The Role of AI in Insurance Pricing

The Key Ingredient to Competing in Today’s World

For centuries, insurers have been on the forefront of applying analytics to pricing. This aspect of their operations has been critical to the development of the industry, and is more important today than ever before.

In a world of uncertain economic conditions, hyper-competition, and unpredictable world events, the advantages of artificial intelligence (AI) and machine learning (ML) in insurance can revolutionize pricing strategies and streamline consumer experiences.

Insurance Pricing Before the Advent of AI

Well before computers entered the scene, insurers were using predictive modeling. As far back as the 19^th century, the early development of actuarial science spawned the use of linear regression models and probability theory to calculate premiums.

In the mid-20^th century, insurers were among the first to realize the potential of computers in streamlining and improving the productivity of their businesses.

Travelers Insurance was among the first to employ IBM mainframes for administrative tasks (billing, claims processing, accounting, financial reporting, etc.), and other insurers followed suit, making the industry among the pioneers in applying computing to business operations. Over time, their growing technology needs helped drive the development of minicomputers, PCs, and networking, among other innovations.

Realizing the potential for applying computing power and the vast amounts of data now at their disposal, insurers expanded their horizons to the world of pricing and underwriting, and earlier models gave way to generalized linear models (GLMs) and decision trees, allowing for the incorporation of multivariate risk factors to increase the accuracy of premium calculations.

At the beginning of the 21^st century, continuing growth in computing power, its declining costs, and growing pervasiveness drove the use of machine learning in insurance for demand modeling.

AI in Insurance Today – the Driving Factors

Since then, AI in its various forms has found its way into the mainstream of nearly every aspect of the insurance sector, including risk assessment, pricing, rating, and underwriting.

With the rise of the Internet, insurers have seen the growing need to communicate directly with their prospects, customers, and producers to present timely pricing offers, policy alternatives, customer support options, renewals, and all other aspects of doing business across the entire customer lifecycle or the full customer experience (CX).

This has put particular pressure on insurance pricing, as the need for instant quotes, the incorporation of Internet of Things (IoT) data, and demands for personalization have driven intense competition for consumer attention and dollars.

Insurers are looking to take advantage of the power of Intelligent Operations, or Intelligent InsurOps, which will allow them to tailor every customer interaction to the individual, driving faster revenue realization, enabling market share gains, raising customer satisfaction, lowering costs, and increasing profitability.

The Role of AI Pricing in Insurance

AI enables a number of capabilities for insurers that allow them to compete more effectively in a fast-moving world:

1. AI and Dynamic Pricing

 Dynamic pricing, changing the price for a product or service, has permeated the online world across industries, and is a familiar and accepted practice among consumers. They are well-versed in how airlines and hotels, for example, adjust pricing to seasonality, days of the week, desirability of locations, and to clear off their excess inventory.

In the insurance world, within the bounds of regulatory requirements, factors such as consumer preference, location, claims history, purchase propensity and offer attractiveness can all be combined in pricing that varies with each customer or prospect.

AI allows insurers to implement the most flexible pricing strategies possible. It enhances transparency and fairness, in addition to adapting quickly to changing market conditions and customer requirements, builds new flexibility and agility within the organization, and allows insurers to close more business with strong governance.

2. Personalization

Based on their interactions with vendors in other industries, consumers are demanding more and more personalization in their dealings with insurers. They are looking to be more than a number or a member of the “unwashed masses,” and expect a level of attention well beyond what can be delivered based on demographics, geography, or income.

They have come to expect personalization at every step of the insurance customer experience (CX): marketing, initiation, claims management, policy renewal, and offers of new or complementary products and services (upselling and cross-selling).

One key to making greater personalization work is effectively leveraging massive amounts of data. Without the use of AI and ML in insurance executions the prospect of making consumer personalization a reality lands somewhere between daunting and impossible.

Insurers are sitting on huge stores of internal data about their current customers, prospects, claims, lost business, pricing sensitivities, and written policies.

Add in external data such as economic projections, changing demographics, climate data, and competition, and “separating the wheat from the chaff” becomes nearly impossible without the application of modern analytics.

Carriers can leverage AI to break out of their “one-size-fits-all” thinking and provide insurance solutions based on context and a new level of customer-centricity.

By being more flexible and agile and employing a data-driven approach, insurers will propel themselves into a world in which consumers find their offerings infinitely more relevant and attractive.

3. Integrating Pricing and Underwriting

In addition to the “front office” and customer-facing measures outlined above, AI enables a digital transition in the underwriting function and an ability to tie it more directly with pricing.

The way that underwriting rules are developed and deployed today is a major culprit in limiting insurers’ speed and effectiveness, and it ties up underwriters in a cycle of repetitive and unproductive work.

The pre-AI underwriting process lacks flexibility, wastes valuable much time, and multiple handoffs across functions increase the probability of errors, which in turn requires additional review, simulation, and validation before putting a new rule into production.

If underwriting is not tied directly into what pricing and rating are doing by leveraging AI, these adjacent functions lose some of their productivity gains in the cycle of constantly re-interpreting rules and then testing, implementing, and managing them.

Using AI and ML in the insurance industry acts as a powerful “glue” between all these related functions (as well as sales, marketing, claims, and customer service) aligns goals, functions, KPIs and implementation, resulting in previously-unattainable internal collaboration, a unified face to consumers, and a consistent and timely customer experience.

The Earnix Solution

The Earnix solution capitalizes on advanced AI technologies to bring the best in pricing and rating to insurers.

In today’s world, AI insurance pricing enables the rewarding experiences that customers and prospects crave. Pricing teams can deploy dynamic pricing that moves at the speed of the market, and pricing models can be scaled up to meet expanding markets and maintained with ease.

These agile insurance solutions integrate with existing systems and infuse automation and industry-leading analytics into every aspect of the pricing process.

The heart of the solution is Earnix Price-It^TM, a single-platform solution which includes integrated modeling, simulation, fine-tuning, and deployment, allowing insurers to bring personalized and innovative products to market faster, while maximizing ROI on their technology investments and ensuring strong governance and regulatory compliance.

Complementing Price-It and the Earnix Enterprise Rating Engine is the specialized underwriting software of Earnix Underwrite-It^TM.

Together all these capabilities deliver composable, agile, and real-time-capable technology, a coordinated, intelligent layer of insurance solutions.

Conclusion

We’ve seen how AI pricing has progressed in the insurance world, and how through the adoption of Intelligent Operations insurers can compete more effectively for new business and maximize customer retention and lifetime value.

The rewards include increased revenue, lower costs and overhead, and new levels of profitability.

We invite you to learn more today about how Earnix delivers on the promises of this new world of AI and ML insurance pricing.