It’s remarkable how little innovation there has been in the world of insurance statutory financial reporting.
For well over the past decade, reporting teams have been resigned to using outdated and backwards technology with questionable performance and reliability to complete high-profile, mission-critical, and complicated financial filings.
This is a market that has long been ripe for disruption, but no one appeared to be interested in stepping up to the challenge. (Spoiler alert: for the tl;dr crowd, Gain has answered this call.)
How did we get here?
Software for the financial statements filed with regulators was codified by the National Association of Insurance Commissioners (NAIC) in the late 90’s. By 2001, a handful of entrenched vendors had successfully (a) secured the required NAIC certification and (b) participated in the initial land rush and secured a foothold with a requisite number of customers to sustain a business.
In such a highly regulated marketplace, carved up across a limited number of suppliers, and with steep barriers to entry, the results over the following years were predictable:
- no new entrants appeared on the horizon, and
- the incumbent vendors, free of pressure to innovate and invest, did the absolute bare minimum to keep customers happy(ish).
Thus, the stage was set for the stage for time and technology to bypass insurance statutory financial reporting. And, for a very long time, this is exactly what happened.
Before we talk about how this played out in the present, let’s take a moment to get an understanding of just how software from this era was designed, built, and (since it is basically frozen in time) what it still looks like:
How I Built This, the 1999 to 2001 version:
The legacy vendors’ products are a function of the best practices at the time. Software engineers gathered the specifications and requirements, conceived the data model, and configured the user interface. This work was generally performed in a bubble, with the engineers driving the decisions as to how users would interact with the product.
Also important: the computing languages, technologies, and architecture of that era imposed really significant limitations on what software could do. And these limitations necessitated design compromises. As software was installed directly on users’ computers, the engineers walked a thin line between functionality and performance. In short, significant concessions, logical for the time, were made.
The combination of engineering-led interface design and technology limitations resulted in software that, quite frankly, has not aged well.
Rather than an interact with a product that was intuitive and instantly understandable, users embarked on a journey to learn the data model and design layout through training sessions and thick user manuals. And the software was slow, and it had limited functionality as compared to the possibilities offered by modern architecture and approaches.
Better late than never….
Gain identified just how backwards the legacy products were by current standards. Importantly, we applied not just state-of-the-art software tools and architecture to the problem, but a modern software design approach as well.
Instead of delivering installed software, we built the first native cloud application for these statements.
Instead of designing our solution in a bubble, we involved customers heavily from the outset – through focus groups, one-on-one interviews, and even embedding ourselves in the reporting process.
Instead of launching the software to great fanfare in a splashy launch event, we rolled out features iteratively, solving different reporting problems sequentially over time, until we had a polished application that gracefully handles some of the most complex filings in the industry.
Our approach is, by no means, revolutionary; this is just how software is built in the current day.
By leveraging current best practices in user-focused design, we created a modern software experience that stands in sharp contrast to our competitors’ legacy products. By harnessing the unlimited computing power of the public cloud, we deliver a different standard of performance around an increasingly complex data model.
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