How AI is Transforming the Insurance Industry

Introduction to AI in Insurance

In an era characterized by rapid technological advancement, artificial intelligence (AI) stands as a beacon of innovation across various sectors. Among these, the insurance industry has emerged as a prime beneficiary of AI’s transformative capabilities. From streamlining operations to enhancing customer experiences and revolutionizing risk assessment, AI in insurance is reshaping the very foundations of the industry as we know it.

Indeed, the global market size for AI in insurance is expected to reach $45,110 million by 2030. The benefits of AI are expected to reach across the entire insurance industry.

The insurance landscape is poised to transition from its current reactive stance of detection and repair to a more proactive approach of prediction and prevention.

This transformation will be accompanied by a rapid acceleration in the rate of change, driven by the growing proficiency of brokers, consumers, financial intermediaries, insurers, and suppliers in leveraging advanced technologies to boost productivity, reduce expenses, elevate decision-making processes, and refine the overall customer experience.

In this article, Silverskills’ experts take you through the ways AI is affecting the insurance sector.

The Need for AI in the Insurance Industry

There remain certain areas where insurers have not fully embraced AI, leaving themselves vulnerable to risks, inefficiencies, or unnecessary costs.

Indeed, according to Deloitte, while 32% of companies in the software and internet technologies sector have initiated investments in AI, a mere 1.33% of insurance companies have done the same. However, AI is indeed being increasingly integrated into the insurance sector, and carriers need to prepare themselves to adapt to the evolving business environment.

Insurance leaders must grasp the key drivers of this transformation and comprehend how AI will redefine claims processing, distribution channels, and underwriting and pricing strategies.

Armed with this insight, they can proactively develop the necessary expertise and talent, adopt emerging technologies, and foster the organizational culture and mindset required to thrive as influential participants in the future landscape of the insurance industry.

AI Use Cases in Insurance

Underwriting

The digitization of existing touchpoints and access to new data assets through digital partnerships have expanded insurers’ data reservoirs in the underwriting process.

Data sources such as telematics, remote sensors, satellite imagery, or digital wellness records provide insurers with unprecedented access to diverse data sets.

The ability to harness this data and convert it into actionable insights for underwriting is a crucial competitive advantage. It empowers insurers to offer customers more personalized coverage and pricing options tailored to their specific needs and risk profiles.

AI methodologies like supervised learning offer valuable support in streamlining specific underwriting (UW) procedures, such as optimizing triaging and routing processes.

For example, at Swiss Re, AI-driven predictive models play a crucial role in facilitating the triaging of Life & Health underwriting tasks, thus simplifying the customer journey. These AI models have been integrated into Swiss Re’s automated underwriting platform, enabling the orchestration of human and machine intelligence on a large scale.

Risk Assessment

Traditionally, insurance underwriters have depended on information provided by applicants to evaluate insurance risks. However, this approach has certain limitations, as applicants may provide inaccurate or incomplete information, leading to flawed risk assessments.

ML, particularly natural language understanding (NLU), empowers insurers to delve into more nuanced sources of data, such as social media posts, online reviews, and Securities and Exchange Commission (SEC) filings. By aggregating relevant data from such diverse sources, insurers can enhance their ability to evaluate the potential risks associated with insurance carriers.

Improved accuracy in risk assessments translates to more precise premium calculations. In an industry where pricing is often a distinguishing factor among insurance companies rather than product differentiation, the adoption of a more personalized exposure model could impact outcomes.

Claims

Claims processing stands as a pivotal operation within insurance. As per a study conducted by Ernst and Young (EY), 87% of customers cite the effectiveness of claims processing as a factor influencing their choice to renew insurance policies with the same provider.

• The integration of AL and ML algorithms holds the potential to streamline and expedite the claims-handling process, minimizing the need for human intervention.
• Claims processing is expected to remain a primary function of carriers, but many claims activities will be replaced by automation. Sophisticated algorithms manage the initial routing of claims, enhancing efficiency and precision.
• AI capabilities have the potential to not just enhance efficiency and provide valuable insights, but also to pave the way for the creation of innovative solutions and coverage for risks that were previously deemed uninsurable.
• Furthermore, ML algorithms prove instrumental in various facets of claims assessment, including image recognition, data consolidation, analysis, and forecasting potential costs. By leveraging historical data alongside insights from images and sensors, ML algorithms accelerate the process of claim settlement.
• While claims processing retains its central role within insurance operations, automation is poised to revolutionize many aspects of claims management. Internet of Things (IoT) sensors and a variety of data-capture technologies, including drones, are largely supplanting traditional manual methods for reporting initial losses. Upon occurrence of a loss, claims triage and repair services are frequently activated automatically.
• For instance, in the event of a car accident, a policyholder can capture streaming video footage of the damage, which can be then translated into descriptions of the loss and estimated amounts for repair.

Open-Source and Data

Open-source platforms have become pivotal in facilitating the development and deployment of AI solutions within the insurance sector. These platforms offer accessibility to a wealth of resources, tools, and frameworks that empower insurers to harness the full potential of AI.

• By leveraging open-source AI libraries such as TensorFlow, PyTorch, and scikit-learn, insurers can develop sophisticated algorithms for a myriad of tasks ranging from claims processing to risk assessment.
• Moreover, the collaborative nature of open-source communities fosters knowledge sharing and collective problem-solving. Insurers can tap into a global network of AI experts, data scientists, and developers to collaborate on projects, share insights, and contribute to the advancement of AI-driven solutions customized for the challenges of the insurance industry.
• The proliferation of data ecosystems is another key driver of AI adoption in insurance. With the advent of big data, insurers are inundated with vast amounts of structured and unstructured data from diverse sources including IoT devices, social media, and third-party data providers.
• AI algorithms excel at extracting valuable insights from this deluge of data, enabling insurers to make data-driven decisions, mitigate risks, and personalize insurance products and services to meet the evolving needs of customers.

Data from Connected Devices

In industrial environments, sensor-equipped equipment has long been prevalent, but the forthcoming years will witness a significant surge in the proliferation of connected consumer devices.

The adoption of existing devices such as cars, fitness trackers, home assistants, smartphones, and smartwatches will continue to escalate rapidly, alongside emerging categories like wearable clothing, eyewear, household appliances, medical devices, and footwear.

Experts project that by 2025, the number of connected devices could reach up to one trillion. This surge will unleash a torrent of new data generated by these devices, enabling insurance carriers to gain deeper insights into their clientele.

This enhanced understanding will catalyze the development of novel product categories, more personalized pricing strategies, and the delivery of increasingly real-time services.

Thinking Technologies

Deep learning technologies such as convolutional neural networks, traditionally utilized for tasks like image, voice, and unstructured text processing, are poised to expand their applications across a broad spectrum of uses.

These cognitive technologies (or “thinking” technologies), inspired by the human brain’s capacity for learning through decomposition and inference, will emerge as the standard approach for managing the vast and intricate data streams generated by “active” insurance products linked to an individual’s behavior and activities.

With the advancing commercialization of such technologies, insurance carriers can gain access to models that continually learn and adapt to their surroundings. This will enable the development of new product categories and engagement strategies, all while dynamically responding to shifts in underlying risks or behaviors in real-time.

Conclusion

The insurance industry is poised for disruptive transformation over the next decade due to rapid advancements in technology.

In AI-based insurance, success will favor carriers that leverage technologies to craft novel products, harness insights from cognitive learning derived from new data sources, streamline operations to reduce costs, and surpass customer expectations by offering personalized and adaptable services.

Crucially, carriers that adopt a proactive mindset, embracing disruptive technologies as opportunities rather than threats to their existing business models, will emerge as leaders in the insurance landscape in the coming years.

AI presents numerous entry points for revolutionizing the entire insurance value chain and delivering substantial benefits to customers. However, alongside the promise of these powerful tools, companies must remain vigilant regarding their associated risks and challenges.

Achieving proficiency in data management and cultivating a culture of responsible AI usage are essential steps for ensuring that human oversight remains paramount in the decision-making process.

Silverskills helps you cultivate such a culture with our AI/ML services. We help you design and build innovative products and processes that train, re-train, and act on their own, utilizing the full power of the data provided to them. Contact us now to speak to one of our experts.