Insurance Transparency Optimized: Why Machine Learning for Insurance Pricing is a Game-Changer
The emergence of machine learning (ML) algorithms has opened new possibilities for revolutionizing insurance pricing, helping actuaries and pricing teams to make better decisions, faster.

In the insurance industry, pricing processes have remained largely unchanged for the past three decades. However, the emergence of machine learning (ML) algorithms has opened new possibilities for revolutionizing insurance pricing, helping actuaries and pricing teams to make better decisions, faster. This article explores the transformative power of machine learning for insurance pricing and highlights its significance in today's rapidly evolving insurance landscape.

Actuaries face a plethora of difficulties in delivering the most accurate pricing that take into account the vagaries of risk and data about past claims. These include:

• Cumbersome data preparation
• Utilizing black box ML algorithms
• IT dependency

All of these are usually key elements of the price modelling process.

Using ML driven portfolio management tools however allows pricing experts to identify profitable or non-profitable segments in a timely manner and take action in real time.

All these issues on the pricing front are significant. Insurance companies experience poor customer retention rates (up to 9%), quantifiable time loss, and higher loss ratios in new business of up to 5%.

Artificial intelligence (AI) is transforming the insurance underwriting process by increasing its efficiency and accuracy. Insurers can customize policies for each customer's needs and move to dynamic pricing, by utilizing predictive analytics to better understand risk and provide real-time data for quotes on demand.

Actuaries and pricing experts spend too many hours on administrative and data-entry duties. In fact, a recent survey indicated that actuaries spend more than 50% of their time on data issues. But with the help of new ML technologies, actuaries can forget about non-core tasks. They can focus on where they’re needed the most: performing complicated analysis, and scenarios that require human judgment and expertise.

Below are some of the benefits of machine learning that insurers can leverage today.

Improve loss prediction
Traditionally, pricing relied on manual data preparation and mostly generalized linear models (GLM) for risk modeling. But with advancements in modeling techniques, machine learning algorithms now offer the potential to significantly improve loss prediction. By capturing intricate patterns and correlations in vast datasets, insurers can make more accurate predictions, resulting in enhanced risk pricing strategies.

Need for transparency in machine learning
One of the challenges in adopting machine learning algorithms is their black box nature, which hampers transparency. However, if you can solve this problem and deliver transparency through revealing the variables used, identifying their significance, and even their interactions, then you will be able to empower actuaries to gain insights and make informed decisions, enabling more precise and transparent risk pricing.

Inform with better data
In today's rapidly changing world, agility is crucial. Machine learning and new tech platforms allow for insurers to work with real-time data and conduct analytics, enabling prompt action and behavior correction. Precise monitoring of portfolios at a micro-segment level facilitates informed decision-making and ensures effective portfolio management.

The 360° view of the customer
To optimize pricing, insurers must obtain a comprehensive understanding of their customers. Incorporating external non-insurance data alongside insurance data provides a 360° view of the customer. This results in superior risk pricing and commercial pricing determination. This approach can even be extended to personalized behavioral pricing, tailoring premiums based on individual behaviors and circumstances.

Machine learning algorithms offer immense potential to transform insurance pricing. By improving loss prediction, enabling precise risk pricing, harnessing better data, and obtaining a complete customer profile, insurers can enhance pricing accuracy, and better serve their customers. Advanced technologies and data are already affecting distribution and underwriting, with policies being priced, purchased, and bound in real time. The impact of this technology is undeniable and will soon become table stakes for insurance companies. Therefore those embracing machine learning as an operational and business driver in the insurance industry will gain first mover advantages and stay ahead in this rapidly evolving market.

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