With an insurance boom in the Asia-Pacific (APAC) region, Insurers are competing for developing superior technological capabilities in order to meet their customers’ demands better. Therefore, to stand out from the competition, companies are regularly adapting new tactics to ace the game, and AI is one of them.

According to a study, more than 80 per cent of insurance CEOs mentioned that AI was already a part of their business model or would be within the next three years.

AI has honed the way increasing data, computing capabilities, and evolving consumer expectations are handled and executed by making processes more automated and efficient. The role of AI has evolved over time to fulfil complex business requirements. In this blog, we will cover six significant areas in which AI is transforming insurance companies, but before proceeding, let’s take a look at how AI trends within Insurance.

Trends of AI in Insurance (50-100 Words)

Google Trends, reveals a constant uptick in AI-powered insurance applications acquired by the insurers between 2015-2020. 

However, the impact of COVID-19 in 2020 has slowed this pace down a little. This is because insurer spending on AI systems had taken a back seat to mitigate other more pressing challenges that required allocation of budgets to those priorities. But in the Post- COVID world, it is expected that AI and insurance have a long way to go together.

How AI is Transforming the Insurance Industry 

Artificial Intelligence has driven positive impacts on many different business models, and insurance is no exception. Also, it works much better with AI because insurers have a treasure-trove of data, which is the primary fuel to drive successful results with AI.

Among all changes AI brought, the six major ones are mentioned below:

1. Claims acceleration

AI is applied to automate or accelerate the process of claim. Claims processing includes a lot of tasks like reviewing, investigating, making adjustments and remittance or denying. If solely done by humans, the following issues might occur:

• Inconsistent processing and more probability of errors
• Varying data formats and time-taking management 
• Staff training and process updating sessions

These processes can be accelerated with new Artificial Intelligence capabilities, leading to claims being paid in hours or days rather than weeks. However, likely, this kind of automation for claims acceleration will only work in low impact claims. For complicated requests, AI, along with human interaction, will be able to achieve the goal.

2. Price sophistication using GLM

Insurers widely use AI techniques like GLMs (Generalised Linear Models) for price optimisation in tar and life assurance fields. Pricing optimisation allows companies to understand their customers better and enable them to balance capacity with demand and drive better conversion rates. 

Moreover, adding non-traditional data like unstructured data and written reports can also augment price optimisation and make better decisions.

3. Using IoT 

IoT (Internet of Things) is one of the most significant AI opportunities within the insurance industry. These devices are getting a lot of traction from the users and are beneficial for insurance companies to assess customer risk profiles. Several IoT smart home devices are being used to alert customers when there are issues within their home or commercial property, for example, leak/moisture sensors. Using them, along with AI, helps insurance companies to offer better services.

For example, predictive analytics models could be built using the datasets of customers using leak detection sensors to predict which customers might be vulnerable to a leak. This prediction will help companies to send out repairers to replace faulty pipes before they burst to lead to claims.

4. Personalised Services and Recommendations

Personalised services help customers to match their needs and lifestyle. Artificial Intelligence creates personalised services using customers’ product ratings, demographic data, preferences, interaction, behaviour, attitude, lifestyle details, interests, and hobbies. This helps companies in selling the right product to customers and target the correct audience. An Accenture study suggests that 80% of insurance customers are looking for more personalised experiences, and AI helps companies do so. 

Moreover, with the recommendations based on the customer’s behaviour or past purchases, AI shapes the way things are recommended to the customers. For example, a customer looking for health insurance would be displayed with offers on health insurance. Also, this helps in sending meaningful marketing messages.

5. Eliminating underwriting risks

Humans solely did the process of underwriting. Therefore, the probability of getting errors was quite more and also it was a time-consuming process. But AI technologies have worked their way into this area of insurance and made the process quick and efficient without manual efforts.

6. Affective computing (Emotional AI)

Also known as emotion AI, Affective computing is used to understand customers better and make decisions according to their mental/emotional states. It identifies, processes, and simulates human feelings and emotions and behaves and replies based on the same. This technology is shaping the Insurance industry in the following ways:

• Fraud detection: Voice analytics is used to understand if a customer is lying while submitting a claim. AI makes this analysis based on various previous data sets and customer behaviours.
• Intelligent call management: Customers running short on time or are angry are directed to more experienced call agents to ensure their satisfaction. 

New Adaptations

This ever-changing digital era is continuously adopting new technology. Therefore, another critical element to understanding the industry transformation is comparatively learning about the existing techniques and the new ones. 

The chart mentioned below contains some generic high-level use cases that many Insurance organisations are adopting. The abbreviations used are:

• ML: Machine Learning
• NLP: Natural Language Processing
• SVM: Support Vector Machines

Conclusion

So far, the blog must have helped you know how AI is transforming the Insurance industry in various ways. You can adapt to these modifications in your business model to stay ahead in the competition. However, it is worth mentioning that AI to an Insurance company could be beyond standard use cases and be viewed as a way to augment the role of data assets. There’s a lot to gain from the AI-first world for insurers, and also a lot to lose if AI is not embraced and well understood.