For businesses in the digital age, data might be more valuable than gold or oil, as raw materials that can be refined and used to develop successful products and services. This is especially true for the insurance business, where data analytics are becoming increasingly more important with the vast amounts of data needed to develop future insurance products and assess claims.
This article will discuss the importance of the data analysis process in how to improve insurance customer experiences (CX), including how to implement the right data strategy and corresponding business solutions.
Insurance companies are trying to develop InsurTech, or Insurance Technology, to win the hearts of customers and drive business success.
We’ve previously covered how important InsurTech is to the insurance sector. In InsurTech, insurance data analysis is considered one of the important functions used to create a better and more personalized experience.
According to market research firm Mordor Intelligence, the global insurance data analytics market will grow to US$27 billion by 2029, with a compound annual growth rate (CAGR) of 15.90%, with Asia Pacific as the region that is anticipated to grow the fastest. Understandably, companies in this industry are willing to invest heavily in order to implement the latest findings and technologies in InsurTech. Read on to learn more about how they will do this, and why.
Insurance fraud is a prevalent issue in the industry. The National Insurance Crime Bureau (NICB) of the United States reveals that 10% of all insurance claims are fraudulent. This might originate from the insured party themselves, or in collusion with a third party, or from field staff conspiring with the insured to make fraudulent claims.
Because of this, insurance companies end up compensating fraudulently exaggerated claims. It causes the loss ratio and the loss adjustment expense ratio to increase unnecessarily. Having a data analytics system helps insurance companies search for information from various sources in the process of detecting fraud more quickly and accurately. This can be more efficient in reducing the rate of fraud than random inspections, or wasting time and resources manually searching for documents, as was done in the past.
Insurance companies can use customer data and statistics to analyze and evaluate individual risks. When combined with simulation models, this helps calculate insurance premiums that are most appropriate to the level of risk. Insurance premiums are more accurate and fair, and your company prevents unnecessary loss of reserves and resources.
A good data analysis system also helps automate more workflows. Gone are the days of guesswork and manual data entry that may lead to incorrect calculations. Insurance data analytics can help improve existing insurance policies to accurately reflect the actual risks, and make designing new insurance policies more efficient.
Additionally, implementing usage-based insurance (UBI) or charging insurance premiums based on actual usage can help customers feel they’re receiving more value for the insurance premiums they pay. Examples of UBI include using IoT devices with motion detection sensors, devices that detect engine ignition, and pairing devices with GPS tracking to calculate insurance premiums based on the driver’s mileage and behaviors.
An example of data-empowered insurance in Thailand is Thaivivat, a leading auto insurance provider in Thailand, offers pay-per-use car insurance. Customers pay insurance premiums based on the actual time spent driving, by connecting the TVI Connect device to the car’s USB port. Another example is an innovative InsurTech product that lets customers pay insurance premiums according to the actual period of use of the vehicle through an application on your Smartphone by using the TVI Connect device which is powered by the car's USB port. Leading Thai companies like SCB and TIP Up2Mile also offer pay-per-mile insurance, which doesn’t require additional equipment. In pay-per-mile insurance, premiums are charged according to actual distance driven, such as every 5,000 km. or 10,000 km.
When information from insurance claims documentation and systems are recorded digitally, connected online, and are updated real-time via the cloud, it becomes much faster and easier to analyze data for claims processing. By eradicating the need for manual management, the entire claims process can be completed in a matter of minutes.
Analysis of customer and market data helps insurance companies with lead generation. This form of insurance data analysis identifies how to draw potential customers to read the details of your insurance policies, and make the decision to purchase your insurance products instead of your competitors'. Data analytics offers visibility of key metrics such as your customer acquisition cost (CAC), which prove invaluable to your marketing team when planning more impactful content and personalized marketing campaigns.
When your company has insurance products that respond to the needs of your customers, they can be executed quickly, with fairer insurance premiums. This creates a better customer experience, increases satisfaction with the services provided, and has the potential to improve renewal rates, or encourage customers to buy additional insurance products in the future. It also increases the chances of referrals. All these factors drive your business’s success in the long run.
These factors emphasize that collecting customer data – whether in person or on the cloud – and analyzing that data without a well-defined plan is no longer sufficient to keep up in the insurance sector. Here’s what effective data analysis should look like.
Seven Peaks is not only a consultant in digital transformation. We also have deep experience working with insurance data analytics and leading InsurTech businesses both in Thailand and abroad. We are a trusted technology partner for global InsurTech companies, providing advanced data analytics solutions and implementations such as Mixpanel. We offer our clients complete data and end-to-end cloud solutions that elevate the quality of your data collection.