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Balancing profitability and fairness in insurance pricing
Balancing profitability and fairness in insurance pricing is one of the most complex and critical challenges for the industry. On the one hand, insurers must achieve underwriting profits to ensure their long-term viability, especially in a prolonged environment of low interest rates where investment income can’t reliably offset underwriting losses. On the other hand, policyholders seek financial protection at a reasonable cost. Striking this balance is essential for creating a fair and sustainable insurance market.
At Markel, we aim to achieve underwriting profit by providing high-quality insurance products that transfer risk from policyholders’ balance sheets to ours in a mutually beneficial way. While profitability is an inherent goal for insurers, fairness ensures that policyholders see value in the product and perceive the transaction as equitable. Let’s explore how insurers can navigate this delicate balance, focusing on pricing principles, predictive modeling and operational trade-offs.
Setting an appropriate premium
Determining the appropriate premium that satisfies both insurer and policyholder goals is a balancing act. The actuarial profession, through principles like those outlined by the Casualty Actuarial Society, offers foundational guidance:
- Insurance premiums must account for all costs of risk transfer, including expected losses, expenses and the cost of capital.
- Rates must be adequate, not excessive and not unfairly discriminatory.
While these principles might seem vague, they provide a robust framework for establishing “fair” premiums. An adequate premium ensures that the insurer can cover the costs of transferring risk while maintaining profitability. Competitive markets typically ensure that rates are not excessive, as carriers charging disproportionately high premiums risk losing market share.
Balancing act in insurance pricing: Premiums must cover all costs while being fair, adequate, and competitive, ensuring value for both insurers and policyholders.
Fair discrimination
The principle of avoiding unfair discrimination often prompts debate. To the general public, any form of price discrimination may seem unfair. However, in insurance, fair discrimination—pricing policies commensurate with risk—is essential to equalize expected loss ratios and ensure value for all policyholders.
Fair discrimination benefits both insurers and insureds. For policyholders, pricing commensurate with individual risk ensures that premiums reflect the coverage’s value. For insurers, accurate risk-adjusted pricing helps attract a profitable portfolio and reduces adverse selection.
Conversely, limiting the ability to differentiate risk could lead to cross-subsidization, where safer insureds subsidize riskier ones, potentially increasing overall premiums and moral hazard. While this debate is more active in personal lines insurance, the principles hold firm in commercial and E&S lines: a fair premium reflects risk.
For policyholders, pricing commensurate with individual risk ensures that premiums reflect the coverage’s value, while for insurers, accurate risk-adjusted pricing helps attract a profitable portfolio and reduces adverse selection.
Challenges in risk-based pricing
Risk-based pricing drives a virtuous cycle: insurers seeking profitability invest in more accurate pricing algorithms, which in turn yield fairer premiums and incentivize safer behavior. However, this pursuit of precision creates its own challenges.
Expanding Rating Variables
Incorporating additional predictive variables, such as business credit scores or more granular territory profiles, improves pricing accuracy but may increase the burden on insureds and brokers. Lengthy application processes can frustrate stakeholders and deter potential customers.
Sophisticated Algorithms
Advanced predictive models, including machine learning algorithms, enhance accuracy but often lack transparency. “Black-box” models can make it difficult for stakeholders to understand how premiums are determined, potentially eroding trust in the process.
Moreover, overly complex application processes can drive business away, as brokers and insureds may prioritize simplicity and transparency, even at the expense of slightly higher premiums. These challenges partly explain why insurance remains a laggard in adopting AI and machine learning compared to other industries.
Striking the Balance
Given these trade-offs, how can insurers balance the occasionally competing interests of fairness and profitability? Several strategies can help:
Operational efficiencies
Streamlining the application process through automation can reduce the burden on stakeholders while incorporating additional predictive variables. For example, pre-filling application data using third-party integrations minimizes manual input without sacrificing accuracy.
Sparse models
Algorithms that prioritize fewer but highly predictive variables can improve transparency and interpretability. Counterintuitively, these models can sometimes outperform more complex algorithms by avoiding overfitting.
Balancing profitability and fairness is a dynamic challenge requiring thoughtful consideration of trade-offs. By leveraging operational efficiencies, maintaining transparency and adapting to technological advances, insurers can better align their pricing strategies with the needs of all stakeholders. As the industry continues to evolve, this balance will remain central to building trust, ensuring solvency and achieving mutual benefit in the insurer-policyholder relationship.
