You will rarely find an industry that’s not been hugely impacted by the forward march of artificial intelligence, machine learning, and other emerging technologies.
The insurance industry is no different.
There are quite a few promising areas of practice in the insurance industry that have been rapidly changing with the advanced use of embedded analytics, AI, ML, and Robotic Process Automation (RPA) technologies.
I host a data analytics podcast where top minds in the industry share all the valuable insider insights into how these dynamic data operations work in running modern high-performing financial organisations.
In this article, I will be sharing some of the major use cases of AI, ML, data analytics, and automation in the insurance industry to make sure you are not missing out on any of these beneficial use cases of AI.
AI in Customer Contact Centres
The pervasive use of AI in contact centres is not specific to the insurance industry. It’s a manifestation of the overall implementation of AI technologies that is going on across industries.
Insurance companies are also heaping many benefits by utilising AI chatbots running on natural language processing (NLP).
Research and development on these NLP AI assistants are going on rapidly. There are also voice-enabled customer service representatives communicating in real-time.
These AI reps can forward the customer to a human customer service agent whenever it runs out of its capability, offering a seamless experience for the customer.
Another great use case of using chatbots or virtual assistants is to help the agents themselves.
The agents can interact with these bots to access whatever data or information they need in a faster and easier manner instead of scourging the siloed databases for hours.
It enables the agents to do their job easily & effortlessly without clogging the human contact centres.
Insurance Claims Processing
Improving insurance claims experience using machine learning, AI, and deep learning is a specific use of data technologies in the insurance industry.
The complete claims processing lifecycle from filing insurance claims to approval has been automated in numerous insurance sectors in recent years.
For example, auto insurance claims processing has been fully automated by many insurance providers worldwide, such as zero-touch claims processing using IBM Watson and computer vision at Suncorp.
The customer can simply apply online and submit a few photos of the damage to their vehicle. Then, the image processing AI bots can assess the damage and approve the claims.
With AI mobile apps trained to assess car damages after an accident, it would be possible to get damage-specific repair costs in real-time and to file for claims with virtual claim adjusters.
AI is also helping with fraud claims detection, claims routing, claims triage, and claims management utilising big data.
In case of claims where complete automation is not possible, these AI tools can help streamline the process or facilitate partial automation.
Risk Assessment and Underwriting
Risk assessment and underwriting are also areas where AI technologies have been used with great success.
AI models trained with historical data and all the ever-emerging new data sources, like demographics and behavioural data sets, are securely assessing risks associated with particular policies far efficiently with lightning speed.
Online interfaces and virtual claims adjusters can pull on customers’ geographic and social data for personalised interactions in real-time.
Data sources like satellite imageries and aeroplane imageries are getting easy to access via machine learning models and computer vision.
All these data sources, coupled with the analytics power of today, make risk assessment procedures very efficient.
It has also resulted in the transition from static to dynamic underwriting.
Place-Based Analytics
Another ingenious use case of data analytics and AI in insurance is the placed-based insurance pricing, risk analysis, and policymaking.
Armed with all the historical data sets on weather patterns and natural disaster occurrences, the AI model can predict the risk associated with a property located in certain locations with creepy accuracy.
This place-based analytics and use of AI offers yet another avenue for insurers for perils pricing & address-level individualised premiums pricing and product development.
The insurance companies can also use that data to forecast the propensity of future insurance claims coming from particular regions and better prepare to handle any sudden upsurge of claims occurring from natural calamities.
They can even streamline their reinsurance strategy in line with these insights gained from the AI tools.
Personalised Insurance Pricing
Combining all other factors described above, like place-based geo data, demographic data, and historical customer behavioural and lifestyle data, the insurers are in a far better position to offer an all-encompassing personalised insurance service.
A glowing example of this is the use of dashboard cam data to assess responsible driving and facilitate favourable policy for responsible drivers.
Yet another example is the use of wearables and IoT peripherals to assess the healthy lifestyle of health insurance customers.
These sensor data are fed into personalised pricing models that enable safer drivers to pay less on auto insurance or people with healthy lifestyles to pay less for health insurance.
Insurance Product Innovation
Now, bring all the technology, all the data available, and the advanced AI/ML models at your disposal to the table and imagine how much flexibility and strength you will have for streamlining your whole insurance product development process.
All this technology can revolutionise the already existing policy lines and offer immense help in innovating new insurance lines.
On top of that, the high accuracy of predictive data analytics will ensure that your new products see a successful run in the market.
At the forefront of innovation, some insurtechs are even being able to better protect policyholders from dangers by analysing and acting upon data from IoT devices.
Some new emerging models allow analytics professionals and actuaries to incorporate additional factors like linear and nonlinear relationships among variables and expert opinion for product innovation.
Insurers could benefit from this new model in multiple ways, including understanding the mortality modelling using individual-level data, solving risk classification problems for insurance claims, microsimulation health models, long term care insurance pricing, and much more.
Key Takeaways
The insurance industry is a competitive sector.
As customers are gradually becoming very selective, the insurance industry is turning to machine learning & artificial intelligence to create a better customer experience.
Even a few years ago, no one could imagine how these data-driven AI/ML technologies could solve so many complex problems for the insurance industry.
By nature, insurance operations are data-intensive.
Traditionally, the underwriters would have to manually look into various actuarial data and claims data using different statistical methods to develop pricing, premiums, and insurance products.
Back then, all this data used to sit in different siloed computer systems with virtually no sharing among agents, brokers, underwriters, and also among different offices.
Now imagine a centralised data analytics and AI platform providing the power of limitless possibilities to all the parties. Wouldn’t that be great?
