AI in Underwriting: Efficient & Data-driven Insurance Operations

AI in Underwriting: Efficient & Data-driven Insurance Operations

Insurance is the art of pricing risk. AI increases both efficiency and accuracy of the risk pricing process, creating competitive advantages for insurers that use the technology.

From deep learning to RPA and chatbots, applications of artificial intelligence enable insurance companies to conduct processes faster and more profitably. With the wide range of time-consuming manual tasks in insurance and an increase in available data, the level of interest in AI is ramping up:

  • 56% of insurance executives believe that AI would improve operational efficiency
  • 47% state that their investment in AI would accelerate over the next year

Underwriting is an essential part of the insurance through which insurers assess risk and determine premiums to accept it. Evaluating and pricing risk requires extensive research on the risk profile of the customer. Consequently, manual underwriting is time-consuming, prone to errors, and can lead to inefficient pricing. This is why AI is well suited for underwriting and risk pricing processes.

How does traditional insurance underwriting work?

Processing underwriting submissions involves:

  • Collecting large volumes of data from multiple sources
  • Extracting useful information from unstructured data from different types of documents such as PDFs, emails, images, handwritten texts, etc.
  • Organizing and analyzing the data to make a final decision

Traditional underwriting relies on

  • historical data to develop rules for risk assessment.
  • manual labor to complete the steps above including most data collection.

Historical datasets can go out of date, become irrelevant and lead to inaccurate predictions. However, collecting new data continuously with manual effort is costly. Therefore, underwriters face a tradeoff:

  • They either need to accept the risk of incomplete data to process submissions faster and cheaper,
  • Or, they need to accept higher costs and take the risk of losing customers for prolonged processing times in favor of more complete data.

How can AI improve insurance underwriting?

Efficient submission/application processing

AI can significantly improve submission processing and free up valuable time for more productive tasks.

For instance, several steps in the underwriting process can be automated. Underwriters can automate data collection, data extraction, filling forms, or other repetitive and tedious tasks. For example, an important application of AI in underwriting is extracting information from unstructured data through optical character recognition (OCR) and natural language processing (NLP). These technologies can eliminate the necessity of manually reviewing each document coming from traditional or non-traditional sources and help underwriters capture and classify useful information.

Better risk assessment

Using machine learning models and other analytical techniques, underwriters can deepen their understanding of the risk associated with a client’s profile. By using data from internal and external sources such as third parties, claims histories, location, or historical data of business occupancy for property risk assessment, these models can learn from the past and predict the risk profile of new submissions. Underwriters can save a significant amount of time allocated to data analysis and can make more informed decisions since these models are better than humans at recognizing patterns in large data.


[VRS]™ Virtual Risk Space by Virtual i Technologies is an intelligent platform for insurance underwriting and risk assessment. The platform provides risk engineering services to collect data in real-time and leverages artificial intelligence to increase risk visibility.

For example, an insurer needed to conduct a risk inspection in a client’s construction project in a hard to reach location. Virtual i deployed a local risk engineer and a remote senior risk expert for the inspection. The inspection took 6 hours to complete resulting in dynamic risk assessment questionnaires, photos, videos, and other data. Then, all the data from the inspection is converted into a final report with a scientific risk scoring that is estimated with analytical models.

Profitable pricing

Having a better understanding of risk enables underwriters to decide on profitable and fair pricing that fits the risk profiles. By using machine learning models, insurance companies can price risk more competitively. Moreover, AI-driven systems can enable real-time data-based dynamic pricing. These can increase profitability through improvement in workflows, pricing strategy, reduce time-to-market and ensure customer satisfaction and retention.

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