CUSTOMER STORIES
Lula Unlocks Near Real-Time Lending and Fraud Protection with Snowflake
Lula centralized siloed data on Snowflake to power fraud detection, near real-time credit decisioning, and AI-driven innovation, giving small to medium enterprises (SMEs) faster access to capital and greater trust in lending.
24Hours of credit access now available to customers
Industry
TechnologyLocation
Cape Town, South AfricaStory highlights
- Transforming banking for small to medium enterprises: By moving from fragmented systems to a central, AI-powered data foundation, Lula built a single view of customers that has made data more reliable, broken down silos and made it easier to provide insights.
- Enhancing fraud detection and efficiency: With fraud models powered by Cortex AI, Lula has dramatically accelerated investigation time and boosted customer trust.
- Accelerating lending and credit decisioning: Lula’s models are now running much faster, unlocking the potential for more near real-time credit decisioning and greater competitive advantage.
Video Transcript
Lula is primarily an alternative lending business to SMEs in South Africa. More recently, we've ventured into banking, and specifically banking for SMEs. Lula's mission is to empower every SME to succeed. We found that SMEs are underserved by the traditional banks. Getting access to capital is slow. It's difficult for them to meet their demands. That's where Lula steps in with very quick access to capital within 24 hours. We've got a wealth of data that we've collected over the last 10 years. We've just really been struggling to use it efficiently.
Every department within the company had this siloed data set. They were trying to answer the same questions, looking at different data, it was really difficult to make big changes to how we do upgraded decisioning, how we do our reporting, how we provide insights to customers.
What we really needed to do was just reset our data strategy.
Snowflake allows us to build this sort of foundational layer. We're moving away from having these fragmented systems into this central AI factory.
What was one of the key strategic goals was to build a single view of customer, so a centralised source of all of our customer and company data. There are a number of use cases that we've earmarked to leverage that single view of customer.
We've managed to build off our own internal fraud models based off of our Snowflake data. It's gone through rounds of quality control, centralization. The company know they can trust it. They know they've got good data to work off of. What we've actually done is we've used Cortex to provide insights layer to them as well. Using Cortex, we've already gone; "look, it was flagged because of this, and this is the insights into why we think this is potentially fraud." The amount of time that they now spend per case has dropped dramatically. And then, of course, from a customer perspective, it gives a lot of trust and gives us that added extra protection to our customers. Also a major win for us is that our models are running so much faster, which does unlock the potential for a more near real time credit decisioning going forward, which is something our competitors haven't even accessed on that kind of level.
We've made significant progress in a short space of time. Snowflake absolutely enabled rapid development.
