According to Early Metrics, two-thirds of insurers believe that artificial intelligence can help them increase their productivity. Yet a recent report by PwC indicates that only a quarter of them have integrated this technology into their activities. To take the plunge, how can they draw inspiration from other industries?
There are several sectors in which the implementation of AI is more advanced than in insurance. This is particularly the case with automotive equipment manufacturers (76%, according to a Google study), in the retail trade (50%, according to KPMG), or even within pharmaceutical companies, which would be 90% to have bet on this technology in the wake of the year 2020, in particular after having been able to observe its essential role in the development of the vaccine against Covid. What lessons can insurers learn from the deployment and use of AI in such diverse markets, and far from their core business?
Auto: when humans and machines work hand in hand
In the automotive industry, cobotics represents a typical case of collaboration between artificial intelligence and human beings. At the crossroads of “cooperation” and “robotics”, “cobots”, also called “collaborative robots”, generally take the form of small trolleys programmed to follow an order picker. When the latter finds the parts it needs, it places them in the cobot, which then makes its way to the assembly line, unloads, and returns, systematically choosing the most direct route, which allows optimize the work rate.
Also in insurance, algorithms are dedicated to simplifying and making intuitive the tasks which will then be finalized and supervised by the managers, thus optimizing the two intelligences, artificial and human – and proving their complementarity. For example, they are able to extract all the information from an invoice or a medical report and determine what is or is not covered by the insurance contract. The claims handler then acts as a supervisor of a set of AI modules that perform various time-consuming and repetitive tasks for him.
Retail: chatbots at the service of the customer experience
In retail, industry players are turning to AI to improve the customer experience. Using chatbots and virtual call centers, merchants are able to parse natural language, triage and route customer inquiries to the appropriate channels, while channeling the most complex queries to human collaborators. . Result: 40% of users prefer to chat with virtual agents rather than after-sales service, according to a study conducted by Juniper Research.
Insurers can learn that it is more possible than ever to rely on this AI-driven technology. For example, to allow policyholders to declare and monitor the processing of their claims directly via an online portal. The only prerequisite: that it be able to understand what the policyholders are saying, and to answer their questions in an intelligible and relevant way. The situations encountered in insurance can be very diverse and sometimes very complex – the chatbot must understand the circumstances of the claim, the damages, the responsibilities – only the simplest and most common claims can be fully automated. Claims management experts will thus be able to refocus on the most complex cases, those that require a certain amount of empathy or concrete human support, such as a major bodily injury or a distress situation experienced by the insured. .
Pharma: sorting data to support laboratories
In the pharmaceutical sector, AI helps to streamline, sort and interpret large volumes of data. For example, when clinical trials generate relevant information on drug dosage, delivery methods, participant health measures, etc. If a participant X falls ill, the cause can be traced to a component of the drug, to an incorrect dosage, but also to food poisoning unrelated to the trial. In the first two cases, it is essential to take this into account, while the third is only a false positive whose investigation represents a waste of time.
The analogies with the insurance sector are also obvious here. In pharmaceuticals, any delay in the interpretation of results will have a direct impact on the time to market. This can harm the competitiveness of the producing laboratory, but also the well-being and safety of patients.
In the insurance industry, any delays in claims resolution can lead customers to terminate their contract, but also have an impact on their health and livelihoods. An additional difficulty, claims management involves an immense amount of information, not all of which is useful. It is crucial to be able to identify those that are relevant, and to rule out the others, in order to take the decisions as quickly as possible that will allow the file to be closed. Policyholders are indeed extremely sensitive to the length of time it takes an insurer to solve their problem – for example, when their vehicle is immobilized, or when water damage is in progress and requires a quick resolution. Accelerating decision-making through the use of machine learning can be a determining factor. This was particularly true during the development of vaccines during the Covid-19 crisis, where this technology allowed laboratories to save a valuable number of days by automatically selecting candidate vaccines.
Generally inclined to dare to bet on innovation and new technologies, insurers will be able to pursue this course of action in 2023 by drawing on these various experiences. To streamline interactions between their managers and the technology they use, to improve the customer experience, and to speed up claims management through the use of artificial intelligence.