New technologies help curb the spread of gota a gota lending in Latin America

Credit automation has become a key tool for banks, credit unions, and fintechs looking to expand financial inclusion in Latin America. With no-code decision engines, institutions can speed up credit assessment using alternative data, cut response times, and reach segments that have historically been underserved. This automation of financial risk not only improves operational efficiency: it also provides the technology to curb "gota a gota" lending, offering safe, accessible microloans that replace informal credit.

In Colombia and other Latin American countries, millions of people are still outside the formal financial system. According to data from the Superintendencia Financiera, 23 million Colombians have no access to traditional banking, which pushes them toward informal financing alternatives that are dangerous and extremely expensive.

One of them is known as "gota a gota" (drop by drop), an illegal lending practice that, behind the appearance of fast access with no requirements, charges interest rates above 300% a year and, in many cases, exposes borrowers to threats, scams, and extortion.

What is a gota a gota loan and why is it dangerous?

According to Colombia’s Fiscalía General de la Nación, more than 8,000 people have been victims of this type of loan through social networks alone. It is also estimated that 1 in 5 people who take out credit does so through unregulated channels, a worrying trend for the economic stability and financial protection of millions of households and micro-businesses.

Informal credit: a symptom of a structural debt

The growth of "gota a gota" lending is a consequence of informality and the lack of financial education, and it is also the result of having few credit alternatives for people with no recorded credit history. How to curb gota a gota lending in Colombia has become a challenge not only for the state, but also for the private sector and fintechs.

Many of these potential applicants have no banking record or traditional collateral, and the financial system’s current products are not designed for them. Abusive solutions such as "gota a gota" appear to fill that gap, taking advantage of urgency and need.

The role of automation technology in tackling gota a gota lending

In recent years, both the public sector and the fintech ecosystem have pushed strategies to curb this practice. On one side, campaigns such as "Ciérrale la llave al gota a gota" in Bogotá aim to provide fast, safe microloans to entrepreneurs in the formal and informal sectors. On the other, a law was passed allowing regional governments to invest in financial cooperatives, widening the credit supply for traditionally underserved populations.

Still, for these measures to have real, scalable impact, lenders need to adapt quickly to this new scenario, and that is where technology becomes decisive.

### How does financial technology help fight informal credit?

Fintechs, credit unions, and microfinance organizations are showing that credit can be offered without collateral or complex paperwork, and at fair rates. But to scale that capacity, automation for financial inclusion in Latin America is essential.

Decision engines can analyze information in real time and assess applications even from people with no traditional banking history, using alternative data such as:

* Purchases at merchants that offer their own financing * Utility payment history * Digital behavior or mobile transactions

Lenders can build more complete risk profiles and offer microloans with greater security and lower operating costs.

No-code technology and BRMS: key tools for banks, savings institutions, credit unions, and fintechs

For risk engines, also called decision engines, to be genuinely effective, they need to be simple to implement and manage. Many lending companies still face the challenge of depending on legacy systems, small technical teams, or slow development cycles.

A decision engine built on no-code technology lets the risk or business teams themselves design and adjust their risk policies without depending on IT. That not only reduces time and cost: it speeds up the go-to-market of products designed specifically for segments that have historically been unbanked or without access to credit.

### What are decision engines in the credit industry?

A decision engine combines credit automation, AI models, and alternative data sources, all through APIs, and it is what is allowing many institutions to offer more accessible and scalable credit, even in highly informal contexts. Decision engines for microloans in particular are changing how financial institutions assess and approve applications.

Financial inclusion through technology: closer to closing the gap

At uFlow we understand that the fight against gota a gota lending can be waged by helping lenders committed to financial inclusion raise their credit approval rate without increasing delinquency, through immediate, secure, automated decisions.

uFlow’s decision engine is 100% web, cloud, and no-code. It lets banks, fintechs, credit unions, savings institutions, and financial institutions automate their credit assessment processes, integrate alternative data, and adjust their strategies autonomously, without depending on IT.