Evaluating the role of AI data analytics in the insurance value chain

Like other industries, the insurance sector has witnessed the impact of AI first-hand. Notably, in commercial insurance, AI has automated processes, driving speed and efficiency and expanding data ingestion and processing capacity.

New tech capabilities like Artificial Intelligence, Machine Learning, Deep Learning, and computing are increasingly deployed to enhance decision-making and productivity, lower costs, and optimize the customer experience. These tech-based solutions are changing the data analytics landscape in significant ways. AI data analytics provide greater depth and assurance in the analysis and predictions.

According to research, the insurance sector is gradually shifting from its current state of ‘detect and repair’ to ‘predict and prevent’ with the help of big data analytics and other tech-based solutions as it approaches 2030.¹

Evaluating the role of AI data analytics in the insurance value chain

When it comes to AI data analytics capabilities, there are a plethora of options to explore, especially for insurance providers. Across the insurance landscape, more accurate and detailed models are trained on diverse datasets to derive in-depth analytics used for predictions. Further, a new set of big analytics tools are applied to extract new types of data that were never available to the insurance businesses before.

For example, live driver feedback related to automotive insurance helps with better fraud detection at a much larger scale and based on subtle patterns. Then, there are live data collections via mobile apps, along with the more traditional statistical models considering larger and more complex data sources, Natural Language Processing for text mining, and many more.

Taking the same automotive insurance as an example, insurance providers are better positioned to ascertain the punctual and dynamic riskiness of who drives what, how, and in which context. With the help of big data analytics, the former can improve risk segmentation and selection and help make the world more resilient.

Insurance providers expect AI data analytics to help them anticipate risks in a timely way to boost risk predictions and enhance risk assessment. Quantifying big data analytics is pivotal in drawing useful meaning from those extracted insights. Insurance providers use such insights across insurance types and risk profiles to understand the landscape and make smarter choices for entering into profitable endeavors.

Challenges and benefits of AI data analytics for insurance providers

New technologies like AI and ML thrive in the presence of extensive, structured data and are helpful for applications that work with unstructured data.

However, insurers also face challenges when implementing AI into their use cases like any other business. One of them is the ethical challenge. Secondly, AI systems are prone to introducing bias in decision-making. The only way to get rid of bias is by making these systems transparent to allow for better scrutiny and address any bias present in the processes.

As mentioned earlier, AI systems require large amounts of high-quality, relevant data. And the insurance sector works with pools of data that should be treated with care, and with the help of AI data analytics, insights can be drawn out without so much as human bias discoloring the outcomes.

Once the initial challenges are addressed, AI data analytics can help insurance providers in multiple ways.

Conclusion

The insurance industry can experience the untold benefits of using AI data analytics to serve customers better and gain an edge in the market over other players, a few of which have been elucidated before. Experimenting with new-age technologies in the insurance sector is still on, and businesses are finding different ways to leverage them to handle more complex processes. And this is where AI data analytics plays a significant role.