In today’s world, data is considered to be a mine of useful resources. Companies can mine this resource to learn, create, deliver and sustain. Since insurance is a traditional business, big players were initially reluctant to deviate from an approach that was tried and tested through the ages. This led to a certain level of stagnancy. Similar products were distributed for a generalized customer profile. Customizations was out of the question in such a situation and growth through innovation was a far fetched goal.
Technological advancements began to take place rapidly from 1960-70s when the internet was introduced. The world came closer and ideas were shared at a faster pace. This led to an explosion of innovation and proved to be a silver lining for the insurance industry. Today, technologies like data mining, artificial intelligence and machine learning, telematics powered by the Internet of Things, and many more have paved way for custom products and fair pricing. In this article, we will discuss the role of machine learning in the insurance industry.
- What is Machine Learning?
- Practical Uses of Machine Learning in Insurance
- Challenges that insurance companies face while adopting machine learning
What is Machine Learning?
In layman terms, machine learning is the ability of a machine to parse and understand data (examples, use cases, history) to predict certain outcomes. Data can be studied to identify patterns and then decide a course of action depending upon the observations. Machine learning can be classified into two types – supervised learning and unsupervised learning. Supervised learning may require human intervention or a pre-existing dataset can be fed to the system for the purpose of predicting patterns and outcomes. On the other hand, when the system learns to detect patterns and create clusters from raw data, it is termed as unsupervised learning. In the insurance industry, both types of machine learning abilities can be put to use.
Practical Uses of Machine Learning in Insurance
Virtual assistant for advising potential customers
Many insurance companies have fancy looking chat-bots that pop-up while a customer is browsing the website. These bots or virtual assistants can be programmed to come up with the best response to a user’s query. Integrating machine learning in a chat-bot system will be useful for achieving the desired result i.e. customer acquisition through proper guidance.
Determination of risk profiles for underwriting
With an ample amount of quality data, the risk profile of a customer can be easily determined. This will help in underwriting risk-related potential events to be insured by the company.
Custom products to suit individual needs
Indian insurers do not have a framework to design customized products for their customers. The system that determined the premium for a 20-year-old bachelor who has just learned how to drive also determines the same premium for a 35-year-old married individual. Their risk profiles could be drastically different based on their age, driving experience, driving patterns, or risks they take. The integration of machine learning will help in creating customized insurance products and premiums based on these factors, resulting in higher customer satisfaction.
Fraud Detection in Claims
Proficient machine learning systems are also able to draw patterns that predict fraud in a particular claim. If possible, collecting data from multiple insurers will help build a fraud-proof system.
Challenges that insurance companies face while adopting machine learning
#1 Availability of data
As mentioned earlier, the growth and development through innovation are still in its nascent stage, this leads to a deficit in the availability of quality data for learning. The data used by a machine to learn patterns needs to be clearly definable for the system to reach an unbiased conclusion. In case the system is fed with raw and ambiguous data, the experience gained by the machine will rarely be fruitful.
The insurance industry is embracing a customer-centric approach. Companies are looking to create products that cater to individual needs and are priced accordingly. They want to get rid of the age-old rigid pricing model that was based on charging a customer by asking a couple of questions and flatly determining the risk profile. Implementing machine learning is proving to be a challenge in terms of underwriting policies based upon the customer-centric approach due to the lack of experience and data.
The security of available data is also a challenge, thanks to remote accessibility and enhanced connectivity. The fear of sensitive data being accessed by malicious forces is huge. On the other hand, buying and continuously using high-end security software may not be a feasible option for new players.
As far as the Indian insurance industry is concerned, integrating an advanced technology like machine learning will prove to be a boon. Enhancements on various levels can be achieved to create a win-win situation for both – insured as well as the insurer.
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