Increasing market pressure is forcing insurers to react more quickly to changes. Most companies lack the tools to cope with this. Business-orientated implementation enables companies to make full use of available data.
Insurance pricing has become a matter of speed
Imagine you’re opening a stall with apples, and you need to set their price without knowing how much you’ll have to pay the farmer for them. And when you decide to sell them for $1, you’d definitely want the ability to change that price as quickly as possible. For example, if you find out the farmer’s price is $1.5. Or when no one is buying from you, because the stall around the corner is selling apples for $0.8, since the cost from the farmer is $0.1. Now, imagine you need a marker to write the new price, and the office supply store tells you that you have to wait 6 months to get it, because they are currently selling only printer paper, and you can’t suspend sales.
Welcome to the world of pricing in insurance, where determining the price is as challenging as in any other industry, and bottlenecks in IT cost millions. In a world where know-how in setting prices in a dynamic business environment is crucial for the company’s success. Fortunately, the rapidly growing technology can help insurers to find a sweet spot and arm the pricing teams with advanced tools to make them self-sufficient in launching new tariffs in a pace they need.
5 things that can help achieve optimal insurance pricing
Quick response to change is key to maintain profitability
We can point to several needs of insurers met by this trend: (1) competitiveness, (2) profitability, (3) a stable customer base, (4) data-driven decision-making and (5) optimal time to market for new/modified offerings.
- Improved rating and pricing strategies provide a competitive advantage. They enable insurance companies to offer more attractive and tailored pricing structures, which can help attract new customers, as policy cost remains a key decision factor in retail insurance.
- Effective rating and pricing models help insurance companies optimise premium rates. As a result, they accurately reflect risks, and by aligning revenues with potential claims, this contributes to the insurer’s profitability.
- Flexibly priced offers based on accurate risk assessments are perceived positively by customers. And this leads to their greater satisfaction and potentially – loyalty to their existing insurer. Who among us would not stay with a business partner that offers fair and competitive prices?
- Investing in evaluation and pricing capabilities involves the use of advanced data analytics. The phrase ‘data-driven decision-making‘ has been repeated like a mantra for a reason – it increases overall business intelligence and has a positive impact on strategy building.
- Modern pricing engines are implicitly fast implementations, i.e. short time-to-market. And in dynamic market conditions and in the face of fierce competition, the speed of response to change is key to maintaining product line profitability.
Insurers need tools to use new data sources
There are 4 main criteria that guide an insurance company in deciding on a (new) rating/pricing system: (1) time to market, (2) efficiency of the solutions used, (3) effectiveness in a changing environment and (4) response time to adverse conditions.
- Insurers do not want it to take months to introduce a new offering. And if it takes that long, it is usually because teams are working on anachronistic solutions such as spreadsheets, are struggling to feed data or are reliant on IT resources (in-house or from a vendor). Modern pricing engines mean that the business needs much less support in managing change.
- The share of claims and costs in premiums verifies whether the pricing strategy is effective. If it is not, solutions are needed to quickly modify premiums.
- If an insurer’s pricing engines are unable to respond within the time limits of the aggregators used for price comparison, it means that they lack efficiency. For the insurer, this translates directly into a loss of market share.
- Risk factors are changing. New data sources are constantly being made available. The modern insurer must have tools that can use this content for risk assessment almost ad hoc. And this will ensure that it can operate stably in a changing environment.
Modern pricing is key to overcome spreadsheet modelling
The aforementioned tools are developed based on years of experience and a deep understanding of the market. Constant and frequent feedback from insurers pricing teams is used to develop such solutions. As a result, they are equipped with numerous additional options, designed to accomplish for insurers results that are not achievable using spreadsheet modelling.
These include time-saving features such as predefined models, automatic tariff validation and modern integration with data sources. Then there is the advanced reporting layer, which helps to make quick and data-driven decisions. Finally, there are powerful yet easily scalable SaaS solutions with high performance in organisations of different capacities.
Pricing engines become popular in commercial lines
Although for some time the solutions in question were perceived as used mainly by private lines of business with large volumes and standardised products, a strong demand for modern pricing solutions that allow for data-driven decision-making has recently been observed also in commercial lines. Yes – rating/price engines greatly facilitate price comparisons, so in practice retail and SMEs benefit the most. Still, as the flexibility to set tariffs is highly desirable across all branches of the insurance industry, modern pricing tools are no longer the preserve of just a handful of them.
Implementing a new pricing system, insurers face 5 main challenges
The first challenge is technical integration to ensure high performance. The second is the labour intensity and complexity of creating new tariffs or migrating existing ones to the new solution. The third is knowledge transfer, meaning: learning the new tool – often the most difficult from the human standpoint. Time consuming and often frustrating – but so important – testing is the fourth challenge. And the last, but not least – pre-processing of the data, including cleaning and transformation, to enable the use of the introduced features and the real benefits of employing the new technology.
Tomasz Klukowski – Head of Products
Technology in Insurance: Facts and Comments
In InfoQ (USA):
To accelerate ongoing and future cloud migration projects, AWS has bundled its best practice recommendations for cloud migration focusing on the Internet of Things (IoT), Machine Learning (ML), and Serverless Applications.
“The new migration Lens of AWS will help insurers migrate faster, avoiding common issues. It doesn’t stop with planning guidelines but guides through all important areas of migration and helps to check if the solution is optimally designed. This initiative will have a positive impact on migration timeline and will decrease the optimization efforts necessary after migrations.”
Dominik Kamiński – Product Manager
In Argus de l’Assurance (France):
5 French bancassurance companies – Sogessur, BPCE, Pacifica, Cardif and CreditMutuel – join the insurance API platform Darva.
“There is a lot of movement in bancassurance. Clients prefer to buy insurance from banks, but regulators are not making it easy. Companies want to transform bancassurance now and prepare for the digital race.”
Patryk Nowak – Lead Consultant