How Insurance Organizations Can Leverage P&C Insurance Automation and Data to Accelerate the Delivery of Insights

Published On: March 9th, 20235 min read

In part 1 of this series we talked about the evolving state of complex P&C insurance risk management. In part 2 we look at how, in today’s fast-paced business environment, underwriting organizations are under pressure to deliver customer and risk insights quickly and efficiently. The property and casualty (P&C) insurance industry is no exception. Underwriting (UW) innovation is all about accelerating the delivery of insights.
There are two main ways to do this. Insurance organizations can enable the automation of “pass-through” low-complexity risks to let UWs handle more such risks. Alternately, organizations can expedite the collation, organization, and analysis of the large amount of data available when an UW works on a complex risk. This allows UWs more time to practice their craft and optimally price complex risks.
In this blog post, we will explore how underwriting organizations are leveraging automation and data to speed up the delivery of insights and how they are addressing the challenges that come with these changes. We will also discuss the importance of considering regulatory compliance, change management, and trust when implementing these innovations. By understanding these challenges and opportunities, insurance companies can better position themselves for success in the ever-changing landscape of underwriting.

“Pass-through” Risk Automation

As mentioned earlier, underwriting innovation is all about delivering insights faster. One way to achieve this is by enabling the automation of “pass-through” low-complexity risks. With the advanced data and associated insights available, some low complexity UW can simply be automated – with pre-defined business rules for elements, such as appetite and pricing. This allows UWs to shift gears into more of a “reviewer” of deals, freeing them up to spend more time evaluating edge-case opportunities, rule exceptions, or more complex lines altogether.

To fully take advantage of these automations, there needs to be a significant cultural shift that involves intentional, holistic change management to better manage UW perceptions on their role and the trustworthiness of the automated UW decisions. UWs need to be bought into the value that the automation unlocks, and there needs to be intentional, holistic change management in place. Without this, UWs will still spend time regularly reworking the automated tasks.

In addition to change management, trust is another key challenge to overcome. Even with buy-in on the concept of automation, there will still be some doubt and skepticism about the quality of the automation. To overcome this, it’s important to provide clear data to reviewers on key decision criteria, time/date stamps of data pulls and decisions, drill down data availability, and escalation paths. In addition, taking advantage of expert UWs who will be auditing the tool and letting your system and processes learn from their feedback can help build trust in the automation.

Complex Risk Analysis – Craft Enablement

When it comes to underwriting more complex risks, automating the UW itself is less viable – there is too much human insight needed to help shape decisions. Instead, organizations must focus on expediting the collation, organization, and analysis of the vast amount of data available when an underwriter works on a complex risk. By eliminating tedious tasks and elevating key decision-influencing insights, underwriters can spend more time evaluating complex risks with more attention and better information.

To achieve this goal, organizations can leverage advanced analytics and data management technologies. By automating data collection and organization processes, underwriters can quickly and easily access the information they need to make informed decisions. Additionally, natural language processing and machine learning algorithms can be used to sift through large amounts of unstructured data, such as historical company data, websites, news articles and social media posts, to extract insights that can inform the underwriting process.

Furthermore, making this parsed, raw data more consumable, relevant, and timely can make a huge difference in how underwriters approach their role. Platforms like visualization tools and dashboards can provide an overview of the most important data, making it easier for underwriters to identify patterns and trends. They can also be used to create customized reports and alerts, allowing underwriters to stay informed about key risks and opportunities in real-time.

Data Governance and Trust

These ideas introduce the special need to emphasize data governance. It is critical to ensure that the data being used is accurate, complete, and compliant with industry regulations. Governance also helps in maintaining the trust of underwriters on the data and decision making.

It’s also worth highlighting that sometimes there’s a trade-off between the time it takes to process the data and the accuracy of the data processed. Sometimes, depending on the data set and the complexity of the underwriting task, the time to process the data will be longer, however the resulting insights could provide more accurate information to price the risk.

Ultimately, it is important to note that, like with any other technological solution, UW innovation initiatives are not purely technical plays. Organizations must not only invest in the right technologies, but also in the right people, processes, and training to fully capitalize on the potential of these solutions. Underwriters need to be trained to use these platforms and to interpret the data provided to them, otherwise they may end up ignoring or misusing the outputs, which negates the reason for the initial investments in the first place.

In conclusion, underwriting innovation is all about accelerating the delivery of insights, and by leveraging automation and data, organizations can more efficiently handle low-complexity risks and make sense of the vast amount of data available for complex risks. However, it’s important for organizations to be aware of the challenges that come with these changes, such as trust and regulatory compliance, and to address them holistically. UW leaders in both business and IT must self-assess and ensure they are collaborating on supporting their underwriting scenarios (be they high or low complexity) appropriately based on the type of products they are underwriting and considering all the various factors involved.