Artificial intelligence has been named a disruptive force in multiple areas, including finance, healthcare, and security. The insurance sector can benefit significantly from these advancements of cognitive technology too. This is made possible with the heaps of data collected by insurance companies and not used to their full potential.
In the insurance business vertical, AI can have a positive impact at every level, from automating call center request processing to helping make accurate assessments and executive-level decisions.
Through its power to recognize patterns and anticipate actions, AI can provide a predictive environment where risks are anticipated and hedged.
There are numerous ways to use AI in the insurance industry. So far, it seems that the main areas of AI application in insurance include customer experience (58%), process optimization (43%) and product innovation
(19%), from a 2018 study by Everest Global.
Ilya Kirillov, CEO of InData Labs, comments:
Expert consulting is an essential part of every successful AI project. At this stage of the tech maturity, professionals can help alleviate difficulties in drawing up a custom AI solution development plan and see where AI can be of biggest help in insurers’ day-to-day routing.
A report by FBI shows that the cost of insurance fraud is
estimated at more than $40 billion per year. The insurance industry is prone to multiple fraud schemes due to inherent vulnerabilities of policies. However, most of fraudsters follow well-known patterns which AI can identify in a matter of minutes. The risk is to investigate legitimate claims while missing out on actual fraud.
This application will become even more critical in the upcoming years when more policies will be issued and handled exclusively online. AI will be able to highlight any abnormal patterns occurring during policy claim submission if any intervention or diversion have been deliberately
made to the insured asset.
Right now, the insurance sector requires a lot of staff for processing and inspecting claims. This makes policies more expensive and case-solving more cumbersome. These manual tasks can be at least partially replaced with a chatbot to record the claim, verify the details, make sure it is not a fraud attempt, and pass it forward to the bank or other authorized organizations.
Through computer vision, the chatbot can analyze the evidence
and asses the amount of damage. This is a way to replace the lengthy process of sending an insurance inspector to take photos and make a report.
The most common complaint about insurance policies is typically
their price. Insurance-trained AI can create personalized rates based on the client’s actual choices and lifestyle. Factors such as the distance traveled, diseases, financial stability, and more can create dynamically priced policies.
For example, you could follow in Metromile’s footsteps and offering insurance based on miles traveled if you don’t use your car too often. The
company uses an AI-enhanced sensor system to monitor the driver’s behavior and incidents if any.
A similar solution could be created for life insurance based on data provided by fitness trackers and medical records. This is called behavioral premium pricing, and it’s about paying for what risks you take. You
are no longer just a data point in a statistic; you are paying for your
actions. It’s not about approximating but about taking responsibility.
It’s not only the insurance sector that can benefit from personalized marketing and retargeting based on user preferences. An enhanced online
profiling tool can help insurance companies create tailor-made products for a wide array of client segments.
Using natural language processing and scanning comments from
online platforms and forums can lead to the creation of innovative insurance products that are more adapted to the modern client’s needs. It’s an age-old problem of listening to the client’s voice that is now solved in a new, AI-powered way.
Customer experience is all about speed and reliability. The
time to settle a claim is a key performance metric for the insurance industry, and if it can be reduced from days to minutes, it is enough reason for most customers to sign it, even if this means potentially trading in a part of their privacy.
The next step of using AI in the insurance sector is not so much about innovating but about integrating. If right now we have different services providing us with insurance for health, car, and home, soon we can hope to see universal insurance models which are customized to the client’s
needs and priced dynamically according to the perceived risk.
A relevant example in this relation comes from McKinsey’s report
Insurance 2030. The narrative describes Scott, a fictional client who uses his digitally powered AI assistant, to do some daily chores. The difference from current practices is in computing the premium on the go, based on Scott’s decisions and his particular lifestyle.
The first conclusion is that we can no longer have one-size-fits-all insurance. Instead, we will have an adaptable version. Also, people will become more careful and try to prevent instead of repairing or treating. This is an interesting paradigm shift since until now joining an
insurance pool was about sharing risks, but AI is making us more responsible for our own actions.
Any new technology is just as good as the adoption rate. The good news is that Accenture has proven that as much as 74% of customers are willing to use computer-generated insurance advice. The promise here is about giving instant help through easy-to-use channels, such as messengers and voice.
In the era when a selfie is enough to buy insurance, like in the case of Lapetus Solutions, the customer gets much more than an insurance policy. They get healthcare advice, possible savings, and dedicated products.
The downside of this approach has to do with privacy. In a world where your insurance company can determine what you did last night and if you took your medication, do you feel safe or do you feel part of a Big Brother system? What is the perfect balance between customization and intrusion?