5 areas of algorithmic underwriting profit | Insurance Blog

The use of algorithmic underwriting is increasing throughout the insurance industry. With improved decision making and improved risk assessment, algorithmic underwriting can improve performance for insurers and the experience for their customers.

In this post we delve into the evolution and benefits of algorithmic underwriting and share our insights into building and scaling an algorithmic underwriting platform.

Evolution…

Algorithms have always been part of the underwriting process, but they are often limited to measurement. For example, in determining the risk factors for car insurance, algorithms, or mathematical formulas, will be used to set rates based on the car's make, model, driver's age, location and previous history. Whether simple or complex, algorithms have long been our primary measurement tool.

The use of algorithms in some areas of the underwriting process has been limited due to fear of overestimating these factors by averaging, or simply a lack of data and analytical power in other parts of the underwriting process to make these decisions. Instead, the insurance industry relies heavily on complex rule engines for risk acceptance decisions, risk classifications and reporting ordering.

With advances in data access and analytics tools, carriers are now rethinking the use of algorithms, using them alone or alongside traditional rules engines, to improve decision-making throughout the underwriting process.

How does this work…

Algorithmic underwriting uses analytical models to automate decision making in the underwriting process or provide insights to assist underwriters. For the same risk, it can automatically write fully or partially.

Key decisions made using algorithmic underwriting:

• Determining whether the deployment fits the risk appetite of the network administrator
• Identifying key risk factors such as correct SIC/NAIC code
• Prioritizing accounts based on desirability and effectiveness
• Making risk decisions on parts or all risks

In this way, carriers can quickly accept risk acceptance or rejection and reduce the underwriting burden. It also helps provide clients with a more personalized risk assessment, real-time risk management and a seamless experience.

5 benefits of algorithmic underwriting

Algorithmic underwriting benefits the insurance industry in 5 key areas:

1. Process efficiency: By automating the underwriting process, we see algorithmic underwriting reducing processing times up to 50%, streamlining operations, increasing testing speed and simplifying the maintenance of complex decision-making systems. In addition, automated algorithmic underwriting processes can help manage increases in revised applications of up to 25%, allowing insurers to increase premiums without additional operational costs.
2. Accuracy: The accuracy of risk assessment can be improved through the analysis of broader data sets. This analysis helps identify patterns and correlations that may be missed by individual underwriters. With this increased understanding and judgment of the underwriter, errors in risk assessment can be reduced and fraud can be detected more easily. We estimate that fraud losses could be reduced by up to 30% for some insurance companies.
3. Amount: Pricing decisions can be more accurate by improving risk assessment. Algorithmic underwriting helps tailor premiums to individual risk profiles, improving customer satisfaction and competitiveness. Additionally, it supports dynamic pricing, adjusting premiums in real time based on changing risk factors, which we see improving underwriting profits by up to 20%.
4. Effective risk management: Algorithms can help insurers identify emerging risks and adjust their underwriting and risk management strategies. This can help reduce potential losses, reduce loss ratio and improve overall portfolio performance.
5. What the customer does: Algorithmic underwriting allows for quick or near-term decisions about coverage eligibility, pricing and personalized offerings. With predictive and predictive analytics, insurers can make real-time, tailored offers, making insurance more accessible and tailored to each customer's needs. It also makes insurance more accessible to customers or segments that may have been excluded by the underwriting methods of the past.

Building an algorithmic underwriting platform at scale

An algorithmic underwriting platform requires a multi-layered approach that considers future benchmarking. Advanced features needed when considering an algorithmic underwriting platform include machine learning models, real-time risk assessment, and dynamic pricing models.

Challenges to consider as you prepare your data and algorithmic underwriting platform:

• Data quality and availability: Data may be fragmented, incomplete or out of date.
• Model interaction: Sophisticated machine learning algorithms used for coding may not be transparent and interactive making it difficult to interpret results.
• Compliance: As the control of algorithmic and AI models increases, insurers must stay ahead of the curve and adjust models as needed.
• Fairness and impartiality: If not properly addressed, algorithmic underwriting runs the risk of perpetuating negative practices and historical biases.
• Data privacy and security: Algorithmic underwriting involves collecting, processing and storing personal and sensitive data. Protecting customer data is critical to compliance and maintaining customer trust.

Success stories…

We see examples of success with algorithmic underwriting across the industry. In P&C for example, Ki Insurance uses AI and algorithms for faster commercial insurance quotes and automated policy issuance. Hiscox has partnered with Google Cloud to develop and AI model that underwrites certain products. Meanwhile, on the life insurance side, ethos uses machine learning to predict risk and offer simplified insurance claims.

The conclusion

Although algorithmic underwriting is not a new concept in insurance, it is a revolution in improving access to new data sources, improved data quality and better analytical tools. These enhancements allow underwriters to understand from other areas of the value chain and increase their power over old models or exclusion rules.

Despite their expertise, insurers will need to be aware of the potential for bias and lack of transparency in algorithmic underwriting models. Ethics and compliance, including data privacy, consumer protection and fair lending regulations will present challenges for insurers to address from the outset.

As technology continues to evolve and data analytics capabilities increase, we are witnessing how algorithmic underwriting will transform the insurance industry, driving innovation and empowering financial institutions to make informed, data-driven decisions.