The role of predictive analytics in the insurance industry

Apart from the banking industry, insurance is perhaps the most critical sector in an economy. It provides businesses and individuals protection against financial risks and helps the government finance development projects without borrowing high-interest loans from local and foreign banks. 

It is no surprise then, that governments encourage citizens to invest in insurance policies, to help secure themselves against future medical emergencies, business losses, and retirement.

That said, like all other sectors in the global economy, the insurance industry was also impacted by the devastating consequences of the COVID-19 pandemic. 

The insurance market is experiencing unprecedented levels of asset risks, volatility in capital markets, and a lack of growth in the market.

In combination with the declining returns on equity in mature markets, these challenges have pushed insurance providers into investing in digital tools, like predictive data analytics, to improve operational efficiency.

In this post, we explore how predictive analytics tools are helping insurance companies improve their operations.

Why is predictive modelling important for insurance?

Insurance predictive analytics is an invaluable tool because it allows firms to operate smarter. Data analytics in the insurance industry helps agents anticipate future trends by analysing data, and these insights help them become more precise and accurate in their service offerings. For example, by assessing transactions in real-time, insurers can determine if certain requests or operations are safe or fraudulent. 

In essence, insurance firms can transition from firms that expend plenty of resources to remediate damage to firms that prevent damage.

What are the different types of predictive models?

Before we dive into how predictive analytics helps insurance companies optimise their processes, it’s important to understand what different options are available for them. These include:

• Forecast models
• Classification models
• Outlier models
• Time series models
• Clustering models

Now that we are aware of the different models available, we can dive into how they help insurance companies.

How do insurance companies use predictive analytics?

Predictive analytics in the insurance industry is applicable across a range of different functions and operations, all of which help companies work more efficiently than before. Here are a few applications of data analytics for insurance.

Pricing and risk assessment

No two insurance policyholders are the same. Depending on their level of risk, they will be paying different premiums even if they are subscribed to the same policy. That’s why risk assessment is perhaps the most important step in the insurance policy application process.

Insurance providers have always used data analytics in this process, but the recent changes in the industry have compounded the need for more robust data analytics tools like predictive analytics. 

Unlike traditional data analytics tools, predictive analytics utilises advanced machine learning and artificial intelligence algorithms. These collect, process, and analyse different types of data such as social media behaviour, criminal records, and credit reports to create a comprehensive and accurate risk assessment for each client.

Based on the predictive insights from these assessments reports, insurance providers can create a personalised pricing plan for individual customers.

Streamlining the claims process

In a traditional claims process, investigating each insurance claim takes weeks or even months, as investigation units use skills and experience to conduct their due diligence process. 

Predictive analytics, however, helps insurers streamline this process by giving insurers the ability to anticipate events before they occur and prioritise claims—shortening the claims investigation time and increasing customer satisfaction in the process.

Predictive analytics tools can also reduce the costs associated with the claims process. 

Researchers found out that more than 66% of insurers who used predictive analytics tools in their claims processes experienced a considerable reduction in processing and underwriting costs.

Using predictive analytics can also enable insurance providers to identify potentially fraudulent claims and take measures to prevent these claims from entering the investigation process, thereby reducing time and resource wastage.

Finally, predictive data analytics can help outlier claims—large unexpected claims that lead to losses for the insurance provider. 

During the height of the pandemic, many businesses had to shut down and file for insurance payouts to keep their employees on the payroll. With predictive data modelling, insurance providers can predict these kinds of events in advance and handle the claims accordingly.

Identifying potential customers

As we’ve established above, traditional insurance markets are maturing, and insurance providers are experiencing low levels of policy buy-ins. 

Insurance providers need to stay profitable in this market and for that, they need to look to newer markets or even niche customer segments in the existing markets; predictive analytics can help insurers do this.

Using the available data, predictive analytics can identify behavioural patterns of target demographics and produce actionable insights, which insurance companies can use to create new insurance products that suit those demographics.

Predictive analytics drive the insurance industry forward in a tough climate

The insurance industry has always been one of the slowest to adapt to changes and utilise new technologies. 

In the current business environment, however, this needs to change and the industry has to take a proactive stance, investing in tools like predictive analytics to improve their operational efficiency and profitability.