Some of what passes for "AI" is really just good data science. Policyholder compression is typically viewed as a data science problem, so actuarial practitioners often revert to their #datascience comfort zone, and apply clustering (or heuristic groupings) based on policyholder characteristics. But as our results at Stoch Analytics make clear, it's possible to to a lot better. Our Compression approach is more #AI-like: Use a training dataset to establish the pattern, and then apply the results to the next problem. When I first used this approach 20 years ago, it compressed a set of a million records down to a thousand and blew me away. Stoch Analytics developed the same approach independently -- and having battle-tested it over the years with a diverse range of products, blocks of business, and use cases, today offers an industrial-grade tool suitable for enterprise use. Industry surveys show that insurers who use other clustering algorithms get compression ratios of 6:1 on average, with "good" ratios of maybe 15:1. With our iReplicate Compression tool, our clients achieve ratios of at least 100:1, and often 200:1 or 400:1. And while clustering methods begin to break down within just a few timesteps into the projection, our training set approach holds up with fidelity well into the projection horizon. This isn't just of academic interest to me a practitioner. As our clients know, this translates to more business insights and -- in an age where fewer compute cycles translates to lower cloud spend -- dramatically lower costs. I'd love to share the benefits. Please feel free to reach out to talk about how to achieve similar results.
Go beyond clustering. Whatever insurance products you’re modeling — and whatever your valuation methodology – our Compression module replicates your liabilities with high fidelity across the projection period. Learn more 👉 https://lnkd.in/eq2TtHBs