How insurers use this data to improve customer experience, reduce fraud or develop new business models will determine their success or failure for decades to come. Some insurers have already taken initial steps and are experimenting with AI use cases in the areas of pricing, customer interaction and fraud detection. Additionally, an increasing wave of InsurTech start-ups are entering the market with competitive offerings that differentiate them through a novel value proposition, attractive pricing or a superior user experience.
If navigating this new landscape isn’t difficult enough, the biggest challenge companies typically face are internal, including cultural and organizational barriers as well as talent shortages, according to Harvard Business Review (HBR) (1). A closer look at AI reveals estimates by Business Insider Intelligence (2) that it could save $390 billion in costs across insurers’ front, middle and back offices by 2030. This time horizon seems far off but insurance companies that have already implemented advanced analytical solutions are outperforming the competition today by 76% (3). Popular insurance industry AI applications range from risk management, fraud detection and prevention, personalized offerings and customer churn (attrition) reduction to climate change impact prediction. On-demand and usage-based insurance (UBI) offerings will be driven by AI’s ability to aggregate disparate data sources and ultimately deliver more flexibility to customers.
Sales and marketing
Pricing and underwriting
“When developing a new product, insurers can take a page out of a large digital retailer/wholesaler’s playbook: Begin with something simple and add complexity as required. Developing a minimally viable product (MVP), which starts as bare bones and grows with iterations, may eventually prove to be a backbone of agility for product development.” (5)
The same type of agile development methodology used for software development and for Unit8’s projects can be applied to develop innovative insurance products. Advanced analytics and AI are expected to become the foundation of new insurance products, allowing for the hyper-personalization of new products and offerings. This iterative process allows features to be continuously added to a product or service. By viewing additional features with this modular mindset, a flexible building-block foundation is created that evolves with new inputs. Any data that future customers might provide from health and fitness trackers (e.g. smartwatches) could be leveraged for more customized insurance offerings.
This type of UBI deviates from the insurance models people are accustomed to. The advantage is that instead of relying on aggregated statistics and past trends, it models individual and current behaviours. This customized offering provides an attractive alternative, given the breadth of lifestyles, habits, cultures and personalities. Data privacy, and more generally peoples’ perception of data usage, is still evolving and will determine the pace at which these offerings will be made available.
Insurers spend billions of dollars every year on marketing to acquire new customers. Depending on the study consulted and the industry, acquiring a new customer is from 5 to 25 times more expensive than retaining an existing one. That makes sense: there’s no need to spend time and resources going out to find a new client — it’s just necessary to keep the existing one happy (6).
Nonetheless, sales and marketing are two of the biggest adopters of AI across the insurance value chain. The new product offerings mentioned above are allowing companies to offer customized options to potential as well as existing customers. This combined with customer intelligence allows the creation of distinct personas that can be targeted along any part of the customer’s buying journey. By creating such personas and better understanding customer life cycles and buying journeys, insurers can create a “next-best-action” tool. This gives sellers and marketers clear guidance on which offerings will resonate with which customers, thereby enhancing the efficiency of the consumer-focused sales journey.
Interactions with existing and potential customers are also changing with chatbots and new levels of natural language processing. These allow insurers to engage with customers 24 / 7, helping to offload more mundane communications in an automated way. Chatbots are now much more than just interactive conversational tools for customers.
Integrating chatbot functionality in diverse insurance offerings supplies an automated insurance terminal where customers can begin and end their entire insurance buying process. The analytics behind such solutions are combined with intelligent customer relationship management and allow this complex web of interactions to be visualized and streamlined at scale.
The conversation is not one-sided, though; customers expect proactive and relevant communication from their insurance providers. Indeed, “57 percent of insurance customers around the world, across all product types, prefer to hear from their providers at least semi-annually; only 47 percent receive that level of contact currently. As AI becomes more pervasive, an increasing part of lead identification [customer prospecting], generation, and capture will be automated by AI. This will slowly become reality for many companies that are looking to personalize at scale, so the key questions to winning market share will become: ‘How do [insurers] … understand and leverage AI tools to offer the most relevant product offerings?” “Where can [they] acquire data that [their] competitors don’t have access to, to help [them] understand what [their] customers really want?” (7).
With customized insurance products based on hundreds of thousands of customer personas, the pricing and underwriting of these complex insurance policies become equally complicated. Yet traditional insurance pricing models are often still based on simple matrix systems that take a small number of variables into account, leading to high exposure risk and large loss ratios. This static approach to risk modelling will be replaced by a bottom-up approach in which internal data is combined with a growing pool of external data assets (economic, crime rate, credit, traffic and weather data, for example.) This data lake, together with a machine learning framework, will enable more dynamic and forward-looking risk models.
Transforming a traditional underwriting process is complicated with no “golden model” that can easily be implemented. Additionally, a defined list of offerings no longer exists, replaced by one with offerings determined by individual customer demands and situations. But four guiding principles can help undertake such a transformation:
“But why is my premium more expensive?” Explaining to a customer why a particular model suggests one insurance plan over another or why one person’s premium is more expensive is a key topic that cannot be ignored. The myriad models ingest so much data that humans can no longer explain why one offers a particular suggestion or price. This “black box” approach should be addressed from the very beginning. Any model used, not just for pricing or underwriting and not just within the insurance industry, must include explainability features. They enable human interpretations of the model’s decisions and an understanding of the model’s decision process.
A particularly interesting data source in the property and casualty insurance sector is geospatial data, previously known as geographic information systems (GIS). Historically, this type of data was only available to large companies and countries due to the high cost of sending satellites into space. Over the years, however, these costs have dropped considerably, as has the computing power to run analytics on a wide scale. Insurers now have the ability to perform a detailed analysis of a property using machine learning and computer vision solutions, that is using machines for recognizing and processing images and videos. “GIS can significantly aid in locating policyholders and assessing the impact. Accordingly, claims processing also gets expedited. Where previously it might take days or weeks to assess the insurance market’s exposure to a climate event, today, geospatial data visualisation tools using real-time flood warning data can quickly calculate the levels of exposure and export customer lists to help with mitigating damage from the upcoming event.” (8). The data can detect trends and changes over periods of time and combine this information with weather and climate data to more accurately calculate risk exposure or even the effects of climate change on particular regions, cities and individual structures.
Geospatial data can help underwriters better evaluate and model risk, but what about after a natural disaster? At that time, geospatial data can be used to estimate damages, predict payouts and reduce fraudulent claims. The most critical time for customers to evaluate their insurer is during the claims process; the customer experience during this process is a major trigger in determining whether they stay with an insurer or look for another. This doesn’t just apply to property and liability insurers, but to the industry as a whole.
The claims process is complex as different claim types follow different processes and involve different departments. From the time a customer submits a claim until the time it is resolved, insurers have the opportunity to prove themselves. This starts with the options the customer is given to submit a claim or to have internet of things (IoT) devices and sensor solutions automatically trigger the filing of the claim. Automating submission tools not only makes it easier for the customer, it also standardizes the data received by the insurer, enabling simpler data aggregation. Handwritten forms can be submitted and processed with computer vision solutions just like pictures, and even customer–agent conversations can be recorded and the data aggregated into a single platform for analysis.
Data generated in this first step alone will create a library of events that can be used to check for fraud, to optimally route claims to the proper department or even to complete the claims process automatically based on the claim’s severity and complexity. The clock is ticking and customers expect the situation to be resolved quickly. They have submitted everything the insurer has requested and, as days, weeks and sometimes even months pass, they must be kept engaged. Insurers know the complex process claims go through, understand each step and what is being done; the customers do not. Keeping them informed about this process and continuing to engage them are key to ensure their satisfaction and avoid frustrated and costly calls with live agents.
Proactively engaging customers will be a major differentiator, but so will speed. Auto-mated claims management that includes computer vision and machine learning to analyse claims has already been mentioned. Based on anomalies, the system can flag the claim as potentially fraudulent for it to undergo further review. Here, again, historically, investigators would often need to physically inspect the damages, something that can now be automated based on weather, geospatial and claims data, or on many other types of information. Considering that fraud makes up 5-10% of insurers’ claim costs, intelligent automation tools can help reduce fraudulent payouts and help investigators focus on only the most likely fraudulent claims (9).
The same data used to analyse the type and validity of the claim is then used to estimate the payout. This again speeds up the time to resolution. Every automation and process improvement is executed to accelerate claim resolution and reduce payout time, ultimately focusing on improving the customer experience.
Insurers should start with an “everything is possible” mindset to unleash truly transformative ideas. Satisfaction surveys pertaining to claims consistently show that customers want a fast and intuitive process as well as transparency regarding the process and what happens next. Consequently, the digital redesign of a claims journey needs to go much deeper than superficial process improvements. The Spanish company Adeslas has completed the end-to-end digitization of its claims journey, applying such features as multichannel First Notification of Loss, automated claims segmentation and digital claims status tracking (10).
Data is key to all of these topics, and every company across every industry needs to become a data company. Companies that embrace this change both from a process perspective and, even more importantly, from a cultural perspective will create a competitive edge. Insurance companies that successfully become data-driven will innovate, simplify and improve their customers’ experience while streamlining internal processes. To do this, they will need to rethink their approach to data; archaic designs from the past need to be replaced with agile methodologies that put data at the heart of decision-making.
This data analytics journey can be broken down into four parts, with questions to consider:
Companies need to understand that this journey will take time. Key is to have an agile approach that allows quick wins but also quick failures — both of which will likely occur. It is important to keep the customer experience and customer perspectives in mind when developing new strategies, and to reinforce a proper data culture throughout the organization. Otherwise, these initiatives have limited chances of success.
At Unit8, our goal is to drive the adoption of AI in the insurance space and help companies close this knowledge gap. Key areas in which we help leading insurers solve major challenges using data, advanced analytics and AI include: