Ojuri review
Deep, explainable fraud decisioning for teams building their own payment infrastructure.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Ojuri is an MIT-licensed, self-hosted fraud detection engine for fintechs, payment gateways, and mobile-money platforms. It scores payment transactions synchronously before authorization and returns accept, review, or decline decisions with reason codes. The product is aimed at engineering teams that want fraud controls inside their own infrastructure rather than a SaaS dependency. It supports web, API, and self-hosted deployments, with Docker Compose available for deployment.
Its strongest area is the depth of its decisioning and model workflow. Ojuri combines PRE-rule and POST-rule evaluation, configurable thresholds, velocity windows, transaction-graph fraud-ring analysis, shared-device detection, and relationship-cluster detection. Decisions are explainable through reason codes and backed by immutable audit records. Drift monitoring and label-driven XGBoost retraining support an ongoing machine-learning workflow, while candidate, shadow, and active promotion gates provide stages for moving models into production. Sentinel adds a review queue, decision stream, model registry, and overrides, giving teams a central operational view.
The trade-off is operational ownership. Teams must run Ojuri within their infrastructure and connect the surrounding components, including Kafka, Redis, PostgreSQL, and ONNX Runtime where needed. The optional self-hosted LLM investigation reports add another infrastructure requirement for teams that want generated decline investigations. Ojuri fits organizations with engineering capacity for deployment, model governance, and fraud operations. Teams seeking a hosted service with vendor-managed infrastructure should choose a different approach; teams building customizable payment fraud infrastructure will find a focused open-source foundation with no per-transaction fees.
Ojuri pros and cons
- Where it wins
- Real-time accept, review, and decline decisions before authorization
- Explainable reason codes with immutable decision audit records
- Graph analysis, retraining gates, and model promotion controls
- Where it doesn't
- Requires teams to deploy and operate the system themselves
- No hosted SaaS deployment or per-transaction service model
- LLM investigation reports require a self-hosted LLM setup
Ojuri fact sheet, pricing and score →
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