Solutions · Fraud detection
Keep fraud features consistent from investigation to live decisions.
Define customer, transaction, merchant, device, and account features once and execute the same dependency graph across historical analysis and request-time scoring.
The fraud feature problem
Fraud models iterate faster than almost any other decision system — new patterns, new features, constant retraining. That speed is exactly what makes duplicated implementations dangerous: when investigation notebooks, training pipelines, and the live scoring service each hold their own version of a feature, every iteration widens the gap between what analysts studied and what production computes.
Typical source contexts
Fraud features draw on several structured contexts around the event being scored — conceptually, not as packaged integrations:
source
Transaction data
source
Customer context
source
Merchant data
source
Device signals
source
Account context
One dependency graph for fraud logic
Source features read each report; behavioral and dependent features build on them. The registry defines the graph once, and the platform resolves it identically in every execution path.
Request-time context
Transaction
Customer
Merchant
Device
Feature DAG
Source features
per-context computations
Dependent features
behavioral ratios, cross-context signals
Output
Fraud model vector
consumed by your fraud models
Historical reconstruction
Investigations and model development need history computed the way production would have computed it. Materializing the same DAG over landed source history gives analysts feature values that match live semantics — not a parallel batch approximation that behaves differently around edge cases.
Request-time computation
Online execution is request-triggered: the current transaction context enters the execution path, and the runtime computes the requested feature groups through the same versioned code, resolving dependencies in the same order as historical runs.
Why the operational difference matters
When a fraud model behaves unexpectedly, teams with duplicated feature stacks first have to rule out implementation drift before they can reason about the model. With one implementation, that whole diagnostic branch disappears: what the model saw in training is what it sees now, by construction. Iteration gets faster precisely because there is less to keep consistent.
Fits your existing fraud stack
ClearFeature computes feature vectors; your fraud models, rules engines, and case-management systems stay where they are. The platform slots between your source contexts and the models that consume them.
Fraud features drifting between analysis and production?
Tell us how your fraud feature logic is implemented today — we can map what one shared DAG would change.