Emerging technology’s role in the future of the insurance industry
As an industry, we need to think bigger and re-evaluate the way companies operate along with the tools they’re using.
The global (re)insurance market is undergoing a transformation from manually run processes to a technology-driven business model, enabling companies to tap into the full potential that data has to offer.
A surge in fast-paced innovation and the utilization of machine learning (ML) with cloud-technology has enabled automation, streamlined efficiency and provided access to more comprehensive analytics fed by this data.
While more than two-thirds of individuals believe that the use of emerging technologies could dramatically increase performance in their organizations, a recent Deloitte Survey noted that less than 30% of organizations are actually deploying these tools — creating a missed opportunity for many (re)insurers.
The industry as a whole believes that these emerging technologies have the ability to unlock hidden value, especially from data, but the complexity and sophistication of these tools have made their use limited to a select few that have the adequate skills and resources.
Now, however, new advancements in ML and cloud technology are helping underwriters, actuaries and executives alike access and use the data, making it possible to achieve business growth and underwriting profitability.
These technologies provide companies with a competitive edge, but the question remains as to how long it will take for data processing automation to become the standard for property & casualty (P&C) insurers.
Looking to machine learning in the cloud
In 2018, net premiums recorded by the P&C industry totaled $618 billion, an increase from the 2017 net premium totals of $558.2 billion. Pressure to grow will continue but there will be even greater pressure to achieve underwriting profitability, especially in the current economic environment.
Companies will look to deliver underwriting profitability by applying traditional approaches like improving operational efficiency, reducing costs, improving risk selection, or any combination of the above. Many would declare a two-times lift in operational efficiency as a victory, but that will not be enough to adequately prepare for the future.
Companies need to think about improving how they operate by an order of magnitude. The challenge is that traditional approaches will not enable companies to operate 10 times more efficiently.
As an industry, we need to think bigger and re-evaluate the way companies operate along with the tools they’re using. To achieve an order of magnitude change in how companies operate, they must begin with how they think about data. Data is the lifeblood of any organization, so why isn’t it always treated as such?
More importantly, why is it so difficult for companies to provide actuaries, underwriters and stakeholders of the business with the right information at the right time? Data analytics leaders at global insurers make significant investments in resources to process and prepare data across an organization, with some indicating that they spend up to $80 million in data analytics each year.
This mindset of spending valuable time and money to process data reveals how companies orient towards data and are aware of its importance. The question is not whether data is valued, it’s when is it considered valuable? Companies today are reactionary, spending a lot of time and money preparing data to meet external pressures and fit for purpose exercises, instead of investing in solutions that can help companies be proactive – but that perspective is shifting.
Now, it’s possible to drastically change the way companies reduce the time and costs needed to incorporate and process data earlier on in the process. By automating processes with ML, companies are gaining access across the entire organization to more robust information.
Treating data as an asset
Today, data is typically considered a cost center by most (re)insurance companies instead of a critical asset. For many classes and lines of business, the level of effort required to collect what is deemed “desired data” on all business is too costly, and existing systems simply do not have the capability or capacity to handle e additional “desired data” elements.
Because customer acquisition costs are high, companies are forced to collect critical data and information later in the process resulting in increased costs, gaps in knowledge, and missing insights and information which limits the amount of lift companies can achieve.
Treating data as an asset requires companies to start thinking about capturing the “desired data” the moment any business enters their organization. For commercial property writers to achieve this, they would need to start capturing the desired data for each and every submission - which is an order of magnitude increase over the 10 to 15 data elements they currently capture today. Many have difficulty comprehending this because it’s not possible with traditional technologies.
Achieving this magnitude of lift would fundamentally advance how companies could operate. It would change how commercial property underwriters would screen, triage and select the business they write. Companies could proactively open more production sources and seek business that performs well across its producers. Furthermore, it would enable pricing actuaries to create even greater product differentiation and empower underwriters with insightful information earlier on in the process.
Achieving an order of magnitude change in how a company operates and how a company thinks about data is achievable today using ML and cloud technologies. Combined, they enable companies to achieve profitability, even under the current economic conditions. The advances made in ML eliminate the need to manually screen submissions and enables companies to capture the desired data across each and every submission.
The ability to process and intake this data faster makes it possible to gain more comprehensive insights and make strategic growth decisions for the business. Utilizing ML in the cloud makes data and the insights it provides access to anyone across the organization.
Staying a step ahead in the future
With the inevitable shift, the market is poised to continue to undergo in the next five years, P&C insurers will be able to extract real-time and accurate data on the loss exposure of individual consumers. This will help them proactively respond with timely and highly personalized interventions.
These emerging technologies enable companies to increase data integrity by automating data transformation and preparation, from modeling, structuring and pricing, and performance analysis. Different stakeholders can easily access the specific information they need, when they want it. Companies are able to scale and plan for the future by accessing data that is relevant and up to date.
This ultimately provides companies that adopt these emerging technologies with an edge over their competitors because they can provide themselves and their customers with options and insights.
P&C companies are starting to understand the true value and potential that data offers, and the role it plays within business growth and profitability. Accessing this desirable data can only come from making changes that provide significant lifts in operational efficiency.
Introducing ML is revolutionizing the way (re)insurers operate, making access to data easier and faster than ever before. Companies who delay the incorporation of these technologies in today’s fast-paced environment will be left behind. The time to act is now.
Scott Quiana is head of products, marketing and partnerships at Quantemplate. He can be reached through LinkedIn.
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