uFlow for fintechs and digital lenders
Iterate your credit policy at the pace of the product and run models in production, without building or maintaining decision infrastructure.
A lending fintech competes on speed: whoever adjusts the policy fastest learns fastest. The bottleneck is almost never the idea, it is the release. uFlow separates credit logic from the product's deploy cycle, so the risk team can test and publish changes self-service while engineering works on the product. RappiPay and RappiCard run on uFlow.
Iterate the policy without going through a product release
When credit policy lives inside the product codebase, every cutoff change competes with the engineering roadmap. The risk team ends up waiting for the next deploy window to test a hypothesis.
In uFlow the policy is designed, tested and published from the engine with a NoCode drag-and-drop editor. The change takes hours and is done self-service, not weeks or months of release cycle, and every publication is versioned with immediate rollback if the result is not what you expected.
- NoCode drag-and-drop editor, operated by the risk team.
- Automatic versioning with immediate rollback to a previous version.
- Built-in testing environments to validate the policy before production.
ML models in production, without maintaining infrastructure
The hard part of a model is not training it, it is putting it to work in production and keeping it there: serving it with low latency, versioning it, monitoring it and being able to explain each inference.
uFlow executes machine learning models inside the decision flow without you maintaining your own serving infrastructure. The model sits alongside business rules in the same policy, and its output is recorded together with the rest of the decision.
- Execution of ML models with no serving infrastructure of your own.
- Models and business rules coexisting inside the same policy.
- Model output recorded within the detail of every decision.
API integration and time-to-market
uFlow is consumed over a REST API from your backend, your app or your onboarding flow: it takes the application, orchestrates calls to credit bureaus and your own sources, runs the policy and returns the decision with its detail. It is a contained integration, with separate identities per consuming system.
Because it is SaaS, there is no platform to build before you start originating: go-live is measured in weeks and the team focuses on the product and the policy, not on the orchestration layer.
- REST API with token authentication and separate identities per consuming system.
- More than 30 data providers already integrated, with no connector to build.
- SaaS: typically live in weeks, depending on integrations and approvals, with no decision infrastructure to build.
Scale volume and add products on the same foundation
Fintech growth is not linear: it arrives through a campaign, a new channel or a second product launch. uFlow runs on serverless cloud infrastructure with redundancy across two availability zones in different regions, so volume does not force you to rebuild the architecture.
Each new product is modeled as its own flow, with its own policy and sources, on the same platform. The platform has processed more than 300 million transactions across more than 80 implementations in Latin America.
- serverless cloud infrastructure, with no capacity to provision.
- Independent flows per product on the same platform.
- Real-time individual assessment or batch processing to rescore the portfolio.
Traceability from day one, not when the regulator arrives
Many lending fintechs end up regulated, acquired or audited by their funders, and that is when the question of how each case was decided appears. Reconstructing that after the fact, from scattered logs, is expensive.
uFlow records every decision with its input, the rules applied and the outcome, with a transaction explorer to review them. It is also certified under ISO/IEC 27001:2022, with data encryption, 2FA and attribute-based access control.
- Complete record per decision, with a transaction explorer.
- ISO/IEC 27001:2022 information security certification.
- Encryption at rest and in transit, 2FA and attribute-based access control.
Everything you need to know
How long does it take my risk team to change a credit policy?+
Hours, and self-service. The policy is edited with a NoCode drag-and-drop editor, tested in a pre-production environment and published without going through the product's deploy cycle. Every publication is versioned, with immediate rollback to the previous version.
Can I run my own machine learning models inside the decision flow?+
Yes. uFlow executes ML models inside the policy, without you maintaining your own serving infrastructure. The model sits alongside business rules in the same flow and its output is recorded together with the full detail of the decision.
Should we build our own decision engine or use an existing one?+
Building it means maintaining not just the engine, but the credit bureau connectors, versioning, testing environments, model serving and traceability. uFlow arrives with more than 30 data providers integrated and goes live within weeks, so engineering stays on the product.
How does uFlow integrate with our current stack?+
Over a REST API from your backend, your app or your onboarding flow, with token authentication and separate identities per consuming system. The engine takes the application, orchestrates the calls to data sources, applies the policy and returns the decision with its detail.
Start growing with uFlow
Transform your credit assessment process with the decision engine.