4 Ways Machine Learning is Changing Insurance

The insurance industry works with an unimaginable amount of data. Traditionally, insurance automatically meant being buried in paperwork. With the advent of artificial intelligence, several industry domain experts have come up with potential use cases of AI and providing innovative services to target users.

While banking and financial services have had their own versions of technological journeys in the form of fin-tech, it is the insurance industry that will witness a meteoric rise in its benefits by integrating its services with the global upsurge of innovation. Over the last few years, AI has come forward to shape a totally different digital era. With the introduction of Internet Of Things(IoT), Big Data and Analytics, there is a massive flux of structured and unstructured data.

Machine learning has been able to identify and segregate all kinds of data from audio to image to video data all the while developing powerful algorithms to assist future segmentation of various kinds.

Right now, the insurance industry focuses on three key parameters for its business.

  • Reaching out to customers in an efficient and timely manner.
  • Providing the right set of products based on user personas.
  • Faster claims processing and fraud detection.

With machine learning, there are certain advantages that the insurance industry benefits from:

Claims Automation and Faster Processing:

Insurance carriers process thousands of claims in a day and as these numbers go up, the processing time increases as well. Machine learning can speed up this process by automatically processing claims through the system. Additionally, machine learning can also be utilized to detect spurious claims which would otherwise decrease processing speed if inspected manually. These innovations will not only help carriers reach a faster claims handling cycle but will also improve the quality of service they offer.

Better Algorithms Means Better Ratings:

Insurance carriers face stiff competition in a variety of things which include policy quotes, customer service, coverages offered, risk assessment as well as assistance during claims settlement. All these parameters make up for a highly competitive market segment. Today’s potential policyholders want a streamlined customer-oriented experience when it comes to buying an insurance policy with no hassle.

With the advent of machine learning, carriers can classify and calculate accurate predictive pricing models for their policies to have an edge over competitors. Additionally, instead of relying on traditional methods to assess risks like using historical data from a particular geography or assessing a person’s claims history or credit score, machine learning comes with new-age tools to come up with far more accurate analyses of individuals. Innovations like telematics which records driving behavior and patterns are used for calculating highly accurate premiums for individuals.

Better Underwriting:

Underwriting is critical to the financial world and especially to the insurance industry. Assessing the degree of risks of individuals to calculate settlement amounts as well as premium rates is definitely an area where machine learning can prove to be of great use. Underwriters navigate a massive river of data to come up with accurate premiums for each policy. It’s a difficult task ridden with uncertainties.

With an unending list of risks for every individual, the datasets can become too large to process manually. In such cases, machine learning can be applied to create models for building a comprehensive view of the insured which classifies and assess all sorts of risks that can contribute to the development of premiums and settlements. These models can be further trained to process additional data for creating and standardizing future risk factors. This, in turn, will help insurance carriers save a huge amount of money and processing time.

Personalization and Customer Segmentation:

Normally, insurance carriers offer standardized policy quotes with additional coverages based on a user’s credit score and claims history. Manually, this process relies on age-old data. However, in today’s fluctuating and dynamic environment, static data may not be helpful in determining the best policies for individuals. With the power of predictive analysis as well as big data, machine learning can create a highly personalized buying environment for a user when he/she is buying insurance.

Policy quotes can be then determined on the basis of an individual's behavior and preferences. From life-cycle to the degree of risk an individual poses, insurance carriers can determine what kind of service and coverages would sustain existing policyholders as well as attract new users. This technology can also be used to generate useful suggestions that fit certain customer segments which can, in turn, provide better business outcomes for carriers and help them increase customer retention.

In conclusion, machine learning is certainly changing the insurance industry in a variety of ways. We can only hope that with time, these processes become even more efficient as we see a rise in the use cases for newer technologies.