June 2, 2022 | Updated: October 11, 2023
Underwriting processes are at the core of insurance companies, and their effectiveness is directly related to insurers’ profitability and success.
Despite this fact, underwriting is often one of the most underserved parts of the insurance industry from a technology perspective. There may be sophisticated policy, customer, and claim administration systems, but underwriters often find themselves wrangling data from a variety of sources, into spreadsheets, in order to adequately evaluate the financial risks that new applicants and scenarios might bring, and translate them into appropriate pricing and coverage decisions.
Due to the complexity and variety of information and sources required to be accessed and integrated, modernized underwriting platforms have often been a difficult objective to achieve for many insurers. The cost and time associated with building such systems, and the possibility of minimal short-term return on investment, have also made it difficult for leaders to secure funding and support within their organizations. These factors have required underwriters to persist manual processes, which, at best, are often highly inefficient. At worst, they do not sufficiently position an insurer to be competitive in the digitally disrupted future of insurance delivery. It does not have to be this way, however.
This blog post highlights ways in which insurance companies can leverage new technology, and incorporate modern architecture paradigms into their information systems, in order to revolutionize their underwriting workflows.
The underwriting revolution
Technology is changing the way organizations operate and measure risk. New technological advancements in the IoT, Manufacturing, and Automotive space, just to mention a few, are driving insurers to develop new underwriting paradigms personalized to each individual, and adjusted based on real-time data.
This is already a reality, with some insurers leveraging personal wearable technology to assess the fitness level of clients and adjust life and health insurance premiums accordingly. We are only at the beginning; let’s explore what this might look like in 2030.
Imagine a scenario, where a professional, living in a major urban area, orders a self-driving car through his digital assistant to get to a meeting. The assistant is directly linked to the user’s insurer, which allows the insurer to automatically calculate the best possible route taking into account the time required, past accident history, and current traffic conditions so that the likelihood of car damage and accidents is minimized. If the user decides to drive him or herself that day or picks a different route, the mobility premium will be set to increase based on real-time variables of the journey. The user’s mobility insurance can be linked to other services, such as a life insurance policy, which can also be subject to increase depending on the commute’s risk factors.
We don’t have to wait for 2030, for a scenario like this to come to fruition. Thanks to advances in IoT devices, mobile computing, and deep learning techniques mimicking the human brain's perception, reasoning, learning, and problem-solving, many of these capabilities can be made a reality here in 2022.
While the insurance industry continues to innovate, the underwriting process is under constant evolution as a result. Certainly, in the scenario described above, the Underwriting decision-making process has shifted from a spreadsheet-based, manual one, to one that is fully automated, with AI/ML decision support. The insurers who can achieve this will retain and gain a significant competitive advantage over the next decade.
Technology can help streamline new cases
Underwriters are notoriously faced with administrative complexity when managing any new case, regardless of the risk profile or level. In the commercial insurance space, agents and brokers are generally used as a bridge between the insurer and the insured. Email exchanges amongst parties are common, which can often lack sufficient detail, and require the underwriter to chase missing data in order to successfully close the sale and acquisition of new business.
Issues with data quality, or lack of certain key pieces of information, can be addressed by implementing automated claim procedures leveraging Natural Language Processing (NLP), Optical Character Recognition (OCR), and rich text analysis to programmatically extract data from email and other forms of written communication, alert the agent in case of missing information, and even attempt to automatically enrich missing information in order to facilitate a close of the sale.
What’s described above is only the beginning of what’s possible to achieve when we begin to think about what we can do to bolster and augment underwriting procedures within an insurer. Sanding off the rough edges by reducing manual procedures, and helping underwriters focus less on non-differentiating work, and more on high-value activities, can not only alleviate significant pain and frustration of the underwriter, but it can help grow the book of business, by offering more competitive pricing, products, and turn-around times.
Triaging times can be drastically reduced
Insurance providers seeking to grow their book of business, and expand the channels through which they sell, may have to deal with a surge of new coverage requests and changing risk scenarios. However, many insurers may be unprepared to handle such increases in new business intake volumes. Because of legacy systems, workflow, and resource bottlenecks, it’s possible that a significant uptick in new business could actually result in a negative outcome for the insurer, due to the inability to process it in a timely and efficient manner. Could you lose business to a competitor because it could not be underwritten in time?
Augmenting traditional workflows with automation and Machine Learning algorithms can begin to address this challenge. How can you do more, without significantly burdening or expanding your underwriting team? Many insurers are beginning to automatically classify and route such increases in business demand, using AI/ML.
A first step in the underwriting process, after initial intake and enrichment, is triaging, or deciding who can best underwrite the given request. Often, this is also a manual process, relying heavily on someone within the organization who knows how to best route the flow of work, based on the skills and experience of the underwriting staff. As with the ability to detect the need for, and enrich the initial submission intake, Machine Learning algorithms can also be leveraged to ease the burden, and reduce the human bottleneck of routing the intake work to the best suited underwriter.
Risk assessment processes can be made more effective
Once the intake of new cases has been automated and triaged, we need to think about how to streamline the risk assessment process. Does every single new business case need to be priced and adjusted by an actual underwriter? If we can triage and determine who should work on the new case, can we also then route some of the low-risk work to a fully-automated pricing and underwriting workflow? Can we begin to save the precious time of our underwriting staff for the higher-touch business and accounts that truly need their attention and expertise?
Automated risk assessment has roots in rule-based expert systems dating back to the 1990s. These systems contained tens of thousands of hard-coded underwriting rules that could assess medical, occupational, and advocational risk. These systems became very complex over the years and still play an essential role in underwriting.
ML algorithms can enhance the performance of these systems by fine-tuning underwriting rules and finding new patterns of risk information. The vast amount of data available to insurers can also be used to predict the risk of new cases and scenarios.
Once the risk profile of a new case has been established, a pricing model can be applied to programmatically derive the policy cost and communicate it to the prospective client without involving the underwriting team, as imagined in the 2030 scenario we mentioned earlier in the article.
Conclusion and follow-up
There are plenty of digital transformation opportunities in the insurance industry. More specifically, focusing on underwriting will help new and existing players in the insurance industry gain a significant competitive advantage in the coming decade.
Whether human-based or AI/ML augmented, underwriting decisions will be underpinned by an ever-growing variety and volume of complex data. In the next blog of the series, Riding the Transformation Wave with MongoDB, we’ll dive deeper into how MongoDB helps insurance innovators create, transform and disrupt the industry by unleashing the power of software and data. Stay tuned!
Learn how MongoDB is helping insurance innovators create, transform, and disrupt the industry by unleashing the power of software and data.