Nelo increases the buying power of consumers in Latin America by providing a modern alternative to a credit card.
Here at Nelo, our data science team uses machine learning models to make inferences about consumers' capacity and intent to repay on time. We used Jupyter notebooks to explore, analyze, tune and test our models. To productionize our models, we have iterated through several solutions; however, it was difficult to strike a balance between simplicity and reliability until we found Modelbit.
In moving to Modelbit, our data scientists are able to iterate on these models faster and with fewer dependencies on engineering. Its ease of use made it faster to ramp new team members, and iterate on features/models frequently, leading to more rapid business results.
