data · open source
Fraud & Anomaly Detection with PostgreSQL
Fraud & Anomaly Detection built on PostgreSQL, chosen where it genuinely fits, and swapped where it does not.
- Category
- data
- Vendor
- Open source
- Alternatives we also use
- 6
Why PostgreSQL for this
Orqent Labs builds detection systems sized to your investigation capacity, with explanations attached to every alert.
PostgreSQL is strongest at it handles far more workload than teams expect, with one operational model. For fraud & anomaly detection that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: genuine analytical workloads past a certain scale belong in a warehouse. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
We hand over with runbooks, tests and a team that knows how it works, not a dependency.
The honest assessment
- What it is
- Our default database, relational, JSON, full-text and vector in one engine.
- Strongest at
- it handles far more workload than teams expect, with one operational model
- Trade-off
- genuine analytical workloads past a certain scale belong in a warehouse
- Category
- data
We are not a reseller for PostgreSQL and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Hybrid rules-and-model scoring, because rules encode known fraud well
- Real-time decisioning within your latency budget
- Case management for investigators
- Explanations attached to every flagged decision
- False-positive rate tuned against investigation capacity
- Feedback loop from confirmed outcomes
Questions
How do you reduce false positives?
By tuning the threshold against your actual investigation capacity, adding context features, and feeding confirmed outcomes back into the model. The goal is the alert volume your team can genuinely work.
Can it explain its decisions?
Yes, feature-level explanations on every flag, which investigators need for case files and regulators expect to see.
How fast does it score?
Real-time within a payment authorisation window where required; batch where the use case allows it and the cost is lower.
Alternatives for fraud & anomaly detection
Same capability, different stack. Each page states its own trade-off.
Building with PostgreSQL?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878