Evaluating the ROI of policy review automation (Part 1)
Automation can speed up processes and free up employees, but what is the true financial return from implementing these systems?
According to McKinsey, 25% of the insurance industry will be automated by 2025 through the implementation of artificial intelligence to overcome bottlenecks and reduce manual processes.
These automation efforts could drive cost savings of $390 billion by 2030, according to a report by Autonomous NEXT. Leveraging intelligent document processing (IDP) and robotic process automation (RPA) is becoming essential for insurance companies in order to keep their competitive edge.
Automation holds the promise to solve the challenge of handling labor-intensive paperwork, which is still at the core of many insurance operations. The quoting process is one of these paperwork-intensive areas.
To prepare new quotes, group insurance underwriting teams are typically tasked with reviewing and extracting key information from prior carrier plans and submissions in addition to comparing locally issued policies against their company-issued counterparts.
During the course of a year, the process can involve thousands of documents and is onerous work. Teams of quote specialists can spend fully one-third of their time simply comparing policies and looking for differences in provisions. This is why more and more insurance companies are turning to IDP-driven automation in order to reduce the time it takes to review and compare these documents while increasing the accuracy of data extraction.
By implementing automation solutions, insurance companies are able to reduce quote generation time from days to hours. But how do these efficiency improvements translate to return on investment (ROI) for an insurance company? Is it possible to calculate the hard ROI of automation? And how do you estimate the value of the soft benefits that automated policy review brings? This white paper is designed to help buyers from the insurance industry evaluate the ROI of automation solutions before making their final decision.
Why automate policy reviews?
The overall purpose of automating the process of extracting and analyzing essential information from insurance policies, benefits booklets and prior carrier plans is to increase the operational efficiency of an insurance company.
A well-chosen solution will have an immediate benefit: It will reduce the time it takes highly skilled, expensive quote specialists to screen plans while delivering more accurate results. With plans being analyzed more quickly and accurately, the organization spends less money on the review process: This is the hard-dollar impact of policy review automation.
But the many side benefits should not be overlooked:
- Policy review automation greatly contributes to shortening the sales cycle, because the sales teams get key information needed to create accurate quotes much quicker.
- New customers are more likely to accept an offer they receive quickly, and customer satisfaction and re-engagement are in direct correlation with the responsiveness of a company.
- Improved quote accuracy generated by automation enables the insurance carrier to price policies more competitively, and consequently close more deals; quote specialists spend more time on strategic tasks rather than sifting through huge piles of documents.
- Upper management gets quicker and better insights about key policy parameters that can heavily impact the business.
Tools to automate policy review can be bought as a stand-alone product (like Cortical.io Contract Intelligence) or as part of a robotic process automation (RPA) system. Having a checklist of the top criteria to look at when selecting a solution is a good idea. Once you have identified a solution that meets your criteria, you should ask about the ROI.
Defining hard and soft ROI
Since the main goals of policy review automation are to improve operational efficiency and shorten the sales cycle, can its impact on an enterprise’s margins be calculated? Is there a way to estimate the money saved with more efficient processes and more accurate quotes? This is an essential question to ask your vendor, all the more as determining an appropriate ROI for the investment will help ensure key stakeholder sign-off.
One needs to distinguish between the hard ROI (how much money will the organization save by automating the policy review process?) and the soft ROI (can it help indirectly improve margins?).
The number of documents that must be processed, their average size, as well as the number and type of extractions and inferences have a big impact on the ROI of automation solutions. But as each organization has different parameters and vendors have different pricing models, there is no rule of thumb to calculate a generic ROI. This has to be calculated case by case.
Some companies have simple requirements, such as having to review a small number of standard policies. Other companies only deal with plans they write (as opposed to reviewing plans from competitors using different structures and terminology). These organizations will get good results with standardized, pre-trained tools that are cheaper than more advanced solutions, on the other hand, the amount of hours they can save is rather small.
Other organizations have to navigate through a wide range of different document types from third parties, with hundreds of pages each and challenging provisions to interpret.
For example, an insurance company requires the system to correctly interpret the sentence: “Insured employees are not required to contribute to the cost of the Long-Term Disability coverage” and classify it as “100% employer-paid.” This requires implementing a system with advanced natural language understanding capacities, which is more expensive than basic keyword-based review software, but since the savings at stake are much greater, the financial benefit of automation could be higher than in the previous example.
Editor’s Note: Part two of this series will answer the question: “Can this impact be quantified?”
Steve Levine is CMO of Cortical.io. He brings extensive marketing and sales experience to his role. Most recently, he led marketing for Civic Connect, a GovTech startup. He has consulted for a number of cybersecurity companies including Flashpoint, RiskSense, Qualys & Panda Security. Previously, Steve was CMO at publicly traded Edgar-Online and financial services startup UB matrix. Steve has held vice president of marketing positions at Oracle, Cassatt, Ketera and Arcot. While at Oracle, he led the company’s first global e-commerce marketing campaign. Steve also brings a sales perspective having held business development and sales roles at Tektronix and ParcPlace Systems. Steve has a B.S. in computer science from Southern Methodist University. He can be reached at s.levine@cortical.io and on LinkedIn.
Opinions expressed here are the author’s own.
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