Artificial intelligence is slowly but surely changing the insurance landscape. From chatbots to underwriting, the technology is impacting the industry in a number of ways.
Here’s a look at how AI is transforming the insurance industry and what it could mean for the future.
Use Cases Of AI In Insurance industry
According to McKinsey, during the next decade, 10 to 55% of the work performed by core activities inside insurance companies—including actuarial, claims, underwriting, finance, and operations—could be automated, while 10 to 70% of jobs would alter dramatically in scope.
As insurance providers begin to implement artificial intelligence into their business practices, the insurance industry as a whole is expected to see increased efficiency and profitability. For insurance companies, AI can be used to automate many tasks such as claims processing and fraud detection.
By using AI, insurance providers can improve their customer service and underwriting processes. In the long run, AI will help the insurance industry become more efficient and provide better services to its customers.
1. AI For Underwriting
The underwriting process can be seen as one of the most hard and tedious processes in an insurance company. Traditionally, underwriters need to gather a lot of information from various third-party sources and data to determine the risk profile of a potential customer.
However, with AI and robots, such data gathering is now possible – allowing insurers to look at more appropriate risk profiles at much lesser time and effort.
2. AI For Customer Service
Customer service is often cited as a major pain point for potential customers in the insurance industry. With chatbots and AI systems working behind the scenes, it will greatly help insurers provide their clients with timely yet tailored assistance.
3. AI For Telematics
Telematics is a type of insurance that uses vehicle-based technology to assess the risk of insuring a particular car. Using data from vehicle sensors and GPS, insurers can assess how risky a vehicle is and set premiums accordingly.
By leveraging AI, insurers can even further fine-tune this system to better predict an upcoming accident or if the driver is deemed irresponsible on the road.
4. Risk Modeling
Risk modeling is another area where AI comes into play. By using advanced machine learning algorithms, insurers are now able to create risk models that take into account more factors than ever before – including weather patterns and similar incidents in a specified area.
By doing so, the technology can help insurers assess risks more accurately, resulting in more efficient and personalized insurance products.
5. Automated Fraud Detection
When it comes to fighting fraud, AI is proving to be a highly effective tool. With automated voice recognition, AI algorithms are now able to detect fraudulent claims – right through the sales cycle – and can save a lot of time and money for both insurers and their customers.
6. Customer-Facing Apps
Insurance companies are now taking advantage of the latest developments in mobile and app technologies to deliver a better user experience. With intelligent applications, customers can now get the information they need – and even make payments – in just a few taps on a mobile app.
7. Claims Handling
With the growth of digital technologies, insurers are also able to provide additional services for their customers, including claim settlements. By leveraging AI-powered technology, insurers can now provide their customers with 24/7 access to claims handling as well as better support.
8. Customer Profiling
The technology is also being used to better understand customer behavior – from their spending habits to lifestyle choices. This allows insurers to create more relevant and personalised products for their customers – which is something that the latter have been looking for for years.
Benefits Of AI In insurance
According to McKinsey, up to 30% of underwriting tasks might be automated by 2030, with another 30% including increased use of analytics tools and collaboration with data scientists.
Data scientists are always looking for new ways to detect insurance fraud. Claims data is a rich source of information that can be used to identify fraudulent claims. Emerging technologies, such as machine learning, can be used to effectively detect insurance fraud.
Insurance agents, insurance executives, and insurers are all looking for ways to reduce operating costs and claims processes. Many insurance companies are turning to technology to help them accomplish this.
1. Higher Customer Engagement
AI is already being used to make insurance smarter by generating new tools that help insurers better engage with their customers. Chatbots are now being designed as virtual agents that can answer customer queries and provide more personalized assistance.
By leveraging AI, insurers can also build more in-depth profiles of customers, which will in turn enable them to have a better idea of how to deal with various risks.
2. Improved Claims Handling
When it comes to claims processing, AI promises faster and better claims handling for both consumers and insurers alike. By using advanced models, insurers will be able to process claims significantly faster and hence save a lot of time and money from the earlier manual processes that were involved in such operations.
3. Improved Efficiency
While AI will not replace the human element, it can definitely help insurers to reduce their total cost of ownership through greater efficiencies and improved decision-making. By using advanced predictive modeling tools, insurers can better anticipate fraudulent claims or potential claims from customers and take preventive measures accordingly – which is a huge plus for the industry.
According to McKinsey, employing a new employee costs 100% or more of their yearly compensation, but upskilling or reskilling costs 10% or less.
4. Reduced Resources
To become more scalable and efficient, insurers need to drastically reduce the amount of resources they use – and technology is here to help them achieve exactly that. AI is also helping insurers automate a lot of their processes to free up more resources for faster growth and expansion.
5. Better Underwriting
As mentioned above, underwriting is at the heart of insurance and is a key component of the business. With AI being used to automate as much of the underwriting process as possible, insurers can reduce the number of manual interventions and data entry, which in turn frees up more time for their agents – which in turn benefits both parties.
6. Improved Customer Support
AI can also help improve the overall customer support by providing more relevant information on claims handling or lost claims – with agents being able to provide customers with 24/7 interactions on matters relating to their claims.
Besides, AI can also be used to better serve customers in terms of information on their claims or policyholders’ profiles, which will in turn make the process easier for consumers and insurers alike.
7. Improved Underwriting Models
In order to meet specific regulatory requirements such as Solvency II, insurers are now leveraging AI to improve their underwriting models – and these models are being used to further reduce risk just as well as they are being used to automate the underwriting process. This is a perfect example of how AI is helping companies scale up faster while at the same time reducing operational risks that often result in inaccurate projections and forecasts.
8. More Personalized Products
AI can also help insurers to better understand their customers by building more relevant profiles. These profiles can then be used to create more personalized products that are tailored specifically to the customers’ needs to meet these goals. The information on these products will be more accurate and reliable than ever before – which is a huge plus for both consumers and insurers alike.
9. Reduced Losses
While AI cannot replace humans altogether, it is definitely helping companies prevent fraudulent claims or effective claims – which in turn reduces risks and losses. These advances are helping insurers better understand the likelihood of fraud or another type of risk taking place, which can then be used to generate automated alerts/alarm in order to further reduce these risks.
Risks Of AI In Insurance
According to a recent Willis Towers Watson research, 60% of life insurers say predictive analytics have enhanced sales and profitability. Auto insurance companies face many challenges when it comes to claims management and processing.
Insurance customers are often unhappy with the claims process, and this can lead to bad publicity for the company. In addition, insurance companies must deal with a lot of paperwork and red tape when processing claims.
This can be a time-consuming and expensive process. With the increasing digitization of insurance data, both internal and external data sources are being used more frequently for fraud detection during the underwriting process. Machine learning is playing a larger role in this process as well, with insurance industry ai becoming more sophisticated.
1. Data Quality
The first and probably the most important risk AI brings to the insurance industry is data quality. Since AI models rely heavily on data to generate insights, poor-quality data can render these insights inaccurate or misleading.
In order to prevent these issues from arising, insurers must ensure that their data sets are as comprehensive, accurate, and consistent as possible.
2. Impact On Human Jobs
Another risk AI poses for insurers is the impact it will have on jobs in the sector. Automating a lot of their processes can significantly reduce the need for human resources – which may impact both consumers and insurers alike in ways that cannot be readily predicted at this point in time.
It may be that some jobs will get replaced by AI that will replace humans in certain aspects of underwriting or claims management.
According to PwC, 81% of insurers are worried about the availability of important talents within their workforce—but this does not necessitate widespread recruiting.
3. Impact On The Industry
While AI can help insurers ensure greater efficiency, it can also result in less need for human intervention. This can potentially lead to automation in some aspects of the business – which will make insurance less innovative and help insurers miss out on opportunities for growth – which is a big risk for both consumers and insurers alike.
4. Impact On Surveillance
While it’s important that insurers protect themselves against fraud and other unauthorized activities, there must be a balance in their monitoring processes. If, for example, an insurer tries to cover up or hide certain cases of fraud or misuse, this may result in increased levels of unnecessary risks.
To prevent this from happening, insurers must use real-time data to identify suspicious claims or potential fraudulent activities – which can then be addressed more quickly and efficiently.
5. Data Security
Another risk of using AI is data security – which is particularly important for larger companies that are dealing with vast quantities of information on their customers. With so many computers and networks that process this information, data security can be difficult to maintain – which is why insurers need to carefully evaluate their security measures in order to protect their data.
6. Interruption Of Service
If there is an interruption in any part of the business or process, it can cause a lot of damage to the company and its customers alike.
To prevent this from happening, insurers must continue investing heavily in their IT infrastructure and make sure that all their technology solutions are used in a way that does not compromise the quality or integrity of sensitive information pertaining to consumers or the company itself.
7. Regulatory Changes
Another risk that AI poses for insurers is regulatory changes – which may change the way insurers do business, for better or for worse. For example, it can affect the restrictions on capital requirements, solvency ratios, claims reporting and other operational or legal requirements – which will also impact consumers in an indirect way.
At the same time, regulatory changes can also lead to AI being used more extensively to improve a range of processes within insurance – which is a good thing overall.
8. Disruptive Innovations
While disruptive innovations do not pose any serious threat to existing insurers, new technologies can pose a number of challenges and issues that can lead to the disruption or failure of these firms.
When new technologies are introduced and existing players are unable to adapt, they may be forced out of the process altogether – which can be very damaging for insurers and consumers alike.
Insurers must invest in their own IT infrastructure as well as other forms of technology, just in case this happens to them – but at the same time, they must also ensure that this investment is not too expensive and does not become a significant burden on their finances.
Final Note
Artificial intelligence in the insurance business is valued at USD 6.92 billion by 2028, with a compound annual growth rate of 24.08% projected through 2028.
While there are many exciting ways that AI can be applied to the insurance industry, it remains to be seen whether AI will really play a major role in the sector.
It is too early to tell whether insurers can really take advantage of all these advances, but given that the technology will only continue to improve and evolve, it’s safe to say that insurance companies may need to explore this new frontier in the future.
Last Updated on October 10, 2022 by Parina Parmar
