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. RappiCard, RappiPay, Naranja X, albo, Migrante and KOA already decide with uFlow.
How do you change credit policy without waiting for a product release?
In uFlow the credit policy is designed, tested and published from the engine itself, with a NoCode drag-and-drop editor that the risk team operates. A cutoff change takes hours and is done self-service, without waiting for the next deploy window, and every publication is versioned with immediate rollback if the result is not what you expected. That matters because when the policy lives inside the product codebase, each adjustment competes with the engineering roadmap and the risk team ends up waiting weeks to test a hypothesis that should take hours.
Built-in testing environments let you validate the change against real cases before it reaches production.
- 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.
How do you run machine learning models in production without maintaining infrastructure?
uFlow executes machine learning models inside the decision flow, without your team maintaining its own serving infrastructure. The model sits alongside business rules in the same policy: it receives data that has already been orchestrated, returns its output, and that output is recorded together with the rest of the decision, with the same traceability as a rule. That solves the hard part, which is not training the model but 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 when someone asks about a specific case.
Models run in Python, the language your data science team already works in.
- 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.
How does the decision engine integrate over API, and how fast can you go live?
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 token authentication and separate identities per consuming system. Because it is SaaS, there is no platform to build before you start originating: go-live is typically measured in weeks, depending on the integrations and the internal approvals in each case, and the team focuses on the product and the policy rather than on the orchestration layer.
Connectors to more than 30 data providers already ship with the platform, so there is nothing to build per source.
- 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.
How do you scale volume and add credit products on the same platform?
Each new product is modeled in uFlow as its own flow, with its own policy and its own sources, on the same platform: there is no architecture to rebuild in order to launch a second loan or open a new channel. Volume does not force a rebuild either, because the engine runs on serverless cloud infrastructure with redundancy across multiple availability zones and no capacity to provision in advance. That is what growth that is not linear needs, arriving all at once through a campaign or a new channel. The platform has processed more than 300 million transactions across more than 80 implementations in Latin America.
Assessment can be individual and real-time, or batch, to rescore an existing portfolio.
- 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.
What traceability is left for each decision when the regulator or an audit arrives?
uFlow records every decision with its input, the rules that were applied and the outcome, and exposes all of it in a transaction explorer where each case can be reviewed. The platform is certified under ISO/IEC 27001:2022, with data encryption at rest and in transit, two-factor authentication and attribute-based access control. Having that record from day one avoids the expensive work later: 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 it months afterwards, from scattered logs, costs considerably more than having recorded it.
The record is per decision, not per session: every application evaluated keeps its own evidence.
- 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.
Launching or scaling a credit product
We show you how to iterate policies without slowing down your product roadmap.