Insurance is experiencing a digital revolution, despite being an industry that has been resistant to change for centuries. Advanced machine learning algorithms have enabled underwriters to bring in more information to help them better assess risk and provide premium pricing that is tailored to their clients. AI streamlines the insurance process and connects applicants with carriers with less errors.
This rapid evolution is a boon for both applicants and insurers. This is how AI is shaping the future of the insurance industry.
In order to evaluate clients’ insurance risk, insurance underwriters have historically relied on information provided by applicants. Unfortunately, applicants may be dishonest, or make mistakes that could lead to inaccurate risk assessments.
Natural language understanding (NLU) is a form of machine learning that allows insurers to analyze more abstract information such as Yelp reviews and social media posts. This information can be used to help them assess the risk potential of an insurance carrier.
Andy Breen, Argo Group senior vice president of digital, stated that NLU has greatly improved our ability to look at textual data sources and extract highly relevant information. “We are making use of information sources that were not available before or easy to disseminate.”
A more accurate risk assessment means that premiums are more appropriate. A more personalized exposure model could make a huge difference in an industry where the biggest difference between insurance companies is their products, not their prices.
Porges stated that the industry had offered a standard liability policy, which was the lowest common denominator product. What you get is an undifferentiated product. A bakery and a laundry have the same policy. This is not the best way for customers. We will be able to consume greater data automatically and customers will enjoy the benefits of paying for the coverage they need.
Insurance companies are concerned about fraud and AI plays a crucial role in preventing fraudulent claims. In a blog article about insurance fraud prevention Samsung notes that it’s about detecting patterns that may escape human cognition.
French AI startup Shift Technology integrates this technology into their fraud prevention services. They have processed more than 77 million claims. For fraudulent insurance claims detection, the cognitive machine learning algorithms achieved a 75% accuracy rate. The ML algorithms give details about suspicious claims, including potential liability and repair costs assessments. They also suggest ways to resolve fraud protection.
Areiel Wolanow is the managing director of Fiserv Experts. The potential of machine learning to assist with spotting suspected fraudulent activity is well established. But, human-led data science has proven just as effective so far. The cost will make a difference in the long-term. Professional criminals will stay on top of industry-leading fraud indicators, and adapt their behavior accordingly. Machine learning algorithms will learn over time from observable changes in the data, and human data scientists will need iterative analysis.
Reduce human error
Insurance industry distribution chains are complex and winding. Breen said that a number of intermediaries examine the information between the insureds and the carriers, which leads to human error and manual labor that slows down the process. AI is beginning to address this problem.
Algorithms can help reduce errors and time in information transfer from one source to another. Breen stated that insurers can reduce the time and errors associated with data entry by uploading a PDF to a portal.
He said that people get bored, tired, and make mistakes but algorithms don’t.”
Porges believes that bridging the gap between insured and insurer is just as important as reducing errors. Porges believes that customers and insurers both benefit from better data. Insurers can create better products based upon more accurate assessments and customers will be able to pay the exact amount they require.
Porges stated that machine learning will allow us to give the consumer better advice. “Based on the information you provide about your business, and my knowledge about similar businesses, I can tell you that I believe this coverage is right for you. It’s not the agent or the customer who have to take responsibility, but the data that will provide the advice.
Good customer service is essential in a sector that is so change-resistant like insurance. People often quit using companies that provide poor customer service. Chatbots are now a standard feature on many insurance company websites. These AI tools are able to assist customers with many queries without the need for human intervention. They are also available 24/7 unlike other teams of real people.
A customer may ping the chatbot to get assistance from their insurer. This function could solve customer problems quickly. While human customer service agents are still necessary for more complicated issues, AI chatbots can take care of most of them.
Did you know? Insurance website chatbots are a form of AI and one of the latest chatbot trends. Some chatbots are complex enough for customers to navigate difficult tasks. Others are more simple and can be used to answer basic questions.
Processing of Claims
Although insurance companies exist to help customers and process claims, claims assessment can be difficult. Agents need to review multiple policies and go through each detail in order to determine the amount the customer will get for their claim. This can be tedious. AI can assist with this task.
Machine learning tools are able to quickly determine the details of a claim and predict the costs. They can analyze historical data, images, and sensors. The results of the AI can be verified by an insurer who will then settle the claim. Both the customer and the insurer will benefit from the result.
Can AI be used to improve the customer’s experience in the insurance industry?
The widespread adoption of technology in the industry often reflects its benefits to the sector. Sometimes, the benefits are not obvious to the customer. This is not the case for AI in the insurance industry, which has clear benefits for customers.
AI-assisted risk assessments can be used by insurers to better tailor plans so customers only pay for what they need. This can reduce human error and increase the likelihood that customers receive plans that are tailored to their needs. It can increase the customer service options for insurers and simplify the claims approval process. Customers get what they want.
Future of AI in insurance
AI is a new frontier for the insurance industry. Companies are already exploring ways to integrate it into their daily operations, in anticipation of technological advancements.
Breen stated that it was the very beginning of AI. For repetitive, menial tasks, we put computer on it… but we are a long way from being a computer underwriter. At this point, we’re just augmenting human beings.
He said that this is still a significant shift in the industry. Argo Group underwriters are now able to manage portfolios rather than reviewing every submission. Breen stated that machine learning algorithms handle the more predictable claims. The human underwriter, however, is responsible for fine-tuning the process and intervening in cases that require higher-order decision-making.
Porges believes there is more opportunity to streamline the underwriting process. As machine learning becomes more mainstream in the insurance industry, Porges expects that there will be fewer applications that a human underwriter must handle.
Porges stated that technology and machine-learning can eliminate a lot [of human underwriting]. “The percentage of insurance applications that need human touch will drop dramatically, possibly 80% to 90% and even to the single digits,” Porges said.
Although AI adoption is still in its infancy, it has already had a profound impact on the landscape.
He stated that companies can be prepared and remain competitive by assessing the impact of machine-learning on their businesses by prototyping their algorithms. A machine learning algorithm can be used to analyze a single case on its own. In many cases, it is cheaper than a standalone analysis tool.