What is algorithmic underwriting?

Insurance is transforming through the use of algorithmic underwriting, which automates risk assessment and pricing through use of algorithms, AI and other exciting technologies. It enhances efficiency and accuracy without replacing brokers or insurers, instead complementing their expertise, allowing them to scale and improving EBITDA and combined ratios.

Whilst algorithmic underwriting has existed for several years, it is at a pivotal point in modernising the insurance industry and much has changed since we last wrote about its benefits for insurers and brokers.

The role of AI in modernising data handling

AI revolutionises the way unstructured insurance data is processed, automating tedious manual tasks and allowing brokers and underwriters to focus on strategic activities and client engagement. This shift boosts productivity and service quality, positioning brokers as strategic advisors and giving underwriters the power to write business much faster and more accurately.

Traditionally, brokers have been burdened with the task of sifting through large volumes of paperwork and manually inputting data into several different systems. This manual rekeying process is not only time-consuming but also prone to errors.

Algorithmic underwriting changes this by automating the extraction, analysis, and categorisation of data, and plugging into various sources such as ACORD APIs, meaning insurers and brokers can quickly access accurate and organised information and ultimately enabling them to make informed decisions faster.

Using broker tools such as Artificial's Contract Builder enhances the algorithmic underwriting process further. Data is entered without friction at the very start of the process and can be sent to the underwriter where risk appetite algorithms are applied. Finally, these policies can then be bound electronically through Placing Platform Limited.

Keeping human underwriters in the loop

With a human-in-the-loop approach, algorithmic underwriting can also improve data accuracy and consistency. By standardising data inputs and applying advanced algorithms, the use of AI reduces the risk of human error and ensures that the data used for underwriting is reliable, accurate and comprehensive. This leads to more accurate risk assessments and pricing models, benefiting both brokers and insureds.

In addition, these algorithms can handle vast amounts of data at speeds that are impossible for humans. This capability is particularly valuable in the modern insurance landscape where the volume and complexity of data sets are continually increasing.

Algorithmic underwriting in 2024: what’s new?

In 2024, the use of AI in algorithmic underwriting has introduced faster and more sophisticated tools than the industry could ever have imagined. These tools integrate multiple data sources in real time, improving risk assessment accuracy and process efficiency. This benefits brokers and their clients by ensuring quicker service delivery and more reliable insurance products.

One of the notable advancements is the use of natural language processing (NLP) to analyse textual data. This enables the underwriting algorithms to understand and interpret complex documents which were previously challenging to process. The incorporation of NLP allows for a more thorough analysis, ensuring that all relevant information is considered in the underwriting decision.

Additionally, AI tools now have enhanced machine learning models that can predict future risks with greater accuracy. These models learn from historical data and continuously improve their predictions as they process more information. This results in more reliable risk assessments, which benefit both brokers and clients by providing tailored insurance products that better match individual needs.

Another significant development is the increased use of real-time data feeds. These feeds provide up-to-date information on various risk factors, such as weather conditions, economic indicators, and health trends. By incorporating this real-time data into their models, AI tools can offer more accurate and timely risk assessments. This capability is particularly valuable in industries where risk factors can change rapidly, such as property insurance and health insurance.

Then vs now: evolution of underwriting tools

The contrast between past and present underwriting tools is striking. Previously underwriting was labour-intensive, relying on manual data entry and subjective judgement, but algorithmic underwriting solutions now automate much of this process to reduce time and minimise human errors.

In the past, brokers spent hours gathering and analysing data manually. AI automates these tasks, handling larger datasets efficiently. This transformation enables brokers to focus on complex and value-added activities. For a look into how industry trends have evolved, review major trends from previous years here.

Additionally, AI algorithms provide more objective assessments by removing the biases that can influence human judgement. This leads to fairer and more consistent underwriting decisions.

AI's ability to learn and adapt over time is another significant advantage to insurers. Machine learning models continuously improve as they process more data, leading to progressively better risk assessments and pricing models. This adaptive capability ensures that AI-driven underwriting tools remain relevant and effective in a constantly changing market.

Embracing the shift to algorithmic underwriting

Algorithmic underwriting represents a significant step forward in the insurance industry. By leveraging AI, insurers and brokers can improve efficiency, accuracy, and client satisfaction, positioning themselves for long-term success.

To speak to Artificial about implementing algorithmic underwriting in your insurance business, get in touch.