Case studies
Real stories of credit automation with uFlow.
The uFlow team brings over 25 years of experience in financial services, working alongside established institutions, fintechs, retailers and startups. These are some of the processes the engine already decides. Institutions already deciding with uFlow include Lulo Bank, Banco Serfinanza, HABI, Ualá Bank, Naranja X, KOA, Jamar and Banco de Corrientes.
RappiCard (Mexico)
RappiCard adopted the uFlow decision engine for its flexibility, automating credit assessment on credit card applications and making it far more efficient — without depending on IT for every change.
Read the full case →
Juancho te presta
This fintech issues fast loans of up to five million pesos over 12 to 24 months. The uFlow decision engine let them run their own machine learning models inside the decision, sharpening every credit call.
RappiPay (Colombia)
With more than 800,000 users across LatAm, RappiPay automated and accelerated its credit assessment process with uFlow, getting cardholders approved noticeably faster.
On the infrastructure side, see how uFlow scales on AWS with enterprise-grade security. Read the AWS case →
Verifiable results, with a public source
Figures reported by industry media and associations after implementing uFlow.
- 40 → 8 min
Credit-line activation
Colsubsidio, after automating origination with uFlow: credit-line activation went from 40 to 8 minutes (5× faster).
Source: Colombia Fintech / La República (2025)As of August 2025
- 25% → 12%
Delinquency reduction
Colsubsidio, after implementing uFlow's decision engine.
Source: La República (2025)As of August 2025
- +12%
Increase in loans granted
Colsubsidio, after implementing uFlow in its credit process.
Source: La República (2025)As of August 2025
- $138.000M
Disbursed in loans (COP)
Colsubsidio, cumulative amount publicly reported after automating with uFlow.
Source: Colombia Fintech (2025)As of August 2025
- +300%
Processing capacity
Lulo Bank, after implementing uFlow. It previously ran a model with 300+ variables and a 700+ branch decision tree; with uFlow it nearly tripled its processing capacity.
Source: uFlow — event with TransUnion, Bogotá 2024As of September 2024
- Meses → horas
Credit policy change time
Lulo Bank: policy changes went from taking months to weeks or even hours with uFlow.
Source: uFlow — event with TransUnion, Bogotá 2024As of September 2024
- +12 pts
Approval rates
Lulo Bank raised its approval rate by 12 percentage points after implementing uFlow; it also diversified its credit portfolio.
Source: uFlow — event with TransUnion, Bogotá 2024As of September 2024
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
Companies already deciding with uFlow
Everything you need to know
Which companies use the uFlow decision engine?+
uFlow's customers include RappiCard, RappiPay, Juancho te presta, Hendel, Galilea, ExcelCredit, Migrante, Bercomat, Jamar and Ceibo Créditos, among others.
What results did RappiCard achieve with uFlow?+
RappiCard automated and sped up its credit assessment processes for credit card applications, without depending on its IT team.
How did uFlow help Juancho te presta?+
It allowed them to integrate machine learning models into their decision engine, optimizing the granting of fast loans of up to five million pesos.
How many years of experience does uFlow have?+
uFlow has more than 25 years of experience as a strategic partner to financial companies, fintechs, retailers and startups.
How much can a decision engine reduce delinquency?+
It depends on each operation, but there are public results: Colsubsidio cut its delinquency from 25% to 12% after automating its credit decisions with uFlow. The key is originating better and being able to adjust policies within hours when deterioration signals appear (source: La República and Colombia Fintech, 2025).
How much can processing capacity and approvals improve?+
Lulo Bank increased its processing capacity by nearly 300% and raised approval rates up to 12% after integrating uFlow, running models with 300+ variables without degrading response time (source: uFlow event with TransUnion, 2024).
Does a decision engine improve portfolio quality?+
Yes: originating with more data and better rules identifies good payers more accurately. In Colombia, 2023 fintech credit vintages showed better repayment behavior than traditional ones, a result attributed to the use of modern decision engines (source: TransUnion, 2024).
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