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"Fixel Smith" is an AI-generated person, with an article that has very little to do with fraud analysis. 'This' is also a music artist (1), novelist (2), fraud analyst (3), influencer (4), and whatever else you can imagine.
220+ points and 70 comments, and very few notice it's quite a fake post — and no one that it's an AI generated person?
1. https://www.amazon.it/Forged-Soundtrack-Explicit-Fixel-Smith...
Hacker News, we need to talk! "Fixel Smith" is an AI-generated person, with an article that has very little to do with fraud analysis. 'This' is also a music artist (1), novelist (2), fraud analyst (3), influencer (4), and whatever else you can imagine. 220+ points and 70 comments, and very few notice it's quite a fake post — and no one that it's an AI generated person? 1. https://www.amazon.it/Forged-Soundtrack-Expl…
Lagging window functions and/or lateral joins probably would have reduced it to 1/4 the size but definitely increased the cost versus just narrowing the sets into smaller tables first.
Hacker News, we need to talk! "Fixel Smith" is an AI-generated person, with an article that has very little to do with fraud analysis. 'This' is also a music artist (1), novelist (2), fraud analyst (3), influencer (4), and whatever else you can imagine. 220+ points and 70 comments, and very few notice it's quite a fake post — and no one that it's an AI generated person? 1. https://www.amazon.it/Forged-Soundtrack-Expl…
I'd be more surprised to hear that most folks made a habit of investigating the people whose articles we read. To be honest, I usually don't even look at the byline, let alone the rest of the website.
Earlier quoted context omitted.
Worse than that. Coffee usually _is_ a round number in my experience, and I know of people who aim for round numbers when filling their car, and of fuel stations which require a pre-set value, often 10, 20, 50€ etc
Yes, as your parent comment points out, the article centers itself on US transactions, where listed prices seldom include tax and are frequently a cent below a round number. For example, the menu says a dish is $15.00 but the restaurant charges $18.83 after tax and tip. Globally, there's no doubt the US is the exception rather than the norm.
> Fraud detection in transaction data is mostly SQL. Not machine learning, not graph databases, not whatever Gartner is hyping this year. SQL, run against the right tables, with the right joins, looking for the right shapes. It's also not all program-integrity, which is the only work that could justify such blanket statements. Worse is better as long as it addresses the problem domain. Fintech clients are generally i…
For Prevention, you're always going to be constrained by latency requirements, available data and an incomplete picture of user behaviour. You make a quick decision using ML and rules that deals with the majority of cases. But those constraints make it impossible to precisely prevent all fraud.
Detection deals with the downstream consequences of this. A team of analysts will typically analyse the accepted transactions for signs of fraud. This is particularly important for fraud types where you don't get an external signal like a chargeback or customer complaint. Platform integrity is one such example. But Fintechs will also see this building anti-money laundering systems - you need to go looking for the fraud. This is the process the article is describing.
I say they're complementary because the detected transactions become the labels for training and evaluating the next iteration of prevention models.
The main problem with these SQL calculations is that they are deterministic shortcuts for a probabilistic problem. Fraud is not usually a “true because rule X matched.” It is more like "what is the probability this is fraudulent"? SQL patterns are useful, but they are blunt instruments. I really don't think banks use deterministic heuristics but more data science stuff.
Basically it's not just banks and formal financial institutions doing this, and how they do it depends on the company size. Size tends to correlate not only with how many resources you have for a risk team, but also with whether fraud rings are targeting you.
Usually what I've seen is that companies start with some kind of batch SQL/simple logic process that runs daily and tends to flag accounts for manual review and block automatic events like settlement or trading (or whatever the platform does) until that review has been done. Then over time the company will transition to an ML-based approach that still mostly flags things for manual review. The goal of the ML is to improve the precision of the flagging without hurting dollar recall or fraud event recall too much. Depending on the payment system companies may be sensitive to both (for example, in ACH if you get too many returns, even very low dollar payment returns, you're going to get a hard time from your partner bank and you risk not being able to use ACH anymore).
Hacker News, we need to talk! "Fixel Smith" is an AI-generated person, with an article that has very little to do with fraud analysis. 'This' is also a music artist (1), novelist (2), fraud analyst (3), influencer (4), and whatever else you can imagine. 220+ points and 70 comments, and very few notice it's quite a fake post — and no one that it's an AI generated person? 1. https://www.amazon.it/Forged-Soundtrack-Expl…
Kind of fascinating, though it could still be a person doing this using AI as opposed to an entirely generated persona. Thanks for bringing it up.
Earlier quoted context omitted.
Anything that can't be explained and iterated deterministically is too risky for the business of declining financial transactions. Human analysts need to be able to explain to compliance in a single 5 minute email why a specific transaction was declined, and most importantly, what could have been done differently to avoid the adverse decision. Fixing one problem with ML often creates two new problems that aren't quit…
In my experience, Visa support can’t tell me why my legitimate transaction was tagged as fraudulent, other than to say it triggered an AI thing. They also can’t tweak the settings like they used to do, but they can manually allow specific transactions one by one on an ad hoc basis.