Top 6 High-Impact Applications of AI in Motor Insurance Market – 2022

 

In recent years, Insurance companies implemented Artificial Intelligence to increase efficiency in underwriting, claims processing, risk analysis, and product creation while also improving the client experience. 

 

Tasks that used to take months to perform may now be finished in a matter of minutes, allowing insurers to realise significant cost savings. Furthermore, Its capabilities have been taken to the next level to evaluate risk and create new products by allowing companies to concentrate on tasks that offer value and by reducing administrative and process-related duties.

 

The Industry leaders have automated the claims process and claims payouts calculation accurately, based on several elements such as administration procedure, impressions such as accidental history, driving records, and others. 

 

Companies witnessed the improvements in their operational efficiency in Auto claims processing, such as preventing payment oversight and reducing the cost of labour. The insurers increased their productivity by 40 per cent and improved the accuracy of payouts. 

 

Here are the Top 6 elements that improved the Motor Insurance industry by implementing AI.

 

1. CREATING MORE-VALUABLE INSIGHTS

 

 

In this Digitization era, Firms have fast and cost-effective product innovations such as on-demand insurance, micro insurance, and usage-based insurance. To implement this, they need real-time input and analysis of different types of data from sensors or other data transmission devices using AI models which are capable of unlocking value from data in real-time. Speeding up the processes, and bringing new and innovative products. 

 

Some companies use social media platforms such as Messaging applications to predict the drivers’ behaviour and get insights. The insurers have partnered with Telecom companies to provide the underlying telematics services just to have a perception of the customers.

 

Some Insurers have built a large data stack on TensorFlow, Hadoop and Apache spark and collected data from different systems such as CRM data, customer data, and third-party data by machine learning from open sources to harness the data.  

 

The insurers were able to obtain better customer intelligence for improved marketing campaigns to current and new customers, improved product recommendations, and more effective cross- and up-selling, which has been reflected in improved customer service and increased revenue streams along with future innovation across the industry.

 

2. REAL-TIME CLAIM VALUATION

 

 

One of the hardships is untimely processing and customer dissatisfaction in claim processing again the saviour is AI. Which is used to provide better communication through intelligent automation. Companies will extract predictions for claims based on information available, assign complex claims to experienced adjusters, and track  claims leakage from the audit process. 

 

With AI insurers are triaging claims based on the assessment of images. By viewing images of vehicle damage, AI  Approval assesses within seconds,  reviewing and authorising  Repair estimates,  ensuring reliability and speed throughout the claims process.

 

3. USAGE-BASED PERSONALISED SERVICES

 

 

Insurers are integrating proprietary data and new sources of data from digital & social media and sensors & connected devices. Which will be more tailored in terms of experiences that can be created by identifying customer segments for personalization analysis of feedback. 

 

Implementing AI in personalisation increased profits significantly. In the retail industry, companies are using machine-learning algorithms to develop complex models to optimise customer lifetime value and increase opportunities for cross-selling and product recommendation. 

 

Market leaders are currently using machine learning and recommendation engines  to obtain better customer intelligence for improved personalised services to current and new customers, improved product recommendations, and more effective cross- and up-selling. Insurance companies benefit from rich contextualised customer data optimising customer lifetime value through product recommendations.

 

4. PRICE OPTIMIZATION

 

 

This is one of the variables that AI and ML can increase the insurer’s profitability by filtering pricing models through data generated from next-generation telematics.  Most efficient  & reliable techniques are mainly based on statistical methods such as  General  Linear  Models  (GLMs)  which analyse large structured and unstructured  data factoring different variables.  

 

The AI and ML  can build tools on top of these models for non-technical users, combined with a recommendation engine to enable prescriptive analytics. It is best suited for the delivery of a clearer understanding of risk, operational resilience, and improved customer interactions, in particular from the underwriting and pricing purview.  Insurers are yet to launch the full breadth of services across motor insurance. 

 

 

5. MONITORING DRIVER PERFORMANCE

 

 

AI systems can continuously monitor a driver’s behaviour, traffic conditions, and data from in-car sensors. supported the analysis of this data, the system can detect the danger of a road accident faster than a driver and alert the motive force about it. 

 

Also, monitoring data will be accustomed assist drivers in improving their driving skills and developing safer driving habits. Car insurers will enjoy deploying an AI-powered driver monitoring system to teach new drivers and reduce the general risk of collisions on the road.

 

6. RECONSTRUCTING INCIDENTS

 

 

Another promising possibility is to use AI for car accident claims, particularly to recreate the main points of a car accident supported by dash camera footage and in-car sensor data.  While doing so, they will also depend upon AI to detect auto insurance fraud.  

 

Having an explicit, AI-generated timeline of an accident quickens accident investigation and claims processing while reducing the danger of fraudulent claims.

 

CONCLUSION

While holding promising potential, AI technologies can be challenging to implement, which needs to be overcome. In real-case scenarios, AI has solved the most complex problems by using deep neural networks to analyse large amounts of customer data to predict potential losses and optimise prices for their motor insurance policies. 

 

The accuracy rates have gone up by ~50 to 70 per cent when compared without AI implementation and Insurers were able to achieve higher profits, involving developing new insurance products like point-of-sale real-time pricing.

 

Many insurtechs are innovating in the domain of contextualised predictive  algorithms coupled with industry data consortiums,  which addresses one of the  weakest operational areas among many established giant tech providers which has only spread across developed nations and it will enlarge in third world countries.

 

 

 

Akshay Prakash, Research Analyst at Mobility Foresights