Uses and Challenges of Machine Learning Insurance

Data is a mine of useful resources for the insurance providers. The insurers can use this resource to learn, create, deliver insurance products and services to the customers. Technologies like data mining, AI insurance, machine learning insurance, telematics powered by the Internet of Things, and many more lead to customization. In this blog, we will focus on the role of machine learning in insurance.

To begin with, let’s have a glance at what machine learning insurance is? It is the ability of the machine to analyze and interpret insurance data to predict outcomes. Data can be used to identify patterns, the behavior of customers, and accordingly plan the course of action of the organizations. Insurers are increasingly implementing machine learning to enhance their operational efficiency, improve customer service, and also to detect fraud.

Machine Learning plays a significant role in the insurance industry. It can be useful to insurers in multiple ways.

Uses of Machine Learning Insurance

1. It Creates Virtual Agents for Assisting Potential Customers - Insurance providers leverage Chatbots that appear on the agency/company website. These insurance chatbots can be programmed to provide the best and relatable response to a customer’s query. By integrating machine learning with insurance chatbots, the companies can create virtual agents that can provide proper guidance and information to the customer. This will help improve user experience and help companies achieve the desired result and attract new customers.

2. Machine Learning Insurance Underwriting Helps Determine Risk - The massive amount of quality data generated by machine learning insurance underwriting helps insurance providers determine the risk profile of the customers. It assists in underwriting risk-related potential events that are to be insured by the company.

3. It Allows Customization of Products to Cater to Individual Needs - Customization is really important to succeed in the competitive environment. Therefore, understanding individual needs and developing products to fulfill their requirement is essential. Machine Learning insurance helps insurers create customized insurance products and premiums based on individual profiles, resulting in higher customer satisfaction.

4. It Facilitates Fraud Detection in Claims - The efficient machine learning system is capable to trace patterns that help predict fraud in the case of a particular claim.

Though the implementation of machine learning is beneficial for insurance businesses, there are certain challenges of adopting the system.

Challenges of Adopting Machine Learning

1. Lack of Data - When it comes to digital transformation, the insurance industry is still budding. This leads to a lack of availability of quality data for machine learning. The data required by a machine to learn patterns has to be clearly definable for the system to obtain an unbiased conclusion. If the system is fed with raw and vague data, the results obtained by the machine will be scarcely useful.

2. Machine Learning Insurance Underwriting - As mentioned earlier, insurance companies are adopting a customer-centric approach and working to create products that fulfill individuals’ needs. They want to cast off the primitive model that was based on pricing the premium by asking customers a couple of questions and flatly determining the risk profile. But, the lack of experience and data in utilizing the machine learning insurance technology is posing a challenge in terms of underwriting policies based upon the customer-centric approach.

3. Security of Data - The remote accessibility and enhanced connectivity can be a huge threat to the security of available data. There is a constant risk of unscrupulous access to sensitive company and customer information. Moreover, buying a security system every year may not be a feasible option.

Conclusion

The challenges cannot undermine the advantages, machine learning insurance offers to the business. Leveraging advanced technology will improve the overall performance and help achieve excellent results in terms of improved CX and higher ROI.