Distribution management reform strategies for insurers to consider

What are the largest error carriers when data are manipulated?

Carriers often see the evolution of business products in silos. While the importance of data transformation is clear from a BI and Analytics perspective, many carriers fail to realize the benefits of mature data models and modern data that can transform other areas of the business. Carriers lose when they are unable to carry modern data systems to other areas of their business such as payments. Carriers can carry existing products in their new modes.

We have seen examples of carriers that have not taken the opportunity to improve their data quality, ahead of the changes to Distribution Management (DM). This limits the carrier’s ability to successfully adapt their Distribution Management model, when the time comes. Some carriers have ended up restructuring their pre-existing conditions into their new payment system. They were limited with their data when creating a Distribution System, and they improved their existing issues, because they did not plan to change their DM, while changing their data. Starting with data changes, and making sure the data is available and consistent, before making changes to DM can help prevent data problems that make it difficult to downgrade.

What should carriers do, as part of their business strategy, that will position them for successful distribution management change?

Default layer. Often times, we see carriers fail to account for a line of business or product in their data. In order to calculate the problems in their products or their businesses, carriers adjust the data content according to the line of business. This not only adds information in the data, but also requires the management of data from the payment system.

Fixed time. As carriers evolve today’s platforms move from public to virtual reality. Developing a data platform that can tolerate batch and real-time integration can be critical to ensuring that legacy systems (ie, batch) are not built into current systems.

Maintaining a sustainable business. We have seen carriers implement systems that vary by business or product. These methods are required due to incomplete data. They centralize business information and make the management of business transactions (for example, cancellations and refunds) more difficult. That difficulty comes down to the compensation system, and ultimately makes any future compensation changes or changes difficult.

Do you have any horror stories to share where things went wrong?

We have seen many examples of carriers saving their data and changing distribution. At one shipping company, we noticed a great reluctance to change any of the above plans during their change of plans. This allowed the carrier to pass the challenge from their process management system into their system; the result was “modified” compensation that was more difficult and expensive to maintain, than it would have been, if the carrier had been able to deal with the upstream data problems in the first place.

We’ve also looked at providers who have created data to reflect their existing practices. After their DM change, the carrier was left with a new version that retained all of its predecessors.

What can carriers who have started, or are ready to start, their DM switching journey do now, if they haven’t switched data yet?

The first step is to ensure that the data necessary for the requirements of the new compensation plan is available, in a usable format, from upstream.

The second area to consider is the data required from the downstream systems. Carriers need to know what data is needed for tasks such as reporting and accounting, and where the data is coming from (eg data processing, data processing, compensation).

If the data needed from the upstream system, or the data needed to pass to the downstream system, is not available, this is a sign that the provider may need to stop their DM transition and reconsider their data strategy. Efforts to improve data should be the first step in DM reform, and can help identify and mitigate data issues, before they cause problems downstream.