4 Ways in Which Predictive Analytics in Insurance is Paving the Way for the Future


Surya Choudhary Hacker Noon profile picture

@surya-choudharySurya Choudhary

Project Software Delivery Manager with demonstrated experience of 13 years expertise in Insurtech.

Since its inception, insurance has been all about making predictions. What is the probability of an instance occurrence? What is the risk factor associated with an individual? What should be an appropriate reimbursement against a policy? And the list goes on.

Fortunately, with the advent of data, predictive analytics for insurance has become easier and more accurate. With a data-based environment, it is simpler to quantify the eventualities and mathematically compute all possibilities rather than depending on one’s hunch or instincts.

Predictive Analytics in insurance deals with data available from multiple sources to discover trends and patterns, which are otherwise missed by the human eye, to forecast future outcomes. It employs technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to mimic human intuition while making data-driven decisions – essentially, the best of both worlds.

The following are the many ways in which predictive analytics in insurance is paving the way for the future of insurance:

Product and Price Optimization

One of the most significant ways in which predictive analytics in insurance will transform how the industry operates is by making it more customer-centric than product-centric. This quality means that rather than selling the customers the existing policies and other services, insurance agencies will now focus on tailoring the said products and services to meet customer requirements.

In this regard, predictive analytics can prove to be an instrumental tool in matching the policy specifications and the cost in line with the customer expectations. For instance, predictive underwriting in life insurance will dynamically adjust the quoted premiums in line with the health, life expectancy, and other factors of the individual, thereby making the risk and price, and subsequently the policy more relevant for them.

Rank and Prioritize Claims

With your claims department being flooded by work, it can be easy to feel overwhelmed by the burden of the high-volume workload. In the process, it will hamper their productivity and make it near-impossible for them to wrap up the tasks at hand.

However, they can use predictive analysis for ranking these claims in the order of their impact, importance, and complexity. By doing so, you will be creating a hierarchy and lending direction on how to take on the mammoth list of activities. Once the priority has been set aside, you can go through the ranks and clear the most pressing ones to manage the severity in any form.

Effective Staff Assignment

When it comes to settling claims or engaging with customers, you may notice that certain employees have a knack for a certain target audience. Striking the right frequency as your customers could be a result of their ingrained personality or a direct result of their skills and competencies.

In either case, predictive analytics in insurance can also auto-match the right task with the right personnel so that businesses can capitalize on every opportunity, be it revenue generation or customer engagement. Eventually, it will all seep into the customer experience and it is no secret that an improved customer experience will translate into greater profitability.

Fraud Prevention and Risk Mitigation

Using predictive analytics for insurance risk calculation is almost as intuitive as pairing bread with butter. If anything, the use of predictive analytics in insurance is the smartest and most future-forward way to prevent fraud and mitigate risks proactively and reactively.

As discussed previously, predictive analytics in insurance uses AI and ML to detect patterns, which could point towards fraudulent activities or instances where the policies were being manipulated. By discovering such ill-practices right in the early stages, it will be easier for insurance companies to plug in the leakages and contain the damaging effects of fraud.

Final Thoughts

Stating that the role of predictive analytics in insurance is critical would be a gross understatement. If anything, it is the technology of the future that is facilitating the groundbreaking trends that are disrupting the industry at present.

It is only a matter of time until insurance agencies discover the true potential and capabilities of predictive analytics and how it can add business value and help scale operations with time. It is better to ride on this wave at present to gain a competitive edge than to wait until it becomes the new norm.


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