Descubre el futuro de las finanzas en América Latina y el Caribe

The future of finance in LatAm & the Caribbean

O futuro das finanças na América Latina e no Caribe



Uber and Didi test alternative loans for the gig economy

Feb 7, 2022

By Antony Pinedo
Uber y Didi prueban créditos alternativos para la gig economy

Uber and Didi are experimenting with user and driver data to assess credit risk—in some cases receiving hundreds of requests a month—amid a growing trend of alternative finance in Latin America

Uber, the ubiquitous ride-hailing app, receives hundreds of requests each month from drivers in Latin America. It’s an example of the potential for alternative data to generate credit risk histories for users and drivers in the gig economy. Uber, and its rival Didi, are both making financial sense of non-transactional information, such as driving records and brand loyalty, in order to tap the growing market for alternative loans.

In the last decade, the number of collaborative economy apps has increased tenfold, spurring the development of loans for the delivery people, drivers and customers that use the platforms.

Historically, the self-employed have had limited access to the traditional financial system, because often they can’t prove that they have a steady income stream even when they do. This partly explains why companies, such as banks, fintechs and technology firms, are looking to use their data to predict user behavior.

"Financial evaluation systems adapted to new models of flexible earnings generation are essential for financial democratization," tells Uber to iupana.

Uber received 700 requests per month on average in the last quarter of 2021. “Weekly there are requests from the driving partners, consistently," says Uber.

In this vein, Didi launched Didi Loans in Mexico in October 2021. The platform offers credit to drivers and users of up to approximately US$1,450. The continued use of the Didi app is key to accessing the loan service.

“Only users with the longest and most frequent use of the app will receive the invitation, with the intention of guaranteeing a better service. Gradually we’ll extend it to all our users,” explains Jordi Cueto-Felgueroso, public relations manager for Didi in Mexico.

He said the company expects "significant growth” in loans but declined to disclose details on the number of approvals.

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Gig economy and embedded finance

Although the debate surrounding working conditions in the collaborative economy is ongoing—in Peru lawmakers are analyzing a bill that seeks to regulate the ride-hailing apps, and in Colombia congress rejected a similar proposal last week—the perception in any Latin American city is that there are ever more delivery people, and the data confirm this is indeed the case.

At the start of 2020, Rappi had about 30,000 delivery drivers and by mid-2021 it had more than 50,000, just in Mexico. And, according to the International Labor Organization (ILO), Chile had approximately 600,000 delivery and taxi-app drivers in the first half of 2020.

This has led the financial industry—incumbents and disruptors—to devise ways to cater to this market, often through open or emended finance alliances.

In Peru, Uber directs its top-ranked drivers to Mibanco, the microfinance arm of Lima-based Credicorp, to start their credit applications. In an example of open finance, drivers use their app login credentials (username and password) to enter the bank's portal, which can then access the applicant’s driving history.

And this trend might not stop at users and drivers.

“We believe that SMEs are no longer going to go to a bank to ask for a loan […] they’re going to receive a loan offer through Pedidos Ya, through Rappi or through Clip. We’re transforming Latin American platforms into financial providers,” says Roger Larach, CEO of R2, an embedded lending company with operations in Mexico, Colombia and Ecuador.

R2 builds technological infrastructure that allows third-party apps to analyze and approve loan requests from small and medium-sized companies using alternative data.

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Alternative data versus credit bureaus

There are clear signs that the demand for financing in the platform economy will continue to grow and won’t push up default rates.

In Colombia, applications to B2B fintech Ábaco Latam, which provides credit scoring software for the self-employed, quintupled in 2021. And in Chile, Lana, a Spanish fintech focused on gig workers, distributed some 10,000 loans averaging $600 after making its debut in the country last year.

Abaco’s loan approval rate was 30% while defaults were 2%, according to Victoria Blanco, co-founder and CEO.

Lana, meanwhile, calculates its default rate at 3%, which reveals the good performance of the segment, with a 50% loan-approval rate.

“Experience has told us that app workers are quite responsible with payments because they have very few alternatives, so if they fail, they are left with fewer options,” says Álvaro Jara, who’s co-founder and country head of Lana in Chile. The firm also operates in Mexico

A credit bureau rating shouldn’t be the last word when it comes to evaluating a loan, says Tomás Costanzo, CEO of Uils, an Argentine fintech that lends to app drivers.

The company consults the bureaus "at an informative level, not at a binding level," he says, adding: "We use the driving history as a credit score; it’s the only thing we pay attention to when lending money."

Uils has developed a data processing system that analyzes more than 200 variables, such as how long a driver has been registered with the app, the information from each trip, the time they start work, the time between each trip, the rating from passengers and the number of fares that are paid in cash or with a card.

Similarly, Lana consults bureaus such as Dicom in Chile during the loan evaluation process, but even if a client receives a bad score, that’s not the decisive factor.

In Lana’s case, it has a digital savings account that’s act as the basis of its lending service: gig workers are required to use the account to deposit their earnings. The company also verifies worker performance data and the length of time they have worked for their particular platform.

"We are analyzing based on how critical the debt is, using a kind of yellow or red traffic light depending on the weight or size of the debt," he explains.

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