Creditas is a loan fintech primarily operating in Brazil and Mexico. Their core products include home equity loans, vehicle equity loans and insurance. In addition to the B2C approach, the company also leverages B2B strategies to attract new consumers by offering credit products to businesses looking to expand their portfolios.

One of Creditas' major partnerships is with the fintech Nubank, a pioneer in the Brazilian financial services sector.

Through integration with the API, our partner, such as Nubank, can share their customers' data with us, enabling us to perform the necessary credit assessments.

Challenge

Through conversion rate indicators, we have identified that 40% of the leads received were denied due to discrepancies in vehicle data.

So, how can we optimize the vehicle data filling process to make it more efficient and user-friendly, aiming to increase the credit approval rate? 🔮

Process & Role

Before reaching the end user, it was crucial to understand the motivators and limitations of the partner company. While mapping Nubank's journey, we encountered several intriguing questions, such as:

1. Which user profiles were being exposed to the option of vehicle-secured loans?

2. At which points of interaction was the user informed about credit availability?

3. How can we enhance the efficiency of user data queries, eliminating the need for data entry on the frontend?

4. Which user profiles were being exposed to the option of vehicle-secured loans?

5. At which points of interaction was the user informed about credit availability?

Here are some finds

o obtain the resources needed to develop this initiative, it was necessary to align with various stakeholders, understand the credit processing workflow, and seek insights from end users.

• In a survey integrated into the registration process, 70% of responding users indicated that they did not wish to use their car as collateral.

• Among these respondents, 95% mentioned that they were unaware of the requirement to have a vehicle registered in their name.

• In previous interviews, we identified that some users simulate out of curiosity, without a genuine intention to apply for the loan at that moment.

• It was not clear to the user that they had to fill in all vehicle details during the simulation.

The outcome

Based on our findings, alignments, and brainstorming sessions with the team, we have arrived at an initial optimization version. Our proposal is to make adjustments to Creditas' lead generation form and, after gathering additional insights, present concrete and effective recommendations to the partner.

In this first phase, we will incorporate license plate verification directly into the backend, along with enhancements to the manual vehicle details input process.