In today’s world, data is regarded as a gold mine of useful resources. Businesses can use this resource to learn, create, deliver, and sustain themselves. Because insurance is a traditional business, major players were initially hesitant to deviate from a tried-and-true approach.

insurance machine learning

This resulted in a state of inertia. Similar products were distributed to a broad customer base. Customizations were out of the question in such a situation, and growth through innovation seemed like a pipe dream.

When the internet was introduced in the 1960s and 1970s, technological advancements began to accelerate. The world became smaller, and ideas spread more quickly. This resulted in a burst of innovation, which proved to be a silver lining for the insurance industry. 

Data mining, artificial intelligence and machine learning, telematics powered by the Internet of Things, and other technologies have paved the way for custom products and fair pricing. The role of machine learning in the insurance industry will be discussed in this article.

What exactly is Machine Learning?

In layman’s terms, machine learning is a machine’s ability to parse and understand data (examples, use cases, and history) in order to predict certain outcomes. Data can be analyzed to identify patterns and then used to determine a course of action based on the observations. 

There are two types of machine learning: supervised learning and unsupervised learning. Supervised learning may necessitate human intervention, or a previously collected dataset can be fed into the system to predict patterns and outcomes. Unsupervised learning, on the other hand, occurs when the system learns to detect patterns and create clusters from raw data. Both types of machine learning are used in the insurance industry.

Machine Learning’s Practical Applications in Insurance

Potential customer advice is provided by a virtual assistant

Many insurance companies have flashy chat-bots that appear while a customer is browsing their website. These bots or virtual assistants can be programmed to respond to a user’s query in the best way possible. Integrating machine learning into a chat-bot system will help achieve the desired result, which is customer acquisition through proper guidance.

Creating risk profiles for underwriting

A customer’s risk profile can be easily determined with a large amount of quality data. This will aid in the underwriting of risk-related potential events that the company will insure.

Individual needs can be met with customized products

Philippine insurers lack a framework for developing customized products for their customers. The same system that determines the premium for a 20-year-old bachelor who has just learned to drive also determines the premium for a 35-year-old married person. 

Their risk profiles may differ significantly depending on their age, driving experience, driving patterns, or risks they take. Machine learning integration will aid in the creation of customized insurance products and premiums based on these factors, resulting in higher customer satisfaction.

Claims Fraud Detection

Machine learning systems that are advanced can also draw patterns that predict fraud in a specific claim. Collecting data from multiple insurers, if possible, will aid in the development of a fraud-proof system.

Key Insurance Driving Machine Learning Factors

AI and advanced machine learning are two of the top ten strategic technology trends that leading companies are using to reinvent their businesses for the digital age.

The following are the key market forces driving AI and advanced machine learning adoption in 2020 and beyond:

  • Everything is smart

Enterprises want to use advanced machine learning to power smart, automated applications in areas like healthcare diagnosis, predictive maintenance, customer service, automated data centers, self-driving cars, and smart homes.

  • Everywhere there is open source

As data becomes more prevalent, open source protocols will emerge to ensure that data is shared and used consistently. Various public and private entities will collaborate to build ecosystems for sharing data across multiple use cases under a unified regulatory and cybersecurity framework.

  • Using data from the Internet of Things (IoT)

The volume and velocity of data generated by IoT will necessitate the use of advanced machine learning tools to automate the generation of actionable insight. Gartner predicts that by 2020, 20% of enterprises will have dedicated people monitoring and guiding machine learning (such as neural networks). The concept of training systems, as opposed to programming systems, will become increasingly important.

  • Capability to respond

Algorithms for natural-language processing are constantly improving. AI is improving its ability to understand spoken language and recognize faces, making it more useful and intuitive. These algorithms are evolving in unexpected ways, as Google discovered when Google Translate invented its own language to aid in translation.

The difficulties that insurance companies face when implementing machine learning

  1. Data accessibility

As previously stated, growth and development through innovation are still in their infancy, resulting in a scarcity of high-quality data for learning. For the system to reach an unbiased conclusion, the data used by a machine to learn patterns must be clearly definable. If the system is fed raw and ambiguous data, the machine’s experience will rarely be fruitful.

  1. Underwriting

The insurance industry is moving toward a more customer-centric approach. Companies are attempting to develop products that cater to individual needs and are reasonably priced. 

  1. They want to do away with the age-old rigid pricing model, which was based on charging a customer by asking a few questions and determining the risk profile flatly. Due to a lack of experience and data, implementing machine learning in terms of underwriting policies based on the customer-centric approach is proving difficult.
  2. Security

Because of remote access and enhanced connectivity, the security of available data is also a challenge. The threat of malicious forces gaining access to sensitive data is enormous. Purchasing and maintaining high-end security software, on the other hand, may not be a viable option for new players.


In terms of the Philippine insurance industry, incorporating advanced technology such as machine learning will be beneficial. Enhancements can be made at various levels to create a win-win situation for both the insured and the insurer.

Machines will play an important role in customer service, from managing the initial interaction to determining which insurance coverage a customer requires. According to a recent survey, the vast majority of consumers are content with computer-generated insurance advice.

Consumers want personalized solutions, which are made possible by machine learning algorithms that analyze their profiles and recommend products that are tailored to them. Insurers are increasingly using chatbots on messaging apps to resolve claims queries and answer simple questions on the front end.