Case Study: Tala achieves faster real-time credit decisions with Outerbounds

Scaling Global Financial Inclusion with Real-Time Machine Learning Using Outerbounds

Tala, a global financial services platform serving underbanked communities, faced significant challenges scaling its real-time machine learning operations. Their existing infrastructure, built on tools like Kubeflow and Airflow, was slow and cumbersome, requiring months to manually retrain models and onboard new data scientists. This created a major bottleneck for deploying accurate credit approval models. To overcome this, Tala turned to Outerbounds and its platform to automate their ML pipelines.

By implementing the Outerbounds platform, Tala achieved full automation of its model retraining and deployment processes. This solution delivered a 500% increase in annual model deployment frequency and reduced the onboarding time for new data scientists from months to just days. Model retraining time was slashed from months to days, providing the agility needed for business growth and more accurate real-time credit decisions for their customers. Outerbounds provided the scalable and compliant infrastructure critical to Tala's mission of financial inclusion.

Tala

Will High

Head of Data Science and Machine Learning Engineering