ML for Commercial Property Insurers

Machine learning lets teams spend their time on business-generating activities, instead of shuttling spreadsheets back and forth.


For years, the preparation and management of data have exposed themselves as two costly and critical challenges for commercial property insurers. These challenges are hampering production and efficiency and inhibiting growth and profitability. The flow of submissions and the preparation of statement of values are laborious and time-consuming to agents, brokers, insurers and anyone else in between. Without a solution to meet the changing market needs to manage these complex data sets, commercial property insurers' ability to quickly respond to markets and aggressively price business is hindered.

The inability to address these issues has obstructed the process, making it prone to error and hard to scale, especially in today’s market. In turn, this obstruction limits the speed and accuracy of commercial insurers' decision making and debilitates businesses’ potential to grow. The gap between data preparation, screening, prioritization, analysis and pricing steepens, and companies find themselves stagnant and looking for answers. There is yet to be a commercially viable solution focused exclusively on automating the operational preparation and processing component of commercial property insurance data so companies can better meet the growing need of customers and markets and handle the substantial work that is required.

A company’s inability to respond quickly can affect the relationship with the producer, leading to a higher chance of being selected against. These types of companies are more likely to take on more complex characteristics, along with riskier business as the expectation of long processing times is already set.

But we’re starting to implement machine learning into problem-solving tools to address these challenges. These tools enable commercial re-insurers to take their raw data sources and harmonize them with next-gen technology that analyzes, reviews and writes business submissions to provide companies with the competitive edge that’s been sought after for years.

Making the most of your data 

On average, commercial property insurers can only process a portion of the submissions they receive. Typically, managing and preparing results in inconsistencies surrounding labeling, coding and more, which create downstream issues with pricing, modeling and aggregation. Critical amounts of data are lost through the process, and information is not consistently accessible, hindering the ability to make crucial decisions. The only way to solve this and manage business expectations is by hiring additional skilled labor, but this increases the acquisition costs, hurts profits and isolates information among the skilled experts.

Using machine learning, data integration and analysis offer the ability to make data mapping suggestions based on learning algorithms. Manual adjustments are then fed back into the decision-making model, transforming complex, big data into actionable insights that are accessible, in real time, to the entire organization. This allows teams to spend their time on business-generating activities and acting on insights from data, instead of the constant back and forth editing spreadsheets.

See also: How Machine Learning and AI Reduce Risk  

Potential opportunities to grow the business are lost today because of the acquisition costs for new business, but machine learning allows insurers to get from point A to point B by enabling them to screen and prioritize submissions. Today, submissions can be prepared one at a time, but, with machine learning, employees are able to triage multiple submissions at once, including new submissions, enabling the underwriter to focus on the key deals and negotiating terms.

A solution for the enterprise

Giving users the ability to gain access to all commercial property data gives them a wider, more detailed view of the market as well as an understanding of the risk profiles that producers are sending. By providing an automated process to ingest and prepare data, insurers are afforded a more efficient and flexible way of consolidation that essentially helps eliminate errors, cuts costs and promotes growth as companies can now allocate resources to address other areas of the business. Ultimately, automation and machine learning provide insurers with the ability to process submissions at a much higher rate of around 80%.

While giving data access to individuals within the company is beneficial, expanding that access in the form of outsourcing can create a number of different security concerns. Many insurers are operating and sharing data globally, making security and compliance with regulations like GDPR an absolute necessity. Outsourcing is nearly impossible under GDPR due to the heightened risks in sending and having external sources manage large amounts of customer data. Insurers need to show due diligence in not only securing their own data but their customers', as well. In place of outsourcing, we are now seeing data management and storage platforms incorporating heightened security and data integrity into the design, ensuring these tools meet security standards such as ISAE 3402, SSAE 16, AES at rest and SSL/TLS in transit and ISO 27001. Meeting the standards not only helps to prove compliance with regulatory requirements, it also shows customers that insurers are taking their data privacy demands seriously.

Looking ahead

For the commercial property insurer, it is of the utmost importance to have the ability to prepare and manage complex data sets with an easy, quantifiable solution. Emerging solutions across the industry will enable insurers to make fast, appropriate decisions required to address the always-changing market and expand the business.

See also: How Machine Learning Transforms Insurance  

With the introduction of technology such as artificial intelligence and blockchain, combined with machine learning, the realm for new directions provides the insurance industry an unprecedented opportunity to collaborate. While these changes will continue to bring us new and improved methods to get things done faster and more efficiently, one thing is certain, ambitious commercial property insurers are already discovering collaborative initiatives to establish concept cases.

Three Key Takeaways

  • The current processes are putting insurers behind their competitors in the commercial property market because they typically process 20% to 30% of the submissions received.
  • Machine learning is allowing insurers to triage, screen, prioritize and score submissions much faster and with a higher output rate.
  • The result is more completed submissions, which leads to the ability to be first to market.

Scott Quiana

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Scott Quiana

Scott Quiana is the head of products, marketing and partnerships at Quantemplate, a leader in self-service cloud-based automated data solutions for the (re)insurance industry. He is responsible for the vision and innovation of the Quantemplate product.

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