Predictive analytics can help insurance companies design their strategies for marketing and pricing as well as streamline processes, such as underwriting, claims, and reserves.
The insurance business deals with covering risks through monetary premiums. Actuaries are responsible for setting the right price for insurance policies and methods have evolved over the last few centuries. Historically, statistical methods were involved, and each insurer developed their models for assessing and hedging risks.
Predictive analytics is, therefore, a natural evolution of this traditional approach which uses modern tools like machine learning and big data analysis. It looks at historical data, tries to find patterns in consumer trends and create risk assessment models based on real-life events rather than estimates.
Uses for Predictive Analytics in Insurance
Most insurance providers leverage the use of predictive analytics in multiple areas to get a competitive advantage. The motivation for this choice lies in its ability to predict outcomes and behavioral changes.
Applications range from general to very specific to the insurance industry, but the methods are similar to other activity sectors. The most common uses are listed below.
Marketing Strategy
A great marketing strategy aims to bring the right message, to the right customer, via the best channel, in prime-time. Predictive analytics can do this by naturally segmenting customers into clusters. The difference from traditional marketing approaches resides in the fact that predictive methods look at patterns embedded in data while not trying to fit recordings in predefined categories, usually based on demographic and sociographic distinctions.
This way, the products can be targeted to an existing group from the market. It’s an inside-out approach which will make clients feel more connected to the company. It’s no longer a matter of designing insurance for a 24-35-year-old white male with medium-high income.
Now, the algorithm could say that a group of college-educated people aged 22 to 33, with a multi-cultural background and a passion for extreme sports who usually stay at Airbnb would be ready to pay $800 per year for car insurance and prefer to be contacted via instant messaging on Friday afternoon. Customization is the key to sell more. The goal is to make each client feel that the offer was created with their needs and lifestyle in mind.
Competitive Pricing
In the previous example, the price emerged as a combination of multiple factors. Previously only the value of the insured object and the probability of the risk would go into the model as determining factors.
Right now, through predictive analytics, it’s not only about the projected loss and possible claims, as numerous other dimensions shape the final number. Dynamic models are preferred. These look at the client’s unique features, such as risk predisposition, but also at the competitions’ offer for the same input data. It’s not a matter of starting a price war but of remaining reasonable for the market you serve.
Underwriting Process Improvement
Underwriting is the delicate process of assessing the risk posed by each client and coming up with a price for the insurance policy which is fair towards both parties, insurer, and customer.
Predictive analytics can be useful in assessing the risk class for each potential client. This is also done through clustering techniques taking into consideration not only individual scores for different risk factors but the synergy between these.
For example, while smoking or having a heart condition are red flags on their own, a predictive model which combines the two and adds age can be far more accurate. The difference comes from the fact that most of the times, individual risks multiply if they are present in the same individual, giving rise to a risk of a very different magnitude.
Claims Processing
Claims processing should be straightforward and not involve any variability. The client presents the insurer with the policy and the proof that the unfortunate event occurred and collects the money. However, in this simple process, there is still room for variability and fraud.
Predictive analytics experts from InData LabsOpens a new window state that technology can help insurance companies prevent fraudulent claims by analyzing all the circumstances involved in the event. For example, factors, such as speed, visibility and the usage degree for tires could tell a different story in the case of an accident than simply reporting it. It could make the difference between the fault of the client and a genuine misfortune.
Funds Reserving
A cornerstone aspect of the insurance business is computing the amounts needed for reserves. This means subtracting from the turnover the value required to sustain the claims before calculating the projected profit. Reserving is another domain where predictive analysis can give an insurance company the upper hand in business.
By looking at historical data, computing trends and extrapolating the result, insurance companies can approximate with outstanding competence the amounts needed. The most used methods include the Chain Ladder MethodOpens a new window (CLM) and the Bornhuetter-Fergurson MethodOpens a new window (a generalization of the previous method).
Success Factors
In the insurance business, success is built on finding the right balance between risk and claim payments as well as between cross-selling and up-selling. The new generation of clients, mainly Millennials are used to highly customized services, regardless of the industry. To get their attention and wallets, insurance companies need to keep up with times and change the personal agent approach to a highly customized service available online, 24/7.
Customers now want to have product configurators which create policy offers for their specific situation. Off the shelf, offers are no longer satisfying as modern life is led in many different lanes.
Future Developments
Business intelligence services powered by Big Data will create flexible risk assessment models with a vast number of input fields which distill into a single number: the price paid by the client for the policy. The tendency is to include as many variables as possible and to make this process as easy as possible.