.svg)
.webp)
Large language models are no longer “new,” but many insurance companies are still reluctant to get help from artificial intelligence. However, integrating AI into the existing workflow brings plenty of benefits.
Generative AI can help you get tasks done one after another without errors or delays, especially custom AI trained on your data and for your exact workflow.
It eliminates errors, the need for human intervention, and manual labor – all while improving accuracy and maintaining data integrity. This is, however, just the tip of the iceberg. Here are more ways AI can help and benefit the insurance industry.
How Is AI Used in Insurance?
In insurance, Generative AI is mainly used to reduce the manual labor of repetitive tasks, improve efficiency, and most importantly, improve accuracy. It can also help with advanced tasks like claims process, fraud detection, data entry, customer and employee support, and risk assessment.
In a complex, AI-powered workflow, AI can handle multiple tasks and tie them together to perform actions based on the overall context. Such a workflow is typically made up of separate AI agents that function without any human assistance.
Example: How AI Can Be Used in Insurance
The most beneficial way to use AI is to use it to automate end-to-end workflows instead of one-off tasks. This allows AI to work with more data and, in turn, make more accurate decisions and predictions. We call this SuperAutomation.
For example, let’s imagine that an insurance company gets multiple specialized AI agents to handle different tasks in their workflow.
They could use these AI agents to automatically verify customer-submitted data, compare it with other relevant data (such as claims), analyze it for fraudulent patterns, make risk assessments, extract key data, and perform other advanced tasks one after another.
This would allow AI to handle about 80% of the time-consuming work for insurance agents, who only need to double-check its outputs before making final steps.
This results in three major benefits:
- Customers get a better experience and a faster response,
- Insurance companies drastically reduce their expenses,
- Insurance companies potentially increase their revenues by serving more customers in the same amount of time.
Ready-Made AI vs Customized AI
The accuracy of AI’s predictions is a major concern for most insurance companies. That’s why we strongly advise investing in AI that’s trained on company-specific data and for company-specific workflows.
This skyrockets its accuracy and reliability, plus allows companies to seamlessly adopt it without having to change the way they work.
We can think about it this way: customized AI is like an employee who had already been onboarded before they started their tenure. It has company-specific knowledge and knows exactly how a company likes to perform tasks.
Off-the-shelf AI, on the other hand, requires much more hand-holding. Getting it to accurately perform tasks – and to do so in a way a company usually does it – can take a long time, or may never happen at all.
How Can AI Help Insurance Companies Handle Claims?
AI can help insurance companies with claims processing in several ways:
- It can compare client-submitted data against official records and similar data assets.
- It can help identify patterns indicating potential fraud attempts and eliminate the risk of human error.
- It can provide answers to clients and employees based on its collected data. For example, it can give clients a status update or let the staff know if the data has been confirmed as authentic.
- Finally, it can suggest approving or denying a claim based on its analysis.
This increases accuracy and security, leads to much faster claim settlements for the clients, and frees up insurance agents for more strategic tasks.
Is AI (Inaccurately) Denying Claims?
The number of denied insurance claims is on the rise from 2021, according to the business news section of CBSNews. This even led to a lawsuit that claims the use of AI increases the number of denied claims and negatively affects the claims management process.
Cigna and United Healthcare were two of the companies that got a class action lawsuit filed against them for improperly denying health coverage. Both use an automated claim system.
The case is still ongoing and there is no evidence indicating that the AI is to blame, at least for now.
Still, it’s possible that some insurance companies unknowingly use poorly-optimized systems. As mentioned, AI that isn’t trained on company data can certainly increase the risk of errors and inaccuracy.
How Can AI Help Insurance Companies With Underwriting?
Modern artificial intelligence may completely take over the underwriting process in the near future.
This is largely due to its advanced natural language processing (NLP) and machine learning capabilities. These help it understand and interpret human language, as well as make more accurate predictions than those made by human underwriters.
Here are only a few examples of how it can automate insurance underwriting:
- It can assess risks by analyzing vast amounts of data, such as historical data, market trends, and individual client details.
- It can also tailor policies to fit unique customer profiles, potentially leading to better coverage options and pricing.
- It can detect fraudulent claims by identifying discrepancies and anomalies in application data.
- It can help forecast future trends and potential losses with greater accuracy, which helps insurers proactively adjust underwriting criteria and premiums.
- It can provide underwriters with recommendations on whether to accept or reject an application based on a comprehensive risk analysis.
- It can help ensure compliance by automatically following current regulations and standards.
By integrating artificial intelligence into their underwriting processes, insurance companies benefit from increased efficiency and accuracy, better fraud detection mechanisms, predictive insights for future-proofing strategies, and automated regulatory compliance.
All of this contributes towards enhanced overall performance and customer satisfaction.
How Many Insurance Companies Are Using AI?
Implementing AI can be complex and there are a bunch of questions that follow. That’s why only about 58% of insurance companies in the USA implement AI. This is far less in Europe, for example, where only about 31% of them implement AI models.
Surveys show there was a much bigger interest in the use of AI. The percentage of interested companies and insurers was about 79%. Upon the development of the first AI models and implementation, only 58% took the first step to introduce AI to their business.
We understand that the complexity of implementing LLMs in a business workflow can be difficult. That’s why a personalized approach matters. Generative AI can transform the way insurance agencies work, but only if implemented in the insurance value chain correctly.
Off-the-shelf AI solutions might not be fully efficient and they can be difficult to set up. Not having a lot of support from professionals during the implementation is also one of the reasons why there aren’t more insurance companies that took the leap of faith now that AI technologies are available.
Even though AI can bring all sorts of benefits to the table and the technology is available, we believe that the biggest problem lies in the implementation complexity rather than any risks involved.
This is why we believe in a solution-oriented approach where AI workflow should be custom-implemented to fit the company’s needs. This immediately increases the value of the AI within the insurance agency, builds trust, and provides better results. If all AI workflows were this efficient, insurance companies would rely on AI a lot more.
AI in Insurance Industry: The Risks to Be Aware Of
#1 Inaccuracy
Inaccuracy is the biggest problem with AI in insurance. This risk is higher for AI trained on generic, third-party data.
Such data often doesn’t have much to do with what insurance companies do and need in order to make accurate decisions and predictions. This risk, however, can be easily eliminated by using AI trained on and integrated with company data.
#2 Safety
AI safety — or the lack of it — is another problem for the insurance sector due to high data privacy concerns.
This risk can be mitigated by using customized AI deployed on company-owned VPCs. This also prevents any data leakage or use of data for further training of public models.
Integrate AI into Your Insurance Company
If you’re looking to automate your insurance workflows with Generative AI, we can help.
By partnering with us, you get AI agents trained on your specific data and for your exact workflow. This increases accuracy and allows for easy adoption. Also, we can maintain the AI agents for you.
.webp)