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.
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?
Spreadsheets versus automated system. When it comes to reinsurance programmes, this is a popular debate since spreadsheets have traditionally been used to store reinsurance data. However, when considering the disadvantages of spreadsheets, it appears to be a much riskier option. As Denizon pointed out, spreadsheets can cause major issues in any business enterprise and the reinsurance industry is no exception. Below I’ll share various ways spreadsheet disadvantages apply to the reinsurance world and why an automated system is a safer, more efficient and cost effective for your business. Download the full list of reasons why spreadsheets aren't the answer for reinsurance programmes here.
True or False. Companies should be concerned if their employees are on LinkedIn. The answer – false! I recently led a session at the 2017 User Group meeting on the importance of leveraging LinkedIn for building a personal brand and developing professionally. Two things that companies should be encouraging their employees to do. One of the attendees in my session voiced concerns over what their employer or colleagues would think if they were on LinkedIn (‘aka will they think I am job hunting’). While the social media network for professionals can be used for finding new employment opportunity – this is only ONE of the many use cases. As you’ll see below, LinkedIn can be used for so much more – which is why companies should encourage their employees to be active on the network. Whether you are an employee or employer wondering if LinkedIn is the right place to be – I hope to convince you that it is indeed.
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?
You know that feeling you get at the end of a conference? The energetic burst of inspiration and readiness to implement everything you learned into your everyday work? But then you get back to the office and unfortunately, allow other priorities to get in the way of implementing new learnings. While writing this blog and reflecting on my takeaways, I also thought about how we can make time for new learnings instead of putting them on the back burner. So in addition to the User Group highlights I shared in Part I and below, I'll also discuss how to keep the momentum going post conference. For now, let's dive back into highlights from the 2017 User Group.
Can you believe we just wrapped our 28th User Group?! More than 170 re/insurance professionals joined us in Arizona last week to connect, strategize, learn and soak up the sun. This year, we focused on providing attendees with the opportunity to fuel their strengths on an individual, team, organizational, and industry basis. The action-packed week included: Educational breakout sessions A panel of InsurTech experts And an exciting Keynote that led a finger fencing battle! (More on that later). I hope everyone left feeling energized, refreshed and ready to channel their takeaways into their daily routine. If you missed out on the action or want a quick recap from our 28th User Group meeting, check out my highlights below.
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.