Big Data In Insurance Underwriting - 6 Ways Early Majority Benefits

The US insurance industry is one of the largest markets in the world, with an estimated 1.3 trillion dollars worth of written insurance premiums in 2020 and growing developments in big data insurance underwriting. The industry shrunk slightly in 2020 due to the COVID-19 pandemic. 

Given the sheer size, stakes in insurance are relatively high, especially regarding:

• Segment classification
• Premium pricing
• Premium collection
• Claims filing and resolution

Another significant concern is rising cases of fraud, which has been attributed to the industry's lucrativeness.

Despite this, proactive insurers can harness the power to mitigate the numerous risks and improve their profitability and customer satisfaction.

Read on to learn how early majority companies can benefit from big data insurance underwriting.

Big Data Insurance Underwriting: How It Works and Why It's Superior

Big data insurance underwriting relies on big data technology and Artificial Intelligence (AI) to improve risk assessment.

It uses the superior data collection and analysis capabilities of AI and machine learning. It can:

• Determine various risk profiles
• Understand customer expectations 
• Create pricing that's responsive to the insurer's expected return, risk, and customer expectations

Big data insurance underwriting is superior to traditional insurance underwriting because it has advanced data collection involving alternative data sources and analysis capabilities, relatively lower operational costs, and the potential to help insurers tap into new market segments with better economic outcomes for customers and insurers.

Who Are the Early Majority?

The early majority refers to individuals or companies that pave the way for innovation and their use in society. 

E.M Rogers coined this term while developing the diffusion of innovations theory. In his research, he established there are five main groups of people/organizations involved with all innovations, namely:

1. Innovators. This refers to parties that are actively involved in creating new solutions.
2. Early adopters. These adopters are individuals interested in innovations and the solutions they bring to society.
3. Early majority. These are firms or individuals interested in the innovations. They pave the way by incorporating them into their solutions and businesses.
4. Late majority. These are parties that adopt innovations after the early majority, once they see the benefits from the innovation.
5. Laggards. Laggards are the last group to adopt an innovation if they do so at all. They're risk-averse and prefer working with technology they're used to, even when it's obsolete.

Benefits of Big Data Insurance Underwriting for the Early Majority

The major advantage the early majority has is they get to utilize revolutionary technology long before other users are any the wiser. They often get to do so at a better price. 

While this is the same for early adopters, the early majority don't bear the risk of the new technology failing; the concept has already been proven viable by the innovators and early adopters.

Some of the benefits the early majority can enjoy from big data insurance underwriting include:

1. Better Data Accuracy and Relevance

One of the biggest benefits of big data insurance underwriting is its advanced data collection and analysis capabilities. This helps insurers provide customized coverage to their customers and better manage risks associated with certain market segments.

For example, an insurer can use big data to identify drivers who have been involved in accidents in the past. This information can be used to determine which drivers are likely to be involved in accidents in the future. This way, insurers can tailor their coverage specifically to protect their customers.

2. Improved Customer Acquisition and Retention

Like any other business, insurance companies need a constant flow of customers, or policy buyers. By using big data to understand customers' behavior, insurers can provide them with more personalized services and better rates. This gives customers a better experience when shopping for or applying for insurance.

In addition, by understanding the risks associated with different insurance market segments, insurers can offer more comprehensive coverage to their customers. This helps reduce the number of claims filed and generates lower premiums for everyone in the pool.

Big data insurance underwriting can improve the customer experience throughout their customer journey. For instance, better customer experiences and satisfaction will eventually create an army of loyal customers. Those customers might then refer other potential clients by speaking about the great experience they had. 

At the awareness stage, potential clients will be wowed by the positive reviews and testimonials, prompting them to commit to purchasing a policy. Later, they'll become happy clients and an unending source of referrals, thanks to the better customer experience.

3. Reduced Fraud Incidence

Fraud is a significant yet rampant concern in the insurance industry, with two-thirds of insurers claiming the practice is rising. It also cuts across different segments, i.e., property and medical insurance, and affects policyholders and insurers.

Policyholders lose their insurance compensation to fraudulent actors, causing losses to insurers who now have to compensate genuine policyholders again. Consequently, insurers increase premiums to cover the increased risk, making it costly to customers. Over time, this may dent a company's customer acquisition and retention rates.

With big data insurance underwriting, insurers can use data mining and analytics to identify patterns in claims information and make more accurate predictions about which customers are likely to file fraudulent claims and whether they're valid.

This reduces the need for insurers to pay out fraudulent claims, which helps lower costs for all policyholders. In addition, by understanding which customers are most likely to file fraudulent claims, insurers can provide them with customized protection products that minimize their risk. This leads to lower premiums for everyone in the market, including the early majority themselves.

Fraud also occurs internally and primarily in three main forms:

• Fee churning. Several intermediaries are involved in transactions, resulting in too many fees and eventually less money to insurance companies from premiums.
• Asset diversion. Certain individuals take advantage of events like mergers and acquisitions to misappropriate an insurance company's assets for their gain.
• Premium diversion. This is a practice where agents fail to remit premium payments from customers to the insurance companies.

Early majority companies can harness big data technology for better record and analysis of premium payments to curb possible premium diversion, use AI to cut unnecessary brokers, and better keep stock of and manage company assets. 

4. Enhanced Competitive Advantage

Big data insurance underwriting gives companies better and more informed clarity on the risks associated with specific customer segments and products. 

This helps them reduce their overall risk exposure. They can also anticipate and manage potential liability claims and customer expectations better, thus protecting themselves from catastrophic financial losses.

In effect, early majority companies can offer lower premium rates and personalized products and services than their competitors. This leads to more revenue, higher profitability, and enhanced customer satisfaction.

5. Better Market Segmentation, Targeting, and Effective Pricing

By leveraging the superior data collection and analysis capabilities of big data insurance underwriting, the early majority can easily and accurately identify each customer's unique risks and then price policies accordingly.

Companies can predict trends and make informed decisions on their insurance policy offerings through regular collection and analysis of key data on accidents, claims, and demographic information. This allows insurers to create more effective policies and pricing schemes that are responsive to the market needs while also addressing the risk aspect with each market segment or customer.

6. Cost-Efficient Operations

Traditional insurance underwriting typically involves many activities and a significant amount of human resources — with good reason. Fortunately for the early majority, they can leverage the power of artificial intelligence and big data to accurately perform most of the numerous and repetitive yet crucial tasks in underwriting, claim filing, and claim payment resolution.

Staff can then be deployed elsewhere in other critical departments, reducing job redundancies and possible cost implications from extra wages.

Further, AI, a key component of big data insurance underwriting, isn't prone to fatigue. It accomplishes tasks effectively and at a faster rate than humans, consequently reducing operational cost overheads.

Final Thoughts on How Early Majority Companies Benefit From Big Data Insurance Underwriting

Big data insurance underwriting presents a unique opportunity for insurance companies to differentiate themselves from the competition. However, timing is critical if they're to reap the untapped potential and numerous benefits big data has to offer.

Get started with Pilotbird's underwriting and analytics insurance solutions.

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