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0 pointsjanpeuker1d ago0 comments

I agree the post looks a little AI written but generally this kind of analysis is quite common. Leaving aside human heuristics that are generally too well known to catch real scammers (like time travel or "7 days", which is bad because often weekly patterns are important so at the very least look at 10 days) and actually have low precision, what I find odd it that all results just return a user ID.

So this is really just surfacing cases, but with not enough context to be useful to prioritise. I would expect a score to be included.

Apart from that it misses a lot of signals like refunds, declines, disputes etc [1].

1) https://stripe.com/gb/guides/improve-fraud-management-with-r...

0 comments

stripefan1d ago

There's a rule on HN to say upfront if you're affiliated with the link.

If you're affiliated with Stripe as an Integration Engineer, that should come before the marketing link to Stripe website.

janpeukerOP23h ago

Thanks for clarifying the rule - I wasn't aware of that and follow it next time. But I also don't think it's a marketing link. The post explains why specifically in the cases of transaction fraud other signals are important, with specific SQL examples, and that should translate to any other payment provider.

stripefan22h ago

You're welcome!

I actually find it intriguing that you work for Stripe and therefore presumably understand the content of the article you're referring to, but continue to pretend that the SQL examples somehow have value for fraud prevention purposes.

OK, let's take a look at this SQL. I took a random example:

> select

> date_format(date_trunc('week', d.created), '%Y-%m-%d') as week_iso,

> r.rule_id,

> r.predicate,

> count(distinct d.charge_id) as count_total_charges

> from rule_decisions d

> join radar_rules r on r.rule_id = d.rule_id

> where d.created >= date_add('month', -3, current_date)

> and d.action = 'block'

> group by 1,2,3

> order by 1;

The example above is about grouping rule decisions by Radar rules for performance optimisation, and has no value for any other fraud prevention techniques.

Overall, the test is simple: the link is called 'How to continuously improve your fraud management with Radar for Fraud Teams and Stripe Data' and the article itself is in the 'Product resources' category. It is not a general example, and using Stripe is necessary to get any value from it.

All of this makes the article marketing material, and given that you're employed by this company, that must be disclosed.

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