Top 6 Use Cases of AI in Insurance Industry

Top use cases of AI in insurance industry

The insurance industry is a kind of industry that is under constant pressure to improve and evolve to stay relevant in business. This industry is based on everyday activities which involve huge data and its evaluation. A complicated assessment of data is required each day on a large scale for this industry to function. This is a growing industry; hence it requires solutions that are just as evolved.  

The modern solution for all the complicated tasks in industries is to introduce Artificial Intelligence (AI). Human interaction in industry could be reduced by establishing AI. Once AI is set in motion, the developed models take much better decisions based on facts and proof in very little time, which would eventually drive the company to succeed. 

Assessing the use cases before implementing AI is very important. This will help in understanding the actual requirements of the system and how AI can help deal with them better. Let us consider a few use cases of AI in the insurance industry. 

Claims Processing 

Claims processing is very time taking and prone to errors when done manually. Over the process, the data from the customers is received in various formats and from various sources. This makes the processing tedious and complex. 

With the digital capture technology enabled by AI, large volumes of documents can be reviewed and analyzed to know the claims, detect any fraud and make further decisions. The repair costs could also be calculated in real-time with this technology just with the help of pictures. AI can analyze everything on its own while staying up to date with the regulations.   

Insurance Pricing  

Insurance pricing is set by reviewing a particular customer’s profile. The profile of each customer is analyzed to assess the risk factors based on biometric data, health records, lab testing, etc. Once the data is assessed, the company sets certain pricing for the policy. This process is often time consuming and usually leads to irregularities in the reports generated.  

By leveraging AI pricing modelsInsurers can assess customers risk factor with the suggested parameters. Once the risk factors are set, the system can follow the regulations and set the policy price on its own. This results in faster, confident and customized pricing for each customer.

Claim Fraud Detection  

Fraudulent claims are becoming frequent as the insurers are still using the same legacy claims processes. Detecting a fraudulent claim is very difficult because usually these crimes are committed by experts, and a it takes a lot of time to get hold of accurate documents which could prove that a crime has been committed.  

With the implementation of the machine learning model, these fraud claims could be tracked more efficiently. The predictive analysis tool, powered by AI lets you detect false claims by analyzing the anomalies in the data along with a fraud risk estimate. 

Next best offer: 

Insurance customers often end up with only one policy and do not invest in any more policiesThe major reason may be that they do not get to know about the right offers for them. Customers do not have any idea of what other offers exist for them or they could not find anything that matches their requirements.  

The traditional process to suggest an offer to a customer is that insurers send out random, generic messages to all their customers, this can get annoying over time and customers might not even be interested in spending time reading them anymore. The other way is that companies manually analyze a customer profile and figure out the best offer for the customer, the problem with this is that it would take a lot of time to go through huge volumes of data and understand the eligibility of the customer and then come up with an offer.  

With the implementation of AI in the insurance industry, machine learning models based on AI can make the whole process easier. The system can go through huge volumes of data and analyze the profile of the client. This will allow it to assess the eligible offers for each client. This assessment is also based on various factors like the customer’s history of purchases and browsing. This information will help the insurer to come up with relevant offers for the clients and the messages sent to the client could be more oriented towards what’s best for the client. 

Churn analysis and prediction 

Retention of customers is very important for any business, but churn is always a risk factor reflecting the business loss. It is important to understand the customer and provide them with better services tailored to match their interests. This will help in keeping the customer tied up with the company on good terms.   

Usually, this is done by experts who go through a client’s profile and understand the interests and patterns of a customer. This way, the experts get an idea if there would be an issue in client retention. This process is time consuming and by the time the pattern comes to notice it’s too late.   

With AI-based machine learning techniques, the system can analyze the customer profile and come up with any kind of patterns which could be reviewed by the experts to come up with strategies to eliminate any chance of losing a customer.  

Customer segmentation 

When the business starts to boom it is difficult to keep track of individual customers. This also makes it difficult to understand the requirements of the customers. So, customers are segmented into various categories based on defined parameters.  

Conventional segmentation methods do not allow a greater variation and are restricted to very few parameters. It is difficult to manually evaluate each customer and understand their interests, so this process usually takes time.  

With AI-powered customer segmentation, there is a better scope for catering to the customers with relevant offers. The AI-powered segmentation takes a lot of factors into consideration, unlike the traditional ways. The system separates the clients based on customer persona, customer life cycle stage, behavior, and habits, purchase history, demographic and content engagement. This allows the company to implement strategies restricting it to certain customers and come up with strategies for each segment.   

Modern segmentation is known to yield better sales and reduce overall costs. This allows the company to know what the customers need which will help in improving their business. This process improves customer service and benefits the company. 

Every industry Including the Insurance industry constantly keeps trying hard to compete with its competitors. Every Insurer is looking forward to incorporattechnologies like AI, Machine Learning, Hyper Automation and others into the workflows to reduce the workload while increasing the efficiency.   

 Switching to AI is a major step towards achieving consistent growth while staying competitive. Talk to our Insurance Industry experts who can help you strategize and implement AI across your departments and business functions. 

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