5 steps to prioritize straight through processing

Nearly two-thirds of commercial lines underwriters claim that their workload has increased or has had no change with technology investments. This is due, namely, to inefficient systems and lack of data integration according to Accenture findings. 

While the outlook may sound bleak, this has created a bright spot of opportunity within the insurance industry that business leaders should take advantage of as they seek out new ways to improve operational efficiency and accelerate growth in the new year. These perceived challenges are forcing functions for real change—for data, analytics, and technology to start making material impacts on automating insight at the point of decision.  

One way insurance organizations are looking to do so is through straight through processing. Straight through processing directly aligns to common organizational goals such as ease of doing business, improving customer experience, and the idea of using advanced analytics to drive innovation and create actionable insights throughout the insurance lifecycle. 

Here are the 5 steps your organization should take to start building the foundation needed to succeed with straight through processing.  

1. Enable underwriters with the right data at the right time 
Access to the right data at the right time speeds accurate decision-making. As more data is created in real time, a pursuit to embed the most recent information is driving competitive advantage. Underwriters are making critical and time-sensitive decisions around the clock—whether quoting new business, serving customers, or setting moratoriums during catastrophe events. Getting the right information to the right decision-makers at the exact right moment matters. 

2. Embed analytics throughout the decision workflow
There is a difference between having data and driving insight from it in a meaningful way. By embedding analytics in every piece of the underwriting workflow, insurers can begin to drive insights from data that improve both efficiency and the quality of decision-making. This includes speed and access to an insurer's own data along with emerging sources of data, enhanced scoring capabilities, and more holistic views of risk – not just individual risks but exposures and accumulations of risk within entire portfolios. 

3. Drive real-time insights
Speed of insight is a differentiating factor and a trend that leading insurers are pursuing. Making data-driven decisions in the moment and taking advantage of third-party data is increasingly important. For example, with climate change and catastrophe risk, the past is becoming less representative of the future. Hazard models and historical data are being replaced with real-time data and automation to better predict and manage catastrophes like hurricanes and wildfires. Likewise, in predictive modeling, a shift to real-time models that incorporate new data immediately into the modeling environment allows for the early identification of trends and keeps models from going stale.

4. Sophisticate underwriting rules and experiment with modeling 
There's no one-size-fits-all process for establishing what gets flagged and what gets automated. The point is to determine the underwriting rules that will best guide your automation. For example, low premium policies (e.g., under $5k), low hazard groups, and no (or few) claims on a policy. Likewise, for agencies sending the policy, look to those that have a track record for low loss ratios. From there, you can continue to evolve your rules and combine them with predictive scores to achieve a higher level of straight through processing. Additionally, fueling predictive models with quality and dynamic data is essential to boosting predictive power while ensuring models don't grow stale.

5. Start prioritizing predictive accuracy over explainability
The market is moving in a direction that recognizes some variables are just correlated—that AI and machine learning aggregate massive amounts of data and find correlations beyond a human's ability to grasp. If you build a model and prove that it works, that becomes a pattern. Insurers who are successfully doing this have figured out how to take the output of models and apply learnings to the business for lower loss ratios and improved underwriting profitability. 

What's next?

P&C insurers are looking for greater speed and efficiency through automation to remove redundancy and improve consistency in all aspects of the insurance lifecycle. Doing so requires insurers to commit to a new course of action where underwriters increasingly utilize and trust automation in order to optimize straight through processing. Not only because that's where technology is headed, but because it's been tested and proven to work for many segments of the P&C business. How does your organization plan on increasing its use of automation to get one step closer to successful straight through processing in the next year?