I always know that budget season is upon us when my clients ask, “What technologies and innovations are a must-have to stay relevant and competitive, and how do I keep up with these advances?”
Although I don’t have a crystal ball to predict the future, I have access to many analyst reports, technology and consulting publications, bloggers, and conversations with key industry players. These resources allow me to form a picture of what is in store for the coming year.
When it comes to the insurance industry, 2023 is trending toward an even greater focus on creating an intuitive and frictionless customer experience throughout the policy lifecycle, from purchasing to claims adjudication to enabling employees by reducing low-value manual efforts and simplifying their daily duties.
1. Digital Experience
The insurance carrier’s digital experience is judged by the consumer’s prior experience with another industry. As consumers experience a predictive frictionless experience in other aspects of their digital life, the demand for similar insurance experiences will increase. With the recent boom in digital-first insurance companies, the ability to switch insurance carriers is now much easier and can be done with a few clicks of a button.
Legacy insurance companies need to up their game to remain competitive. McKinsey estimates that by 2040, up to 55 percent of the underwriting, claims processing, invoicing, and record-keeping will be automated through artificial intelligence (AI). Although most personal lines provide a straight-through processing experience, most commercial lines do not due to underwriting complexity. Insurance companies are taking advantage of AI and machine learning (ML)’s ability to work with unstructured data to evaluate risk.
Suppose a beauty salon is applying for insurance. AI and ML can crawl through social media, photographs, and other digital footprints to confirm it is a real shop. It can determine the public disposition of the shop (e.g., did they have any significant losses, employee complaints, and so forth). These data points can be compared to third-party data to help make an informed underwriting decision.
Insurance products are becoming more dynamic in providing coverage in response to consumer demands and societal changes. With the start of Uber in 2009 and Door Dash in 2013, the concept of the gig economy connected via the internet changed traditional social views of work. As a result, insurance products had to adjust. Some fundamental insurance product offering changes include usage-based insurance data from phones and vehicle telematics, and cybersecurity products.
Working remotely from anywhere in the world requires new infrastructure and management techniques. The demands from marketing to drive a digital customer-centric self-service solution require new data and API-based solutions. New data, access, and response times are increasing pressure for the modernization of systems into a cloud-based solution with high levels of security.
This modernization is not only for the core solutions but also the integration of new insuretech to address specific business challenges. The core policy, billing, and claims systems are no longer expected to stand alone; they are expected to be a fully integrated solution, a member of a highly connected ecosystem with a unique part to play. Underpinning the ecosystem is the flow of data to complete the transaction and report the status to management and the insured.
3. Data as an Asset
Insurance consumers are demanding an experience tailored to their unique needs. To deliver on this request, insurance companies need to invest in harnessing the current terabytes upon terabytes of details they possess about the customer, claimant, third parties, involved parties, and more. This data can serve two purposes: improving the customer experience and providing critical insights into predictive analytics.
Insurance consumers are no longer happy with the “Welcome, Sara” boilerplate. They’re looking for suggestions based on their personal life experiences. Knowing Sara has a rental policy, the insurance company can use available or purchased data to know that Sara has recently bought a house and has a two-year-old car. Using marketing trend analysis based on Sara’s persona, an ideal cross-selling campaign can be tailored to Sara’s buying propensity based on others like her.
The data can be used to develop predictive trend analysis, highlighting the need for new products, changes in current products, suggestions for pricing options, package recommendations, and so on. The data can also track customer purchasing, cancellation, and general satisfaction with the products offered. Combining insurance industry trends with marketing will enable a targeted campaign for the ideal customer to purchase at the right time.
4. Operational Efficiency
The mix of non-interfaced legacy, modern, and third-party systems creates redundant, manual work efforts to deliver business results. The technology stack has an impact on securing new employees because top talent now only wants to work with cutting-edge software in innovative companies instead of a perceived out-of-date work environment.
Maintenance costs continue to increase as more manual efforts are required to “band-aid” aging systems as they attempt to meet current demands. The morale and effectiveness of employees suffer due to the complexity and often odd behaviors necessary to accomplish what appears to be a simple task. Additionally, executives cannot receive detailed drill-down and drill-through reports to make data-driven business decisions.
Solving the tangled system web of data, access, and capabilities can be challenging but achievable with some of the following simple solutions.
The easiest and simplest way is to implement new modern solutions with many APIs and flexibility in meeting business needs by taking advantage of modern technology and architecture. The final implementation should be a highly integrated, zero back-office system requiring limited resources to operate the business model. This is also the most expensive and most prolonged-duration solution.
Data Strategy and APIs
Develop a data road map of how the data is created, used, transformed, and stored throughout the application landscape. Then determine the optimal method for storage, access, and API development with the existing solutions with the most impact on the business at the start of the journey.
This will require creative solutions to access data from legacy systems, from creating APIs into the core code or frequent data extracts. If it’s done right, it will create a trusted data foundation from which reporting and predictive analytics can be built. This will be a middle ground of development time and effort.
Robotic Process Automation (RPA)
RPA has the ability to mimic human behaviors in the user interface (UI) across multiple systems and to directly access databases. This framework is ideal for extracting data and offloading low-value, clearly defined tasks from employees.
RPA excels at entering data from a spreadsheet, another system, or even an email. It’s also ideal for extracting the status of a work item and taking the next step, such as posting the payment to the ledger or any task that has crisp decision-making requirements. If the task requires analysis or synthesis, RPA is not the right tool because it needs clear “if, then, else” logic.
5. Reduced Expenses
Because customers benefit from the ultra-competitive, frictionless ability to change insurance companies, many companies relentlessly focus on cost reduction to prevent premium increases from losses and other operating expenses. Insurance companies are enabling the claims adjudication process with various technologies to achieve these goals.
Using available data, similar historical incidents, and best practices, insurance companies are applying AI and ML capabilities to create a straight-through claims process experience for simplistic types of claims. For example, glass claims or vehicle damage other than a total loss can now be evaluated if the damage is covered, determine the degree of damage, and make a payout to the claimant or third party without human intervention. AI and ML can also alert the adjustor to take necessary steps for fraudulent claim characteristics.
Insurance companies are combining drone, GPS, and satellite photography technologies to evaluate property damages. To capture high-resolution photographs, adjustors can fly the drone over a natural disaster site, such as hurricane or hail damage. Using the GPS coordinates of the drone, AI first determines if the property is covered using policy system address data. Then AI compares the physical damage to the primary property photograph to assess the level of damage. Reviewing historical costs, building codes, and guidelines, AI determines the cost to repair the damage and makes a recommendation to the adjustor for payment.
Not all technology improvements are related to evaluating the loss; instead, some are focused on prompt payment. New insuretech has created payment portals that allow for secure storage of banking information and rapid payment options of electronic fund transfers or even gift cards. This elevates the insurance company to maintain PCI-compliant systems and management of payment details, such as reissuing a lost check.
Keeping Up with Trends in the Insurance Industry and Beyond
Keeping up with technology trends in the insurance industry—or any industry—can be daunting. RCI Global has found that using a digital roadmap to enable business goals with technology is the best approach. The process focuses on business goals and then reviews the existing technology to see how and what needs to be changed, taking into account prior investments and future technology trends.
Want to learn more about the trends the RCG team is seeing across the industry? Schedule a personalized conversation with an RCG expert to learn how these trends can be applied to your business.