The Influence of AI on the Future Insurance Industry - 2023 Technology Trends

What is AI (artificial intelligence)?

Simply put, AI is a set of computerized tools designed to achieve objects that usually require human intelligence (Shroff, 2019). A significant subset of AI is 'machine learning, which refers to the concept that computer programs can automatically learn from and adapt to programs on their own without human assistance.

AI & Insurance

Over the past ten years, technology has pushed the boundaries of digital insurance, making new digital-first insurance companies come to life. The existence of artificial insurance can eliminate problems while benefiting customers simultaneously. AI helps insurers access risk, detect fraud and reduce human error in the application process (Uzualko, 2022). In the customer sense, AI tools can guide customers through different queries without human interaction. Time and place are also unrestricted since AI chatbox can provide services right away.

Applications of AI

The technology of AI can be applied to various industries. For instance, one significant example is that computers can play chess. In ideal cases, AI can accurately predict the consequences of any decision they take, so that chances of achieving desired results can be increased. In the finance and business realm, AI can help detect activity in banking including large deposits or unusual card usage. By estimating supply and demand, trading can be easier with AI. Examples include ChatGPT, Apple Siri, Amazon Alexa etc.

Types of AI

Reactive AI

uses algorithms to optimize outputs based on inputs

• fairly static, unable to learn or adapt to novel situations

• tends to produce the same output if there are identical/duplicate inputs

Limited Memory AI

can adapt to past experience

• updates itself based on new observations/data

• limited updates

• length of memory is relatively short

Theory of Mind AI

fully- adaptive

• extensive ability to learn and retain past experiences

• able to stimulate the consequences of their actions

• more communicative with humans (Eg. able to explain their actions)

Self-Aware AI

the final stage of AI

• have their own consciousness, sentiments and self-awareness (have a sense of self and a human level intelligence

• does not exist in reality and it is still a hypothetical concept

• a close example would be Sophia the robot of Hanson Robotics

AI-Related Trends & Insurance

As AI becomes more deeply integrated with the insurance industry, carriers must be prepared to respond to the changing landscape (McElhaney et al., 2021).

Important AI trends

• Improvement in cognitive technologies

  • Cognitive technologies will become the standard approach for processing the data generated by insurance products that are tied to individual behaviour.

• Open-Source & Data Ecosystems

  • Open-source protocols can ensure that data can be shared and used in different industries (Eg. wearable data could be ported directly to insurance carriers).

• Increased Popularity in Physical Robotics

  • Additive manufacturing (3-D printing) will reshape the commercial insurance products of the future.

• Increased Customer-Connected Devices

  • Existing devices such as cars, smartphones and smartwatches will continue to increase rapidly. Growing categories including clothing and medical devices will also increase.

Case Study 1 (Lemonade Insurance App)

Lemonade, a fast-growing machine-learning insurance app, is taking on insurance in an innovative way. The company relies on big data analytics and machine learning models to power insurance tasks (Laurinavicius, 2022).

Using the power of big data

Lemonade continually improves its underwriting and gets better at detecting fraud (by analysing videos sent in by claimants). They also streamline their internal workflow and customer interaction.

Developing a digital platform

Lemonade is able to cut costs by reducing the need to employ human employees and processing claims more quickly. The company estimates that competitors employ 1 person for every 150-450 customers, while Lemonade claims to achieve 1 employee per 3000 customers.

Case Study 2 (TokioMarine Insurance Group)

The auto-insurer Tokyo Marine, deployed an AI-based computer vision system for examining and reappraising damaged vehicles. This allows the company to make sense of the full extent of vehicle repair options. AI will then recommend repairs and blending processes, as well as the costs involved including labour hours.

Increased Time Efficiency

Damage claims can be remotely reviewed without needing to wait for review periods. This speeds up the review process from days to minutes, while removing inefficiencies such as disagreements regarding repair costs and appraisals.

Increased Computer Vision Accuracy

The AI uses deep learning and machine learning strategies to develop computer vision. This is trained on millions of car damage visual references including photos and past human appraiser decisions, which help to inform the AI algorithms' learning ability

Other insurers such as MetLife and Esurance also accept vehicle photos as part of the claims submission process. However, Tokio Marine is one of the few that is leveraging image recognition to speed up the appraisal process and improve customer satisfaction by providing faster settlements.

Opportunities & Risks of AI

Domain-general AI can bring us many advantages. One of the most important ones is assessing risks. Machine learning allows insurers to understand abstract sources of information more thoroughly, pulling pertinent information together to better access the carrier's potential risk (Uzialko, 2022). The ability to look at data sources and highlight relevant information is greatly increased. Moreover, AI is a watchdog against fraudulent claims. Machine learning can help detect patterns that might potentially escape human cognition. Not only does AI benefit companies, but AI is also transforming the customer service experience in the insurance industry. Customers can receive even more personalized experiences whenever and they want it.

Although many benefits, increased reliance on AI also poses potential risks. One major concern is the threat to fundamental rights and democracy. AI could lead to decisions influenced by data on ethnicity, sex, or even criminal proceeding. It could affect the right to privacy or data protection. For instance, AI can be used in online tracking and profiling of individuals. It is possible that AI can harm democracy, since it can be used to create realistic fake videos which are known as 'deepfakes'. With improper usage, it could potentially present financial risks and challenge the decision-making of individuals.

Summary

AI has emerged as a transformative technology and critical differentiator in the insurance industry when applied in tandem with humans. Since thousands of insurance claims are filed daily, it is both difficult and tedious for insurers to investigate all suspicious cases using traditional systems. Therefore, AI can help insurance companies investigate fraud that humans could miss, and even recognize potential threats before they cause impact. To conclude, using AI properly can definitely amplify human ingenuity and create better environments in different contexts. The benefits of AI in insurance seem clear to stakeholders in the ecosystem.