The National Insurance Producer Registry (NIPR)'s retirement of its Company Specialized Report (CSR), which many firms relied on for maintaining licensing and producer credentialing data across the United States, means that insurers' manual, spreadsheet-based processes are no longer feasible—starting with budget. CSR reports were a low-cost way for carriers to keep track of and up to date on their agents—$50 bought up to 10,000 license records, as little as half a cent per report. Now they are $1.50 per report, and in a different format that can no longer be used manually.
The CSR provided a single-point-in-time view of licensing, demographic, appointment, and regulatory actions for a selected population of agents. The sudden suspension of this service pushes many carriers' current spreadsheet-driven compliance processes into obsolescence, posing such risks as delayed detection of violations, difficult moves to a different state, and expired licenses. While drawing data from NIPR and the Regulatory Information Retrieval System (RIRS) is still a necessity, manual data reporting isn't an effective use of time for insurers or their downstream insurance distribution partners.
These shifts represent an opportunity for carriers to modernize and automate their compliance processes—here we'll look at what the NIPR changes open up for insurers, from automation and digitization to AI and machine learning.
Where we are
The old CSR provided a view into only a single point in time of a given agent's licensing compliance and readiness. While the simplicity of this format allowed insurers to maintain their manual processes on their agents, the fast-changing and complex regulatory environment has rendered this static "snapshot" version of reporting insufficient to keep up with the real-time, dynamic "video-level" view that's needed. More modern approaches harnessing AI and other new technologies can give insurers a real-time view into NIPR, helping them nail down that moving target and manage their compliance processes more effectively.
There are tools already in the market that streamline the compliance process through automation and digitization. The latest capabilities specifically geared to the new NIPR reporting format include receiving daily updates from NIPR for accurate real-time compliance status; integration tools to easily pull data back into an existing system; automated compliance workflows for appointment submissions, Just-in-Time appointments, and terminations; fully transparent views through the licensing and appointment processes for both captive and independent agents; notifications to encourage candidates and carriers to take action and stay on top of compliance activities; smoothing the processes of state crossovers and market expansions; managing the end-to-end cycle of an agent, from onboarding through their years of experience; and increasing growth by optimizing business reporting capabilities that empower data-driven decision making.
But carriers need to look beyond pure automation. We live in a data driven world, and insurers should be taking advantage of this by bringing data from across disparate systems into one place, giving them far richer insights on their agents and distribution.
Where we're going
As the industry moves from automation toward more nuanced uses of data, technology is moving faster—meaning companies now taking the wakeup call from these NIPR changes to improve their systems and technology are stepping into far more powerful and effective agent management capabilities than existed even a couple of years ago.
The game changer, of course, is machine learning (ML) and AI.
With the CSR no longer available, carriers can actually leapfrog their existing technology to get more from their data and spur growth. Machine learning allows for cross-cutting use cases, for example across compliance, performance, and training. So in making the leap from automation to any new AI-driven tools, insurers should be examining key capabilities such as the ability to more accurately target recruits, onboard, license, appoint, and train agents with streamlined processes, and easily integrate with background check vendors. An AI-driven platform should allow features like social media outreach, candidate tracking, compliance, background check integration, and training.
What these possibilities might actually mean within the company culture of an insurance firm is a workforce newly empowered to make decisions based on real data and more transparent processes, with much of their most time consuming, eye-glazing work already taken over by automation. They'd be able to access integrated management and training modules to learn how to use the new system. Learning to run the system themselves, they would enhance these AI-driven processes by identifying and using desired data sets and of course bringing the human decision-making element.
In summary, instead of taking one step forward by using current tools that simply automate their compliance processes, insurers can take ten steps forward by holistically sourcing a Smart Document Management System (DMS). The business case for using AI and ML in insurance tasks, from NIPR compliance to prospecting to onboarding and training new agents, can be summed up in a few phrases. Accuracy and efficiency. Real-time updates and adjustments. Complete transparency. Seamless integration with existing systems. Self-service functionality. Optimized business reporting capabilities. This is the future for effective producers.
