The most flexible 100% web, cloud and NoCode decision engine
Automate and streamline your credit assessment process without depending on IT.
What is a credit decision engine?
A credit decision engine is the system that takes an application, queries whatever data sources are needed, applies the risk policy and returns an answer — approved, declined or routed to review — with the limit and conditions where those apply. It replaces criteria scattered across spreadsheets, core banking code and committees with a single executable policy, versioned, applied identically to every application and leaving a record of which version decided each case. uFlow is a NoCode decision engine: the risk team designs and publishes the policy, with no development in between.
The full detail — architecture, governance, models and what to look at before choosing one — is in the definitive decision engine guide.
What makes a decision engine NoCode and 100% cloud?
A NoCode decision engine is one where the risk team designs, tests and publishes its own credit policies without writing code or requesting a development from IT. uFlow does this through a drag-and-drop interface and formulas whose syntax is familiar to anyone who uses spreadsheets: the flow is built visually and intuitively, without losing expressive power. Being 100% cloud also means there are no servers to provision and no versions to install. The platform was built for exactly that purpose, giving risk teams direct control over their policies, and it holds the ISO/IEC 27001:2022 certification.
Why did AWS recognize uFlow as a success story?
Amazon Web Services recognized uFlow as a success story for the effective use of its tools and the quality of the solutions it delivers to its customers. It is not a marketing badge: AWS grants it after reviewing how the platform is actually built, and in this case it points to the serverless architecture on managed cloud services, which is what sustains the engine's scalability and availability. For an institution assessing vendors, it is external technical validation rather than a claim made by the vendor itself.
How do credit bureaus and data providers connect to the engine?
Pre-certified credit bureaus and providers integrate with just a few clicks: uFlow maintains the connectors and the engine receives the data through REST APIs inside the same flow, without your team building an integration per source. You can define the order in which each one is queried and under what condition, so you do not spend an expensive query when an earlier rule already resolved the case. Those sources are the raw material of credit assessment: the more of them enter the flow, the less the decision depends on what the applicant declares.
What does a serverless architecture bring to a decision engine?
A serverless architecture removes the capacity you would otherwise have to provision in advance: the engine scales with volume and does not force you to rebuild anything when a peak arrives through a campaign or a new channel. uFlow was one of the first engines on the market to rebuild its architecture as serverless on managed cloud services, with scalability and availability above those of traditional architectures. For IT that means less infrastructure to maintain; for the business, that a peak in origination does not turn into an outage.
What else does the engine run besides rules: geolocation, models and notifications?
Beyond consuming information and orchestrating sources, the engine runs geospatial calculations, the models your team trained and whatever notifications the process needs, all inside the same policy. GIS calculations compare a customer's position against coordinates or polygons without leaving the decision; models are trained in Python and execute in the same evaluation as the rules; and email and SMS delivery fires from the flow itself. Everything is recorded in the same transaction, so traceability does not fracture across systems.
- GIS calculations: compare a customer's position against coordinates or polygons, inside the policy.
- AI models trained in Python, executed within the same decision.
- Automated email and SMS delivery from the flow itself.
What does the AI agent inside the policy editor do?
The uFlow editor includes an AI agent that works alongside the analyst while the policy is being built: describe the condition in plain language and the agent constructs it, with examples and error prevention. It also audits the order in which the policy executes and proposes how to optimise it, and it reads the commercial documentation the decision depends on, for individuals and companies alike: balance sheets, financial statements and supporting documents. It extracts the data and hands it to the rules inside the same decision.
- Builds the condition from plain-language guidance, with examples and error prevention.
- Audits the order in which the policy executes and proposes how to optimise it.
- Reads commercial documentation for individuals and companies: balance sheets, financial statements and supporting documents.
- Extracts the data from the document and hands it to the rules, inside the same decision.
- Available in the editor's binary and calculation nodes.
How are applicant documents read and turned into data you can decide on?
An agent reads the documents the applicant attaches and turns them into policy variables. Payslips, financial statements, bank statements, invoices, tax returns and identity documents.
It does not stop at extraction. Those variables combine with each other and with the rest of the application data: derived values are calculated and decided on, inside the same policy and the same transaction.
That is the difference between reading a document and being able to decide with it. OCR returns text; here the document enters the flow as data a rule can evaluate, stays in the decision trace and can be queried afterwards.
- Payslips, financial statements and bank statements
- Invoices, tax returns and identity documents
- Document data combines with bureau data and your own sources
- Derived values are calculated and rules decide on them
- All in the same transaction, with no separate process
How is each decision explained in plain language?
A second agent takes the outcome of the transaction and writes it out in plain language: which data was queried, which rules applied and what determined the result.
The text is built on the decision trace, and the trace remains the record of reference. The summary does not replace the technical evidence, it makes it readable for anyone who is not going to walk the detail node by node.
- Support can explain why an application was declined without escalating to risk
- Risk teams review edge cases without opening the full trace
- An auditable explanation in plain language, alongside the technical evidence
The engine in production, with the source behind every figure
Platform metrics of our own, accumulated across our operation in Latin America.
- +300M
transactions processed
Cumulative total for the platform across its operation in Latin America.
Source: uFlow platform metricAs of July 2026
- +30
integrated data providers
Live integrations with bureaus and regional data sources, available from the engine.
Source: uFlow platform metricAs of July 2026
- +80
implementations in Latin America
Cumulative implementations at financial institutions and fintechs across the region.
Source: uFlow platform metricAs of July 2026
- 66
NPS points
Net Promoter Score measured across uFlow's customer base.
Source: uFlow customer satisfaction surveyAs of July 2026
- +25
years of team experience in the financial sector
Track record of the founding and technical teams in lending and risk decisioning.
Source: Track record stated by uFlowAs of July 2026
What can be verified about the infrastructure
- ISO/IEC 27001:2022
information security certification
This is uFlow's only certification. The platform is aligned with PCI DSS and to cloud architecture best-practice frameworks, which are not certifications.
Source: ISO/IEC 27001:2022 certificate No. 2024CRI-308, issued by accredited certifier NYCE, valid through April 2027As of July 2026
- < 5 min
recovery point objective (RPO)
Geographically distributed backups on serverless cloud infrastructure.
Source: uFlow security and infrastructure documentationAs of July 2026
- 2
availability zones in separate regions
Redundancy of the serverless cloud infrastructure the engine runs on.
Source: uFlow security and infrastructure documentationAs of July 2026
The fastest way to make smarter decisions
Visual editor
Edit every credit policy as a flow or decision tree, 100% web, with a drag-and-drop interface. Backtest every change against historical data before publishing.
Reusable templates
Save a set of nodes as a template and reuse it in other flows, private or shared with your whole team, so you never rebuild the same logic or lose consistency.
Policy portability
Download a full policy to a file and upload it back when you need it: your own backups outside the platform and copies across environments, with no dependency on anyone.
REST API
Straightforward integration with any system: core, onboarding or frontend.
Machine learning
Deploy AI models without maintaining your own Python environments, and monitor their performance in production with metrics and drift alerts.
Dashboard
A per-policy dashboard with transaction volume, approval and rejection rates and the top rejection reasons, in real time and without exporting data. Every decision with its reasoning —rules applied and reasons— to tune policies and answer audits.
Cloud / serverless
A 100% serverless platform in the cloud, with the scalability and availability to absorb demand peaks.
Automatic policy versioning
Store every version of your credit policies and reactivate a previous one quickly and under control.
Security
2FA authentication, JWT tokens, encrypted data and credentials protected with strong hashing mechanisms.
Multiple data sources
Pre-certified credit bureaus, your own or third-party web services, and flat files, with caching.
Clear pricing
We work with a transparent, predictable cost model that we define with each client based on their operation: no surprises, no hidden costs.
Companies already deciding with uFlow
What our clients say
“We love working with uFlow as a provider because the answers are immediate, deadlines are met, and they anticipate possible errors. That level of attention is not something you get easily from any provider.”
Roberto Arriaga
Credit Risk Manager · RappiCard México
“uFlow's engine helps us handle multiple credit applications in an optimized way and with a much shorter response time. Changing rules used to take months; now it is handled in hours and without a technical team.”
Felix Diaz
Credit Risk Director · Credijamar
“With uFlow we integrate more easily with different systems for decision-making, we optimized the parameters of all our origination policies, and we implemented machine learning models.”
Sergio Arboleda
Analytics Risk Lead · Juancho te presta
“Implementing this decision engine allowed us to develop new product features more quickly, with constant technical support that sped up the integration.”
Laura Apicella
CRO · RappiPay Colombia
Request a demo of the decision engine
Tell us about your credit operation and we'll show you the engine running on your case.
Everything you need to know
Why is the uFlow decision engine NoCode?+
Because credit policies are designed with a drag-and-drop interface and formulas whose syntax resembles spreadsheets, which lets risk teams create and modify decision flows without writing code or depending on IT.
What does it mean that uFlow is serverless?+
uFlow runs its API on managed cloud services, which removes the need to maintain your own infrastructure and delivers higher scalability and availability.
How does uFlow integrate with my current systems?+
Through its REST API, documented with Swagger and secured with JWT token authentication, uFlow connects quickly and safely with any core system, onboarding flow or frontend.
Can I use machine learning models in uFlow?+
Yes. You can deploy AI models trained in Python directly inside your decision flows, without maintaining your own environments.
Does the engine return the reasons behind each credit decision?+
Yes. Every decision records the input, the rules applied, the outcome and its reasons. That evidence lets you justify a rejection to the customer and reconstruct the decision for a regulator.
Can I test a policy against historical data before publishing it?+
Yes. You can backtest each change against historical data in testing environments before moving it to production, so you change policies without surprises and with instant rollback to a previous version.
How do I monitor my models' performance in production?+
The engine lets you track your policies' and models' performance with real-time reports and metrics, including drift signals, so you can catch when a model loses accuracy and adjust it in time.
Does uFlow have an API to integrate the decision engine?+
Yes. uFlow integrates through a REST API: your system sends the applicant's data and the engine returns the decision with its detail —rules applied, data queried and outcome— ready to use in your core system, your onboarding or your app. The technical reference is in uFlow's documentation.
Can I test two versions of a policy at once (champion/challenger)?+
Yes. With champion/challenger, uFlow runs two strategies in parallel on live traffic and compares which performs better before replacing the current one. It is how you improve a credit policy with evidence and without risking the whole portfolio.
Does uFlow process credit decisions in batch?+
Yes. Beyond real-time decisions via API, uFlow can process large volumes in batch: for example, re-evaluating an entire portfolio or running a new policy over a file of applications.
Can I run Python code inside the decision flow?+
Yes. uFlow includes a Python node for advanced logic, transformations or custom calculations within the same decision flow, without leaving the engine or maintaining separate servers.
Does uFlow train machine learning models?+
No. uFlow runs and governs models, it doesn't train them: your data scientists keep training where they already work (their notebook, Python, whichever cloud they use) and uFlow puts that model to work deciding in production. It's a design choice: it doesn't force you to migrate your data science, and it keeps model development separate from model execution and control, which is what sound model risk management asks for.
Can I use my own scoring models in uFlow?+
Yes. You take your model from notebook to production without rewriting it and without maintaining your own Python environments. Once inside, it is versioned, runs within the policy alongside your rules, is monitored with performance metrics and drift alerts, and every decision it produces is traced and explainable.
What analysis can be done inside the platform, and what cannot?+
Inside: each policy has a dashboard showing transaction volume, approval and rejection rates and the top rejection reasons, without exporting or processing data elsewhere. On top of that: simulating changes against historical data (backtesting), champion/challenger, bulk batch re-scoring, performance monitoring with drift alerts, and the reasoning behind every decision. Outside: custom model training and deep exploratory analytics —ad hoc cuts, cohorts, BI dashboards— which stay in your team’s own tools. uFlow is the decision layer and its monitoring, not a replacement for your data science stack.
Can I see my policy’s approval rate in real time?+
Yes. Each policy has its own dashboard with total transactions, how many were approved and how many rejected, with their rates. The panel reflects the current day’s transactions (excluding the last hour), designed to catch a deviation early after a policy change.
How do I know why credit applications are being rejected?+
The dashboard groups rejections by reason and shows the main ones with their number of occurrences. That tells you whether a single criterion concentrates most rejections —a regulatory validation or a debt rule, for example— so you can decide with data whether the policy is well calibrated or is turning away good customers.
Do I need to export data to analyze my credit decisions?+
Not for operational monitoring: volume, approval and rejection rates and the main reasons are visible inside the platform, with no exporting or outside processing. For deep exploratory analysis or model training you keep using your analytics team’s own tools.
Does the dashboard adapt to how I name my variables?+
Yes. It is configured once per policy: you indicate which variable holds the approval status, which value represents an approved case, and which variable stores the rejection reason. Nothing has to be renamed in your flows — the panel adapts to your naming, not the other way around.
Can uFlow be used to prevent fraud?+
Yes, as an orchestration and decision layer. uFlow doesn't verify biometrics or detect deepfakes by itself —your identity and anti-fraud providers do that— but it coordinates them: queries several in parallel, crosses their signals with your rules, geolocation and models, and decides in milliseconds to approve, reject or refer to manual review, with a trace of every decision.
Can I route suspicious applications to manual review?+
Yes. The decision nodes route each case into three lanes: automatic approval, automatic rejection and manual review. The gray band —neither clearly legitimate nor clearly fraudulent— goes to review with all the evidence ready for the analyst, so your team only looks at the cases that warrant it.
Let's talk about your decision process
We review how you decide today and show you how it would work in the engine, with your own sources and policies.