Case Study: Fundbox achieves accelerated, scalable data-driven credit analysis with Snowflake

Accelerating Data-Driven Credit Analysis with Snowflake

Fundbox, an AI-enabled B2B fintech that accelerates access to working capital, needed to scale analytics and continuously refine credit models by ingesting large volumes of operational and customer data. Its legacy data warehouse was slow (complex queries took hours), couldn’t support concurrent BI workloads, imposed inefficient ETL for semi-structured JSON/XML data (processed via MongoDB), lacked historical change capture, and consumed engineering time for maintenance and disk management.

Fundbox migrated to Snowflake’s cloud data warehouse, using its multi-cluster shared architecture, native semi-structured support, and an Apache Kafka/Spark/ZooKeeper CDC pipeline. The move cut query times from hours to minutes, eliminated Tableau contention, streamlined ETL, and freed the data team from infrastructure chores. As a result, finance, marketing, product, and data-science teams can run contention-free reporting and real-time exploration of millions of records (powering an “X‑ray” credit model), with elastic capacity and per-second pricing supporting future data-source and modeling expansions.

Fundbox

Almog Shunim

Head of Data Infrastructure and BI