Mastercard Transaction Risk Management vs Ojuri
Mastercard Transaction Risk Management
mastercard.comOjuri
ojuri.ioSide by side
| Figure | Mastercard Transaction Risk Management | Ojuri |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| Linux | — | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 2 |
| Spec score · Financial Fraud Detection Software | 6.8 (#10) | 8.4 (#2) |
| Compliance | Mastercard says the solution helps protect the ecosystem and meet global and PCI compliance requirements. | Ojuri says self-hosting can help with data residency requirements including GDPR, NDPR, and CBN, and explicitly says it is not a compliance certification. |
| Deployment | Mastercard describes the solution as quick to market and says no development is required. | The quickstart uses Docker Compose and requires Docker and Node 20; the investigation model is optional and downloads about 7.6 GB of weights when enabled. |
| Purpose | Transaction Risk Management helps acquirers evaluate payment transactions to prevent and detect fraudulent behavior. | Ojuri is a self-hosted fraud detection engine that scores transactions and returns accept, review, or decline decisions. |
Plans
Mastercard Transaction Risk Management
- Transaction Risk Management powered by BrighterionPriced on request
In their own words
Mastercard Transaction Risk Management
Mastercard Transaction Risk Management has no write-up on Specifiction yet; its figures are above.
Ojuri
Ojuri is a self-hosted fraud detection engine for fintechs, payment gateways, and mobile-money platforms. It scores transactions and returns accept, review, or decline decisions, with applications able to call its POST /v1/predict API. The Real-Time Detection Agent scores payments on the authorization path using an XGBoost model served through ONNX Runtime. A Pattern Analysis Agent follows account, device, and payee relationships over time to identify fraud rings and mule networks. Decisions record reason codes and the data considered by the model in an audit log. Sentinel provides a review queue, decision stream, model registry, and analyst overrides that can become training data. Other agents monitor drift, retrain on verified labels, and check evaluations before model promotion. An optional investigation agent creates reports for declined transactions using a self-hosted language model, separate from live scoring. Ojuri says its services and transaction data remain within the operator’s infrastructure. The project is MIT licensed, with no per-call fees or rate limits.