#ML #Ai #Automation #Insurance#DigitalTransformation #DataAnalytics #MachineLearning

The insurance industry has always been seen as a traditional industry and slow to adopt changes, but as an industry that has been dealing with explosive volumes of data for years, consolidated below are key lessons to share on the use of artificial intelligence (AI), data analytics, and machine learning (ML) models. This would allow insurance companies to reshape and digitally transform their internal mechanisms to suit the digital era.

Explosion of Data in Insurance

Today an explosive amount of data is collected from different sources and activities by customers, but it is underutilised as many insurance organisations don’t have the expertise to converge and meaningfully apply data in ways to transform processes.

To overcome this problem, SignOn.ai has created automation mechanisms & models which can be applied for specific used cases together with proprietary technology.

Machine Learning solutions have changed how insurers use the data to deliver appropriately priced premiums to customers based on risk. The insurers now have access to thousands of attributes or data points to differentiate their pricing models. The risk models determine pricing and underwriting.

Now, one can apply machine learning algorithms to identify the most predictive set of data attributes, cancellation history and gaps for cover through a single model which generates a view. This helps insurers stay more competitive allowing them to write the risk that is right for their book of business as they refine their pricing models.

The insurance industry is applying these ideas in smaller ways.

The property or motor insurance has improved claims through virtual claims & automation where no human intervenes to decide the claim. In a particular case image recognition, video analysis combined with capturing damage & invoices, do a complete system study and when the claim meets the approved criteria, payment to the client is processed. This change not only improves efficiency, but it also cuts costs and delivers exceptional customer experience.

Another fantastic case would be about building ‘fraud’ scores based on a quote, transaction, payment & policy history into this process. This layered with the earlier virtual claims & automation would help ensure no previous ‘manipulation’ of data has occurred which would then be used to validate the automated process.

Dual validation for each automation of claim would reduce the pilferages & fraud.

Exceptional customer experience

There may be types of insurance ( health, mortgage, property ) which require customers to answer some exceptional questions before issuing the policy.

Models can help cut down the obvious questions combining historic attributes of the customer along with social listening.

The newest models combine hard to fetch data with decision models layered in ways that data together returns accurate information on the buyer and insurance. The result is quicker insurance quotes and improving conversion rates.

The biggest advantage is reducing the time for converting the quote to a policy.

Consider the broader use of data for initiatives.

In the pet health insurance space, we can apply Medical Electronics advancements with the application of the Internet of Things. A simple patient health monitoring device is developed as an IoT application. IoT devices could read the pulse rate and measure the surrounding temperature of the pet. It continuously monitors the pulse rate and surrounding temperature and updates them to an IoT platform. The readings then pass on indicators to the pet parent and the insurance company who when assigning a preventive care Vet. This reduces the claims significantly. With SignOn.ai, ML has created a separate model, detailed case study on how this will be deployed & measured.

Motor insurance industry with the usage of telematics data is used to get FNOL (first notification of loss), helping to deliver a better consumer experience post-accident, whilst providing valuable insights regarding the circumstances of the accident.

With respect to the commercial property insurance arena, Artificial Intelligence models can provide valuable insights regarding certain localities for business relocation in terms of crime rate, footfall or other local aspects that increase risk.

This insight is shared by insurers smartly to customers for them to take preventative care if they do choose to go ahead, decreasing risk, loss costs, whilst helping to improve customer experience.

Democratisation of data

Each time a consumer applies for insurance they consent for data being used to provide the insurer with the best information possible, this allows the insurer to assess the individual risk effectively and ascertain the premium attached to the risk.

The biggest benefactor with ML & AI applications in various aspects is really helping consumers understand how their data is used & they benefit from it. This then educates the customer in ways to better their premium by creating the right behaviours to reduce the risk.

With wearable tech, insurers can choose to apply discounts based on healthy and active behaviours which in turn helps the customers too.

The data being used appropriately and on time is the essence and this would result in lowering risks, benefits for the consumers and the insurance companies alike.

About the Author

Jaidev Chatanat is a thought leader in the space of marketing intelligence, customer service and digital transformation. He has been an advocate for applying the right digital transformative strategies and techniques for many decades for fortune 500 companies.

He is reachable for consultation on jaidev.c@signon.ai


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