Click to learn more about author Chris Koverman.

Identity management has been an obstacle for commercial insurance companies for a very long time. Many thought that problems would dissipate or at least become easier to correct by moving to digital systems, but in reality, identity management has only grown more complex. It is obvious that we need a better way.

Now, there is fresh hope that identity management will become much easier to wrangle. Artificial intelligence (AI) is progressing rapidly, to the point where it could become a tremendous tool in identifying and cleaning up inaccurate data as well as linking the right medical providers to the correct claims.

Let’s take a step back and examine the key issues in identity management today to understand how AI could be used to shore up existing gaps and move the industry forward.

The Data Problem

First of all, by identity management, as it is applied to insurance, I am referring to a special case of entity resolution, i.e., the process of linking references to providers in claims, bills, and other data to a single flesh-and-blood provider – the so-called “single belly button.” This is facilitated by maintaining a dataset of the actual providers working all over the country, with their names, addresses, specialties, and networks – essentially all the data associated with them for billing purposes. These “golden sets” are also available for attorneys in the claims space, functioning nearly the same way. Still, for the sake of clarity, I’ll focus on medical providers in this article. These lists are available through a handful of third-party vendors (and certainly some organizations have developed their own), and they must be constantly updated as the ground truth evolves.

The Missing Link

Currently, numerous different golden sets have varying degrees of accuracy and cleanliness. While this is certainly problematic, the real challenge in identity management is the linkage process itself. This is because many of the provider references in the claims, bills, and other data can be considered stale or dirty.

There are myriad reasons for stale and dirty data. Doctors change their name through marriage or for other reasons, they move to other cities, they might add a specialty or change focus, Joe Smith might become Josephine Smith. All of these things and more make the process of linking these references to the correct provider very difficult. In many cases, today’s systems lack the ability to link references in claims to golden sets; instead, doing so falls to claims representatives. One of the biggest identity management tasks remaining today is the ability to uniquely and accurately link a claim to the right provider with the correct billing information.

Many companies try to build their own linkage, but it has not been smooth sailing. Developing such functionality is an expensive, time-consuming, complex endeavor. Without clean, accurate, linked datasets, however, claims can go wildly off track. But there is hope.

AI Will Fill the Gap

AI has shown its effectiveness in improving claims operations processes, pulling out key insights to resolve claims quickly without attorney involvement. Now AI could be applied to solve the linkage problem as well.

AI systems that aggregate data from actual anonymized claims, bills, and other data throughout the industry could be used to read massive volumes of data, recognize patterns, and find the links between specific providers and claims. Systems could be trained to identify and update records, managing identities persistently and in real time.

Imagine just for a moment if you had a very high threshold of confidence in identifying the correct provider for a claim and that the provider automatically would be issued a unique ID (in the U.S., that of course is the National Provider Identifier or NPI) that stays with him or her throughout the life of the claim so that every time a change is made – a note filed, a bill paid – the correct provider at the correct location automatically comes up. No detective work, no guesswork.

This is now possible from a technological standpoint, as we have seen in creating our company’s solution. I can attest that it requires a significant investment of time, effort, and intellectual property to build in-house. Given the rate of AI advancement, market adoption, and pressing industry need, there is no doubt that it won’t be long before nearly all identity management systems are powered by AI and machine learning technologies.

As I hope I have shown, the data available to the industry today is nowhere near sufficient. The bar for identity management – and therefore the level of investment, skill, and innovation applied to this problem – will continue to increase. Those organizations that prepare to embrace new applications of AI for identity management will be the ones that thrive and modernize claims, driving down costs and increasing efficiency. The companies that resist this transformation will get left behind as they struggle to sift through their dirty, messy data.