Comment by grvdrm | Hacker News Reader

Comment by grvdrm | Hacker News Reader

I'm curious about experiences in other similar orgs:

I work as a portfolio manager for a large reinsurance/insurance company but spend significant time in SQL, Python, Excel (not unexpected I'm sure).

The Wapole platform in the article struck a chord with me. We built something roughly similar - call it Trek - that handles jobs. Jobs encompass lots of tasks - reading/writing from Excel, executing SQL, running Python, running C#. I could list many limitations, but realistically, the biggest limitation is that the platform can't handle something that it can't configure and run. In other words, the platform isn't set up with R - so no creates data pipelines/jobs that use R. Lots of people here use R (among other tools)

One key problem (maybe?) is this all action happens inside the business. Trek was built by a talented actuary/programmer. No software engineering org involvement at all. I'm sure lots of folks here can imagine why: lots of red tape, general adversity to software that isn't already here, long stretches of time to get things done. Also, frankly, lots of our software devs write bad code.

For folks familiar with the orgs in this article, and other similar orgs, is what the article discusses happening mostly with software devs in IT functions? Are these folks embedded in the business? And also are there folks using the more technical bits of the systems that are business-oriented - analysts, investment professionals, etc.

Realize the lines are very blurry these days, but interested to learn from everyone here about the types/roles of end-users

I work in a Technical PM role at a large North American Insurance company and used to work at one of the largest Banks as a Sr. Business Analyst (or Sr.Systems Analyst depending where you are).

>lots of our software devs write bad code

Ultimately, you get what you pay for, all our full stack devs are making 6 figures... While it might not be FAANG money they also almost never work overtime and the stress levels are relatively low.

>Are these folks embedded in the business? No, thats my job. As a Tech PM I'm supposed to know exactly the business requirements, what my guys can do (to manage expectations) and any limitations of the software/business. I find the best PM's are the ones that have some Dev experience but also have extensive people skills and understand how to manage stakeholders.

>are there folks using the more technical bits of the systems that are business-oriented? It varies, I started off as that business-oriented person (corporate finance) and eventually made my way over to the Data side of things and finally some programming work and now I'm running projects. While you won't get many analysts/portfolio managers doing dev work I do try and get them to have a hands on approach especially when doing QA and UAT work.

> Ultimately, you get what you pay for, all our full stack devs are making 6 figures... While it might not be FAANG money they also almost never work overtime and the stress levels are relatively low.

Yes, it's the blessing and the curse. They are lovely people. They have lives. They aren't working 24/7. But there is misalignment between senior folks who want to innovate and build internal tech around core IP and the talent level of the folks tasked with actually getting that done. Insurance hardly unique in that regard, but an acute issue nonetheless. Firms like GS, JPM, etc have fatter margins (I think) and can afford to pay devs/strats/etc.

Interesting to hear that you went from corporate finance to technical PM. Quite the journey. Would love to dig more into that if you're willing.

>Firms like GS, JPM, etc have fatter margins (I think) and can afford to pay devs/strats/etc.

From what I've seen on my end the Insurance firms have started paying somewhat of a premium to compensate for the lack of "excitement" that is associated with the insurance industry as a whole.

>Would love to dig more into that if you're willing.

Always willing to chat!

Really interesting comments.

I'd guess that you have proprietary (e.g. valuation / capital calculation) systems that need to interface with Trek in some way. Could you share how you've approached that at all?

Also not clear why R couldn't be added to Trek alongside Python and C#?

Certainly - let me try to share succinct version germane to my day-to-day

- We regularly perform group/segment level risk roll-ups. Involves running computationally expensive (by insurance standards) in-house and third-party models that estimate loss from hurricanes, earthquakes, etc. A lot of our insurance data is unsurprisingly stored in disparate systems that don't talk to each other, and in some cases, don't have any useful interface that someone like me can query/view. Things are changing, but still quite a lot of history to overcome

- We also have lots of stuff from outside underwriting parties in form of Excel, CSV, MDF files.

- We have to bring all that together to make sense of the portfolio, so we use Trek to do a lot of the various involved tasks like running the models, processing CSV data, processing Excel data, attaching databases, creating dashboards in PowerBI (tangent: hate it)

- Sample pipeline: query portfolio data from one DB, read CSV file from another third-party, pull both into risk model, kick off analysis, then execute a script to pull together results in the model's databases or elsewhere.

Happy to expand or answer other q's as you have them.

As for your other comment about R - it's just a matter of the install. Someone has to install R so that Trek can use it. Not a major problem. Pointed this out as a contrast in our org that is probably smaller and has many fewer devs compared to what I'm reading in the post where "bank python" sort of feels like the platform in which everything happens / everything is configured.

Thanks for a really comprehensive reply. Enjoyed your comment on PowerBI.

My background is mainly life which is dominated (at least in Europe) by computationally demanding proprietary liability modelling systems but I think Python / R is getting a foothold in capital calculation / aggregation.

My perception that there is a lot more use of in-house models in the GI / property-casualty worlds so more Python etc but sounds like you still have to interface with proprietary modelling systems.

Absolutely - and for quite a long time (I work mainly property).

There's not much (if any) appetite to completely rebuild 3rd-party geophysical vendor models. Those folks have 20+ years of work behind them and a different talent base (e.g. different types of scientists building the base models).

But we do focus on all the other stuff. Making data input easier/more accurate. Same thing re: output. Also the vast majority of our capital and group-level risk work happens in-house - R, Python, etc.

>read CSV file from another third-party Generally, if you're getting a CSV file from a client that indicates to me that theres a good chance they're exporting info from their systems and sending you the CSV.

Have you considered developing a client facing API that can be used to send and digest data?

We have, but that's not something my team (actuarial) can accomplish without help/blessing/oversight from IT.

I don't know all the details but we do have connections to some of our MGAs through XML dumps or perhaps real-time feeds (I'm doubtful). But that data is often missing some of the details as I need. It's useful for policy admin - not for all the other stuff.

We've also explored portals but those are fraught with concerns about double-entry.

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