Industries · Financial services
Feature infrastructure for production financial decision systems.
Use one feature execution model across credit risk, fraud, and other real-time financial ML workflows while keeping deployment inside infrastructure your organization controls.
Why financial decision systems are hard on feature infrastructure
Financial ML decisions are made in seconds, from structured data assembled out of many systems — and then questioned for years. The same feature logic has to serve a live decision path, produce honest training data, and later explain what a historical decision was based on. Few internal feature stacks were designed for all three jobs at once; most grew one pipeline at a time.
Many systems, one decision
A single decision can read bureau reports, application data, income estimates, transaction history, and account context — each landing on its own schedule from its own system. ClearFeature treats multi-source computation as the normal case: sources are joined by the canonical entity key, and every source carries its availability.
Where it applies
underwriting
Credit risk
live decisions
Fraud detection
validation & audit
Model reproducibility
Self-hosted by design
ClearFeature runs entirely inside infrastructure your organization operates — on PostgreSQL, Valkey, S3-compatible storage, and a Kafka-compatible broker. Financial source data and computed features stay under your existing governance and controls; there is no vendor data service in the path.
One platform, three teams
The operating model matters as much as the software. Data scientists write and test feature UDFs. Data engineering owns sources, ingestion, and materialization. Platform engineering runs the services. Everyone works against one registry and one execution model — instead of passing specifications across three re-implementations.
Evaluate before you engage
The platform is open source under Apache-2.0. Your engineers can inspect the execution model and run the quickstart before any commercial conversation — which tends to make that conversation much more concrete.
Modernizing financial ML infrastructure?
Tell us which decision system hurts most — underwriting, fraud, or something else — and we can discuss what a focused pilot looks like.