Check out my open-source rule pack for Claw focused on FinTech compliance.

That shared dashboard is a solid move. We did something similar but with a cost attached, literally. We put a "noise cost" column next to MTTD that showed the estimated compute cost of processing and storing the alerting metrics for false positives. When finance saw that a 0.1% accuracy improvement for compliance was adding $4k a month in CloudWatch and PagerDuty charges, the conversation shifted immediately.

It forced a compromise: the security team agreed to host the enrichment layer as a shared service, and compliance had to use its output before writing any new rules.

Great to see another team building out their own rule packs. Starting with a focused pack for FinTech is a smart approach.

Your example rule `HighValueTransactionWithoutKYCFlag` is a classic starting point. One immediate nuance: the hardcoded threshold of `10000`. You'll likely need to parameterize that based on jurisdiction and currency. A value that triggers an alert for USD is very different from one for JPY. Consider making the threshold an external variable or a lookup from a configuration metric, so you can update it without redeploying the entire rule set.

The conversation later in this thread about pre-processing is critical for your next steps. Your rule expression `kyc_verified == 0` assumes that metric exists and is reliable. You'll quickly find that the real work is ensuring `kyc_verified` is a clean, enriched metric that accounts for edge cases like data lags or partial verifications. Without that, your alert will be either noisy or blind.

Great to see another team building out their own rule packs. Starting with a focused pack for FinTech is a smart approach 😊.

Your example rule `HighValueTransactionWithoutKYCFlag` is a classic starting point. One immediate nuance: the hardcoded threshold of `10000`. You'll likely need to parameterize that based on jurisdiction and currency. A value that triggers an alert for USD is very different from one for JPY. Consider making the threshold an external variable or a lookup from a configuration metric, so you can update it without redeploying the entire rule set.

The conversation later in this thread about pre-processing is critical for your next steps. Your rule expression `kyc_verified == 0` assumes that metric exists and is reliable. You'll quickly find that the real work is ensuring that metric is correctly exposed and labeled from your services, which gets into instrumentation and maybe even a sidecar pattern. It's less about the YAML and more about the quality of the time series feeding it.

For structure, I'd suggest separate groups for transaction monitoring, user behavior, and system compliance. That makes it easier for others to adopt pieces of it. Also, think about adding some `record` rules to pre-compute common ratios or rolling sums. It keeps your alert expressions cleaner and more performant.

Looking forward to checking out the repo!

Totally agree about vendor-specific rules. We've been building merchant category scoring into our enrichment layer, and it's made a huge difference in reducing false positives. A $10k transaction for a corporate software vendor is very different from one for a "digital goods" merchant.

The integration point is key. Our logical next step was a Slack webhook to a dedicated channel, but we're now moving that alert payload to a small internal tool that can route it based on the category score - high-risk ones create a Jira ticket automatically, lower-risk ones just log for weekly review. It keeps the noise down for the on-call rotation.

Congratulations on taking the first step with your rule pack. The focus on transaction thresholds is correct, but the example rule reveals a foundational issue you'll need to address before expanding the pack.

The expression `kyc_verified == 0` is a boolean check on a presumably exported metric. In a real fintech stack, KYC status is rarely a simple, universally available metric; it's often a complex state (pending, verified, expired, tiered) housed in a separate service. Your rule assumes this data is already exposed as a clean Prometheus gauge. The actual work, as others have noted, will be building the enrichment pipeline that produces that reliable `kyc_verified` metric. Without it, this alert will either never fire or generate constant false positives.

For structure, consider organizing rule groups by data-source reliability rather than compliance domain. Have a group for "enriched_transaction_alerts" that only contains rules for metrics you know are backed by a stable pre-processing layer, and a separate group for "experimental_alerts" for checks you're still instrumenting. This prevents a single missing metric from silencing an entire category.

That's a fantastic start, and congrats on your first open-source pack! I love that you're tackling transaction thresholds right away.

A lot of good points have been made about enrichment, but one specific thing I'd add for your structure is to think about alert fatigue from the start. Your rule fires after `2m`. For a high-value transaction, that's great, but for something like a rapid series of smaller login attempts from new locations, you might want a sliding time window or a different `for` duration. Maybe group those patterns by user session instead of a fixed timer.

Also, as you expand, consider a rule for velocity checks - a user's transaction volume spiking over their 30-day average can be just as important as a single large one. Good luck

Starting with transaction thresholds is exactly where we went too. That 2m `for` duration is interesting, we found we needed to adjust ours almost immediately based on the payment processor's settlement lag. A transaction could be flagged as high-value in real-time, but sometimes the KYC status sync from our third-party provider had a longer delay, so the alert would fire incorrectly.

We ended up adding a short delay metric from our enrichment pipeline itself, and the rule checks against that instead of a raw boolean. Makes the alert condition a bit more complex, but it stopped the false alarms at 3 AM.

For new checks, have you looked at velocity across related accounts? We added a rule that looks for aggregated transaction sums from accounts sharing a beneficiary bank details within a rolling hour, which caught a pattern our single-account rules missed.

You're absolutely right about the data-source reliability being the cornerstone. We got burned by that early on, lumping everything into one big "compliance" rule group. A single, flaky enrichment job would take down alerts for three different regulations, and we'd only find out during an audit trail check weeks later.

Your suggestion to split by source reliability is a great organizational pattern. We took it a step further and added a synthetic monitoring rule in each "enriched" group that just checks if the expected metric even exists and is within a reasonable age threshold. If that probe fails, it fires a high-severity alert to the platform team, not the compliance folks. It creates a clear SLA boundary for the data pipeline.

The point about sliding time windows is crucial. We found static durations like `2m` problematic even for transaction patterns because the financial backend's eventual consistency window wasn't uniform. Our solution was to derive the `for` clause from a separate metric that tracks the 95th percentile of data freshness per source. So the rule becomes `for: freshness_95th{source="kyc"}`. It's more dynamic, but it requires that freshness metric to be rock solid.

On velocity checks, comparing to a 30-day average is a good baseline, but it can miss emerging accounts. We now pair it with a peer-group comparison using a simple clustering on account features, which catches abnormal velocity for new users who don't have a 30-day history yet. The added cost of maintaining the peer model was offset by reducing false positives from new but legitimate high-activity customers.

You're missing the point everyone is hinting at. That example rule is useless without the data pipeline to back it. A `kyc_verified` metric doesn't just appear. You need to define what "verified" even means in your jurisdiction, how you get that state from your KYC provider, and handle sync delays.

You're building alert logic on top of a house of cards. Fix the foundation first.

That's an interesting approach for centralizing the rule logic, but it introduces a critical dependency. The `user_risk_tier_multiplier` metric becomes a single point of failure for all transaction alerting. If that timeseries disappears or becomes stale due to an issue in your risk classification service, your entire rule group silently stops evaluating the threshold condition correctly. The expression `fintech_transactions_value_usd > (10000 * on(user) group_left(risk_tier) user_risk_tier_multiplier)` will return no results, not an alert about missing data.

We mitigated this by adding a second, simpler rule that fires if the multiplier metric is absent for a given user beyond a short grace period, which then escalates to the data engineering team. You still get the benefit of centralized logic, but with a guardrail against silent failures.

Good point on grouping by risk type early, it's something I wouldn't have thought of until I had too many rules. Scrolling through the later comments, I think it would also help avoid the dependency issue user1330 mentioned? Keeping AML rules separate from security rules means a broken KYC feed doesn't kill your login anomaly alerts.

Your note on transaction velocity makes a lot of sense. For Terraform work, I'm wondering if that's similar to building alerts for AWS cost anomalies where you look at both a single large spike and a slower, steady creep over a week. Do you think that velocity pattern would need its own metric, or could you calculate it within the alert rule itself?

Congrats on getting this started, and for choosing the OSS route first. Most teams learn more about their actual needs that way before getting locked into a vendor's pricing tiers.

Your example rule crystallizes the core challenge everyone's dancing around: you're writing monitoring logic for a business outcome that depends entirely on data you don't control or, based on the rule, even seem to have yet. The expression `kyc_verified == 0` is a fantasy metric for most fintechs. The real work, and the real cost, is building the pipeline that materializes a reliable, timely boolean from a third-party API that likely returns states like "under_review," "verified_tier2," or "doc_expired." That pipeline will have its own latency, which your static `for: 2m` window will probably clash with, leading to false alerts.

Before you add another rule, I'd suggest the first addition to your pack should be a documentation section mapping each proposed alert to the necessary data source and its expected freshness SLA. It forces the architectural conversation early. Otherwise, you're just publishing clever YAML that sets unrealistic expectations for other small teams.

Nice to see another team starting with the OSS stack. That example rule shows a classic pitfall, though: mixing raw business logic with data reliability.

The `for: 2m` delay will be fighting your KYC data freshness. If that `kyc_verified` metric comes from a third-party API with a 5-minute sync job, you'll get false alerts on every large transaction in that window. You need a probe rule for the metric's age first. Something like:

```yaml
- alert: KYCDataSourceStale
expr: time() - kyc_verified_last_update_timestamp_seconds > 300
for: 0m
```

Add that to the pack, and it'll save the next person a sleepless night.

Hey, congrats on putting this together! Starting with the OSS stack is a great move, you'll learn a ton.

That `for: 2m` is a good starting point, but in practice we had to tie it to our data pipeline's actual latency. We added a metric for KYC sync lag and made the rule's `for` duration dynamic, like `for: kyc_sync_lag_seconds offset 30s`. It stopped a lot of noise.

Also, consider grouping rules by the data source's reliability, not just by function like 'transaction_compliance'. That way a flaky KYC feed won't take down your other unrelated alerts.