Artificial Intelligence (AI) and Machine Learning (ML) are already impacting customer expectations, service options, and how professionals operate. Recognizing the potential and understanding the impact of AI and ML is the first step to maximizing your organization’s use of the technologies.
As the industry continues to grow and transform, life insurance companies and professionals can leverage emerging technology to remain relevant and successful in a fast-paced, service-driven marketplace. Demystifying the technology and embracing the huge potential it offers for insurance are essential as we continue to deliver faster, more adaptable, and more efficient insurance solutions.
Machine learning and artificial intelligence are hot topics right now – and for good reason. Machine learning (ML) and artificial intelligence (AI) are unlocking new insights, capabilities, efficiencies, and opportunities across industries and sectors.
Life insurance is no exception. Getting a grasp of what it is and how it can impact life insurance is critical to rethink challenges, spot solutions, and adapt in a changing industry.
Things change constantly. Plan on it. Flow to the work. This is one of our maxims that I think is particularly relevant to the changing landscape we are experiencing in re/insurance. When business needs change and new products and services come on the market, it is our job as reinsurance professionals to know how to react. AKA learn how changes in underwriting, actuarial pricing and more will trickle down to reinsurance administration. For example, how will new products impact treaty set up or how premiums are paid?
So how can you keep up with it all? Education can play a huge role in not only keeping up with changes but adapting to them as well. Which really applies to professionals in any industry that want to maintain a competitive edge. I decided to lay out a guide of reinsurance designations (both essentials and newer ones) that will help you stay on top of your game. Without further ado, here is:
Spatulas, baking pans, cookie scoops and stand mixers are amongst the essential items bakers must have in their kitchen. Similarly, there are a handful of essential tools that reinsurance analysts use in their day-to-day work. Whether you are submitting claims, querying data or trying to manage data errors, I've curated a list of tools for reinsurance analysts to use based off of my experience:
The treaty is a key source of data in reinsurance. It is the formal contract that binds the ceding company with the reinsurer and lays out the terms of the agreement. Therefore it is referenced whenever there are questions about how reinsurance should be administered. However, treaties are often extremely long and contain a lot of standard legal information to sort through. This makes it challenging to zero in on specific information when you need to review treaties to answer key business questions. When do these treaty reviews happen?
Data integrity is essential to reinsurance administration. A key part of our role as analysts is maintaining data accuracy throughout the entire chain of business. Which means we are responsible for data throughout its entire life cycle. Having the right people, processes and technology in place can be extremely beneficial for maintaining data accuracy throughout its life cycle.
One tool our team of analysts uses to ensure accuracy while processing policies is TAI’s exception reports. This tool generates an itemized report of any policy that was not successfully processed as intended. The ultimate goal in reinsurance administration is to get a zero exceptions report. Why?
Now that you've had an introduction to the Frasier method including what to consider when using it and when errors commonly occur, I'm hoping the concept is a little less scary to you.
As I mentioned in my previous blog, reinsurance analysts usually fear the Frasier method because of the potential for errors. But what is the best way to address a fear? Take it head on (at least for some!). When it comes to Frasier, this means getting an understanding of where the most common errors can occur.