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0 pointswjnc1y ago0 comments

Who, in your example, put the customers financial data in the SQL database? Because in my part of finance that’s either the customer, or an employee.

Our customers are asking for integration with a lot of their systems (say HR / patrolling), but never ever offer to hook up their accounting system. If we want financial data, we either get a PDF with their audited financial statement or in exceptional cases a custom audited statement (you know, the one where a print of a part of the ledger gets a signature from the CPA for a not insignificant bill).

So I am enthusiastic from a data science point of view. Financial data processing of customer data is / was scarce since limited to what was feasible to manually process. That is nearly in the past.

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PeterStuer1y ago· 2 in thread

I created automated descision suppport systems in asset based finance. For daily needs you get customer financials and other risk data from both official national sources and the likes of Dunn and Bradstreet, Graydon etc. The choice of providers depends on both the customer and deal risk/size. While the "api"'s to these providers might be clunky (putting structured request file on an ftp server and polling for a response), the data is structured (enough) to process.

Deals that are exceptional enough get assigned to a risk officer that deals with it as a case (sidenote: they use a lott of selfmade Excel, VB and low-code tools as they never get IT priority for these cases). There is not enough uniformity as well as a decreased tolerance for inaccuracy in those to warrant extensive automation.

wjncOP1y ago

Thanks. I see the context now. Our asset managers are indeed lucky to have Bloomberg and such, which are easily integratable (and indeed, have been "SQL" for more than a decade now). I'm aware of the third party providers of customer financial information. Lucky to operate in a niche that is not served by them. Graydon (the only one I've been in contact with) is facing a massive disruption though. Their higher tiers are perhaps not enterprise expensive, but Trellis and the likes are probably more integratable and more affordable.

But still, the one building the LLM-integration is 4x as expensive as the one manually entering the data. It's all about TOC, scale and risk perception. I also love that "risk people" (in the banking context, I'd say: model people) think their data quality should be exceptional and then use end user computing MacGyver style models. Spit and popsicle sticks.

PeterStuer1y ago

"think their data quality should be exceptional and then use end user computing MacGyver style models"

The choice they have is submit a formal request to IT, be rejected 95% of the time with the remaing 5% being put in the planning with an eta 2-5 years in the future, or, DIY it with tools at hand. In an ideal world this would not be needed, in reality it is DIY or nothing.

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