AI balancing risk and coverage in insurance.

Decoding Insurance: Can AI Fix the Asymmetric Information Problem?

Reese Marlowe in Business & Economy September 2025 • 4 min read.

"Explore how deep learning is challenging traditional insurance models and what it means for fairness and risk assessment."

In the complex world of insurance, one persistent challenge is "asymmetric information," a situation where insurance buyers know more about their risk levels than the insurers themselves. This information gap can lead to higher premiums, adverse selection (where only high-risk individuals purchase insurance), and ultimately, an unstable market. For decades, economists have been grappling with this issue, using various statistical tests to detect and mitigate its effects.

A cornerstone of this research has been the 'positive correlation property' (PCP), which suggests that individuals who choose more comprehensive insurance coverage are, in fact, those who pose a higher risk. While seemingly intuitive, empirically validating the PCP has been surprisingly difficult. Traditional methods often rely on restrictive assumptions and simplified models, potentially missing the nuances of real-world insurance markets.

Now, a new wave of research is exploring the potential of deep learning to overcome these limitations. By leveraging the power of neural networks and other advanced machine-learning techniques, researchers are developing more flexible and accurate models to estimate risk and coverage correlations. This could revolutionize how insurance companies assess risk, personalize policies, and ensure a more equitable marketplace for everyone.

What is Asymmetric Information and Why Does It Matter?

AI balancing risk and coverage in insurance.

Asymmetric information, at its core, means that one party in a transaction has more relevant knowledge than the other. In insurance, this typically manifests as the buyer knowing more about their likelihood of filing a claim than the insurer. For example, someone with a family history of heart disease might be more inclined to purchase comprehensive health insurance, a fact not immediately obvious to the insurance provider.

This information imbalance creates several problems:

Adverse Selection: Insurers may attract a disproportionate number of high-risk customers, forcing them to raise premiums for everyone.
Moral Hazard: Once insured, individuals might take fewer precautions to avoid losses, knowing that their insurance will cover the costs.
Inefficient Pricing: Without accurate information about risk, insurers struggle to set premiums that fairly reflect the actual likelihood of a claim.

The result is an insurance market that's less efficient, potentially less stable, and, in some cases, unfairly priced for both high- and low-risk individuals. Correcting this asymmetry is crucial for creating a healthier, more sustainable insurance ecosystem.

The Future of Insurance: AI-Powered Fairness?

While the study confirms previous findings that the correlation between coverage and risk is small, it also highlights the potential of AI and machine learning to refine risk assessment in insurance. As AI algorithms become more sophisticated and data becomes more readily available, we can anticipate further innovations in this space. The ultimate goal is to create insurance markets that are not only profitable for providers but also fair, transparent, and accessible for consumers, regardless of their risk profile. This could lead to more personalized insurance products, better risk management, and a more resilient financial safety net for everyone.

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2404.18207,

Title: Testing For Asymmetric Information In Insurance With Deep Learning

Subject: econ.em

Authors: Serguei Maliar, Bernard Salanie

Published: 28-04-2024

Everything You Need To Know

What is Asymmetric Information in insurance, and how does it impact the insurance market?

In the realm of insurance, "Asymmetric Information" describes a scenario where the insurance buyer possesses more knowledge about their risk level than the insurer. This informational imbalance leads to several adverse effects. First, it can cause "Adverse Selection," where primarily high-risk individuals purchase insurance, prompting insurers to increase premiums for all. Second, it can foster "Moral Hazard," encouraging insured individuals to take fewer precautions. Finally, it leads to "Inefficient Pricing," as insurers struggle to accurately assess risk and set fair premiums. These factors collectively undermine the insurance market's efficiency and stability, potentially resulting in unfair pricing for both high- and low-risk individuals.

What is the 'positive correlation property' (PCP) in insurance, and what challenges are associated with validating it?

The 'positive correlation property' (PCP) suggests a correlation between individuals who choose more comprehensive insurance coverage and those who pose a higher risk. In simpler terms, people opting for extensive coverage tend to be higher risk. However, empirically validating the PCP has proven challenging. Traditional methods frequently rely on simplifying assumptions and restrictive models. These simplified approaches risk overlooking the intricacies of real-world insurance markets. This makes accurately assessing the relationship between coverage choices and actual risk levels more difficult, thereby complicating the development of fair and effective insurance products.

How can deep learning address the challenges posed by Asymmetric Information in insurance?

Deep learning offers potential solutions by leveraging neural networks and advanced machine-learning techniques. These methods enable researchers to develop more flexible and accurate models. These models can better estimate risk and the correlations between coverage and risk. By analyzing vast datasets, deep learning algorithms can potentially identify subtle patterns and risk factors that traditional methods might miss. This could lead to more personalized policies and better risk assessment. The ultimate aim is to create a fairer and more efficient insurance market for all stakeholders.

What are the potential benefits of using AI in insurance for consumers and providers?

The use of AI in insurance promises significant advantages for both consumers and providers. For consumers, AI could lead to more personalized insurance products tailored to their specific risk profiles. This can potentially result in fairer premiums. For providers, AI can improve risk management through more precise assessment. This enhanced accuracy can help insurers better price policies, minimize losses, and ensure long-term profitability. Overall, the implementation of AI aims for a more transparent and accessible insurance system, improving financial security for everyone.

How does the study mentioned in the text contribute to understanding the correlation between risk and coverage, and what are the implications?

The study confirms the previously observed small correlation between coverage and risk, yet underscores the substantial potential of AI and machine learning to refine risk assessment. While the correlation between coverage and risk might be small, the application of advanced AI techniques offers the potential to improve risk assessment in insurance. This has implications for creating more personalized products, leading to better risk management and promoting a more resilient financial safety net. The advancements in this field strive towards creating an insurance market that is not only profitable but also fair, transparent, and accessible for all consumers, irrespective of their risk profile.