet.
For most people, the health data is already out there. They don’t even need to visit a doctor or nurse for a new health exam. If you’ve had regular health check-ups, that information can be accessed digitally, saving you time and money. A digital proxy can collect and analyze the data and automated lab results and can quickly produce a variety of underwriting possibilities – finding and suggesting options that a person combing through mounds of paperwork might not even consider. The digital collection and analysis of the data doesn’t replace the agents or underwriters but instead gives them new tools and processes to help them more successfully do their jobs which gives them more opportunity and time to connect with new clients and increase their bottom line. Because when you have more time to spend talking personally to a greater number of potential policyholders, you have more opportunities to sign on new business.
Another problem with the current model is that it discriminates against many segments of the population. Currently, most insurance companies have five classes of rates they consider when developing insurance options for you, whether you are male or female, whether you smoke or don’t smoke and what is your age. If you have an underlying condition like diabetes, you may automatically be denied or only offered an expensive, limiting policy. With machine learning, you can access much more data and use it to apply pricing mechanisms and create a spectrum so you can go from having five classes to twenty, which means you can price risk more appropriately and offer policies to people who have not been eligible in the past, making it more equitable. In the long run, this also makes it more affordable for everyone since more policyholders paying insurance premiums mean less financial burden for everyone overall.
Not only does artificial intelligence enable insurance providers to broaden their spectrum and offer insurance to a wider range of people, but it also can help to create new products for agents to sell – ones that people want and need. Cloud-based artificial intelligence-assisted software can process 4,000 different kinds of data from 20 years of mortality, demographic, health, and government trends resulting in a totally different, more dynamic algorithm. With more data comes the need for new and better ways to analyze and process it. To get the most out of the plethora of data, insurance companies will need to have a robust data management plan in place. A Data Science-as-a-Service (DSaaS) platform can help insurance companies more precisely determine risk, create better, more appropriate products for their clients and improve customer experiences. This enhanced data can also help insurance companies price products more accurately, be able to understand the demographics of who is ready to buy, and get the right risk on their books – making them more profitable in the end.
Once the data has helped to create better products that people need, it is essential to incorporate agents’ input. While the products produced by data analytics are based on demographic and risk data, human agents are needed to bring a personal touch to review the product and to help curate and design new ones.
In the past year, digital adoption in almost every aspect of daily lives has increased tenfold. In our new digital world, data, and the ability to fully analyze it, is critical to any company’s survival, but that is especially true of the insurance industry. The information is already there. It is just a matter of using it wisely and correctly. Going forward, the businesses that will succeed will be the ones who can quickly analyze and adapt to data to make more informed, intelligent business decisions and serve their customers better and more expediently. Correct data used the right way, combined with an agent’s personal touch, is essential to a business’s survival.
Source: RTInsights
