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Kuda Bank's Journey to Profitability through Data Visibility
Technology Category
- Analytics & Modeling - Predictive Analytics
- Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
- Cement
- Finance & Insurance
Applicable Functions
- Sales & Marketing
Use Cases
- Behavior & Emotion Tracking
- Leakage & Flood Monitoring
The Challenge
Kuda, a digital bank launched in Nigeria in 2019, experienced a fourfold increase in customers within six months. As a mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal. Initially, a five-person data team was manually building data pipelines and relied on SQL Server Reporting Services (SSRS) to extract insights from transactional databases. Their data was split into 12 disparate Azure SQL databases with no way to successfully join the data across their internal sources. The team was looking to move from running OLAP queries on an OLTP database, which was proving to be a challenging task. They needed a more scalable solution that would relieve them of having to build and manage data pipelines.
About The Customer
Kuda is a digital bank that was launched in Nigeria in 2019. It offers the country’s residents a simpler way to manage their personal finances through an app that includes tools for tracking expenditure and money management. The bank has been on a spectacular growth trajectory, with a fourfold increase in customers in just six months. As an agile, mobile-first digital company, Kuda recognized the need to be data-driven and identified the modern data stack as essential for achieving its goal.
The Solution
Kuda decided to use Fivetran for ELT, with dbt performing data transformations in BigQuery. Fivetran was chosen as the data pipeline, Google BigQuery as the data warehouse, dbt for data transformation, and Looker as the BI tool. The most valuable data came from Kuda’s customer-facing core banking transactional systems, which included metrics around customer acquisition, products, and service monitoring. Kuda also used advanced analytics for credit scoring, mitigating risk by making more informed overdraft offers to customers based on data. Data from Intercom, a customer conversation platform, was analyzed to help the support team allocate resources and plan for the type of questions they were most likely to be asked. The goal was to improve the customer experience and use insights to create better customer engagement models.
Operational Impact
Quantitative Benefit
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