Marketing Team

Jul 23 2018 in Blogs

How Machine Learning is aiding Insurers

At its core, machine learning is the science of finding patterns in large volumes of data in an automated manner using complex algorithms. Since the key focus, as well as the most pressing challenge for insurance companies today is to improve compliance, refine cost structures, and stay ahead of the competition by predicting the types of insurance and coverage plans new customers want and are likely to buy, machine learning is proving to be a game-changer because with its predictive and descriptive data models. Artificial intelligence and machine learning help insure companies reduce the time-to-market for their new product launches, better operational efficiency, and help businesses devise more intelligent ways not only to sell to potential new customers, but also service existing ones.

With the emergence of machine learning, it is not only possible for insurance companies to undertake big data analysis from multiple sources, but also capture new data sources such as the Internet of Things (IoT), telematics, social listening, etc. It is no longer incumbent on companies to depend on historical and current data alone to build their future business models on, with the help of AI and machine learning, seemingly dissimilar data in structured, unstructured or semi-structured formats can be computed and made sense of to understand what is happening in the market, understand dynamic customer needs in great depth, and cater to them. In machine learning, algorithms are adaptive, and as new, real-time data becomes available, the systems keep adapting incrementally, facilitating a continuous learning process.

It is understandable then, why recent market research by Technavio suggests that the global insurance-tech market expected to grow steadily with a compound annual growth rate of more than 10 percent by 2020.

Some of the key advantages and features of machine learning in insurance are:

Enhanced operational efficiency

Deep, adaptive learning can and will change and improve the way insurers do business. With the help of machine learning and the data available on the first day itself, insurers can predict premiums, conversion, and losses to a high degree of accuracy in most cases. This helps underwriters focus on more valuable business, improves the chances of converting a lead into actual business, and the turnaround time. In the longer run, the lower manpower requirement, leads to huge savings in overhead costs for companies.

Personalised products

Big data analysis makes it possible for insurers to connect with new customers at new touch points, by learning about individual needs on a more granular level. It also helps them come up with products and services that are more relevant to the audience they are trying to convert. Providing timely and relevant reminders to complete necessary transactions not only makes a customer’s experience with the company and the product more seamless and stress-free, making it easier to retain them in a hyper-competitive market, which is ultimately good for the insurer’s bottom line.  

Relevant marketing and communication efforts

Conducting business with the help of AI and machine learning is a model built on continuous learning based with the help of new data. When systems are adapted to act upon new information that is the output of adaptive machine learning, it helps insurers market and communicate more efficiently by increasing specific offerings based on geographic and socioeconomic data analysis, while reducing communication that is irrelevant to lead-to-conversion purposes.    

Fraud detection

According to research by Indiaforensic Research, India’s insurance sector loses about Rs 300 billion annually due to fraud, which is roughly 8.5 percent of total industry revenue. Apart from the money lost on fraudulent claims, investigating every claim for the possibility of fraud is a tedious and slow process, impeding the industry’s growth. Machine learning enables insurers to identify claims that warrant deeper investigation, making the investigative process more focused and efficient, with a higher return on investment by deploying resources only in those areas that are likely to be serious threats. Customer satisfaction also increases, because legitimate claims are not constantly challenged.

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