A staggering $47 billion loss and an ever-changing risk landscape are the tip of the iceberg weighing down the commercial line insurance industry. Insurers must shed non-value-added tasks and focus on crafting new products to stay ahead. However, talent shortages, manual tasks and data deluge are holding the industry back. The answer lies in intelligent automation, offering streamlined workflows, data-driven insights, and precise risk assessments. Learn how AI empowers insurers to automate repetitive tasks, reduce administrative costs, increase underwriting capacity, and improve ROI.

The commercial line insurance underwriting industry is at a pivotal juncture, facing numerous challenges in an ever-evolving market. The pandemic has resulted in a staggering $47 billion1 loss for the insurance industry, further intensifying the need for prompt action. However, it was just the beginning of a more significant shift in the risk landscape – geopolitical conflicts and more frequent catastrophes have created an urgency for underwriters to be at the top of their game. With these challenges, a pressing question remains: How can insurers manage the cost of risk transfer without imposing premium hikes?

The answer lies in a two-pronged approach: the ability to innovate products and shedding the weight of non-value-added tasks that hinder efficiency.

Instead of getting caught up in administrative work, underwriters must invest time in creating groundbreaking solutions that benefit their customers. However, extensive paperwork, non-standardized data sources, fragmented systems, and manual processes have become bottlenecks, affecting efficiency and agility.

Underwriters are drowning in manual tasks and paperwork

Data overload increases processing costs and hinders underwriters’ access to relevant and timely information. As information pours in from various sources and brokers in non-standardized formats, cleaning and extracting meaningful, relevant insights consumes valuable time and effort. The rekeying and duplicating of data across multiple systems further erodes productivity. Take, for instance, the growing concern about cybersecurity risks. To price it effectively, underwriters must tap into data from cybersecurity centres of hyperscalers. However, interpreting this information isn’t simple, as customers often have a hybrid setup—part on-premises and part on-cloud. Besides presenting data effectively, underwriters require statistical evidence to ensure responsible decision-making. Processing such an extensive amount of data inevitably escalates backend costs and time.

While the data deluge is a concern, process inefficiencies also take their toll. For instance, while dealing with submissions, underwriters tend to follow FIFO2 as they don’t have a system to prioritize applications. As a result, they waste valuable time on low-priority submissions, leading to a loss of business. In addition, an acute talent shortage creates a need to do more with less. Experienced underwriters are retiring, creating a significant knowledge gap. While efforts must be underway to bridge this gap, a more sustainable long-term solution is to figure out a way to achieve more with fewer people.

So, what can commercial insurers do to stay competitive and relevant?

The answer lies in intelligent automation, especially when it comes to underwriting. Relentless automation presents a roadmap to success: streamlined workflows, data-driven insights, precise risk assessments, and accurate, swift decisions.
The promise of automated underwriting

A leading financial services company providing clearing and settlement services leveraged intelligent document processing (IDP) and achieved a 50% increase3 in underwriting capacity. Their application processing time was reduced by 80%, helping double new business conversions. In another instance, automation reduced the cost4 of the claims journey by as much as 30% and delivered an ROI of as much as 200%.

These are just some instances of how intelligent automation can revolutionize commercial insurance. For underwriting alone, there are many use cases, for instance:

  1. IDP can extract and validate data from new submissions and derive relevant insights from it to help underwriters make better risk and pricing decisions
  2. AI can check new submissions based on pre-set rules to prioritize submissions and help underwriters pick the important ones first
  3. Intelligent, connected systems can automatically extract and plug in relevant third-party information to enhance the applications and give a clearer picture to the underwriters
  4. AI and ML models can analyze this data to help triage risks and make pricing recommendations

Consider a situation where a batch of property-related data, including addresses and information about the surrounding areas, is presented to an underwriter. Typically, the underwriter must manually search multiple websites and platforms to gather details about each location, such as crime rates or neighbourhood characteristics. What if there are 100s of addresses? Excessive data would overwhelm the underwriter, making it difficult to determine what is relevant and what to disregard.

AI-based automation, on the other hand, can effortlessly gather information, contextualize it, and present only relevant insights for decision-making. If additional parameters are required, the underwriter can request more information via simple NLP queries.

Intelligent automation eliminates unnecessary human intervention, freeing up valuable time for underwriters to innovate new products and engage in value-added tasks that directly benefit customers.

Refocus on value and improve ROI with a holistic automation approach

While automation technologies can bring efficiency and scale, it is essential to refrain from creating independent workflows for every line of business, as it would undermine the benefits of automation.

The key to success is to take a holistic approach – creating a connected system and striking a balance between customization and standardization. Simply automating disparate processes in siloes will not lead to cost reduction or effective outcomes.

Let’s consider an example. With over 30 categories to assess and variations within each category, implementing separate, independent workflows for each line would be counterproductive. That would undermine the economies of scale that automation can bring. The ultimate focus should remain to elevate the value chain, enabling underwriters to dedicate more time to critical decision-making and excel in underwriting. And so before you embark on an automation journey, it’s essential to assess your existing processes and leverage process discovery to find ideal candidates for optimization and automation.

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The converging paths: Underwriters + AI collaborate for big gains

As underwriting undergoes digital transformation, it’s important to remember the value of an underwriter’s expertise and judgment in risk pricing. Take this case in point. Flood maps often show the whole island as a flood zone, but local underwriters hold vital insights about the actual situation on the ground. They know the precise roads, problem areas, and the preventive measures in place. This unique insight allows them to make informed decisions beyond what data alone could provide. Hence, human underwriters are irreplaceable.

The industry needs a balance where humans and AI work together in tandem. For instance, AI can crunch the data and present the underwriter with three pricing options and the justification for each. Based on their experience, the underwriter can make an educated decision.

As AI and automation take away the cognitive load of the mundane and the repetitive, commercial underwriters would find themselves focusing on new, complex, unusual cases that call upon their expertise. Collaboration between humans and machines is a win-win for all, leading to an agile, efficient, and prosperous future. Are you ready to embark on this journey?

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies