As the use of predictive analytics and different artificial intelligence (AI) techniques sweep through industry after industry, many insurance companies are already steps ahead. And it shouldn’t come as a surprise as the benefits of advanced analytics within insurance are unquestionable.
A recent survey conducted by Willis Towers Watson among insurers already using predictive analytics revealed that over two-thirds had reduced issue/underwriting expenses and 60% credited the additional insights for increased sales and profitability. At the same time, AI is expected to reduce the time spent by humans on repetitive tasks by 60% and increase the output of human underwrites by 50%. Likewise, around 40% of those surveyed have plans in place to increase the organizational understanding of risk and utilize predictive analytics in existing risk models.
Even though insurance companies have enjoyed prosperous times during Covid19 as people are spending more time at home instead of putting themselves at risk somewhere, more and more insurers are taking the leap forward and adopting AI as the new normal. It clearly creates competitive advantage and many are realizing that late entry into analytics can make it hard for some to ever catch up, like McKinsey has repeatedly emphasised.
The surge in AI usage among insurers in 2020 came as no surprise, and the next two years are expected to be groundbreaking in terms of predictive analytics within insurance.
Predictive Fraud Identification
Fighting fraud has always been part of the industry, but insurers aren’t being as successful as they could. Insurance Europe claims that detected and undetected fraud is considered to cost European insurers €13bn annually.
By using predictive analytics, insurance companies can better identify potential frauds and take proactive actions which can significantly reduce cost. For example, AI-powered clustering techniques can identify outliers that don’t fit the norm and alert insurers that something odd might be taking place, allowing them to proactively investigate before things become costly. Because where humans fail, artificial intelligence and big data can succeed.
Predictive Outlier Claims
Claims that unexpectedly turn into high cost losses are often called outlier claims. The utilization of sophisticated predictive analytics can alert insurers automatically about potential high-risk claims. The prediction model will learn from previous incidents and single out those that are likely to become high-loss.
Furthermore, the prediction models don’t have to start working after claims have been filed. In fact, predictive analytics can use historical data on outlier claims and help carriers take preemptive measures in dealing with similar incidents in the future.
Predictive Customer Churn
Since the onboarding process can be long and costly, retaining customers should be a priority. But knowing which customers are likely to cancel or are seeking to lower coverage is merely an impossible task without AI. Most of them won’t ever inform the insurer of their dissatisfaction or if they’re searching for alternatives.
Using predictive analytics to identify customers who may be unhappy and are likely to leave can dramatically reduce customer churn. This allows carriers to proactively reach out to those customers and provide a more personalised approach in order to align the service offering towards their needs. Without sophisticated analytics, insurance companies could overlook important signs and miss the opportunity to retain clients.
This also comes down to the very core of the business, where insurers fail to stay relevant to their customers which speeds up the exit of even the most loyal subjects. According to recent research by InMoment about 50% of customers say they’ve left a brand - which they felt they were loyal to - to a competitor that fulfills their needs better.
As people get older, they move from one lifecycle to another. It’s therefore critical that insurance companies understand how their clients’ needs are changing over time. Predictive analytics help carriers identify these moments of change in their clients’ lives and allows salespeople to proactively reach out to them in order to offer them products that better serve their needs.
Sales teams can be provided with a prioritized lists every week with clients that are most likely to to accept certain offers if contacted. Since it costs ten times more to sell to new clients than current ones, using predictive cross-selling technology can both increase sales as well as customer satisfaction.
Data is the new oil, they say. And rightfully so, as data is the most valuable resource any company can tap into. Too many are not utilizing data as much as they could and are therefore leaving lots of money on the table. Predictive analytics has proved to be critical for insurance companies to take the most advantage of their data by giving information, providing insights, and clearing the path for quick and efficient decision making.