San Francisco- and Africa-based fintech Pngme has snapped up another $15 million for its financial data infrastructure play. The company is also describing itself as a machine learning-as-a-service platform. Octopus Ventures led the Series A round, with follow-on investment from Lateral Capital, EchoVC and Raptor Group. Other investors like Unshackled Ventures, Future Africa and Two Small Fish Ventures participated too. Pngme also received checks from angel investors; some include Hayden Simmons of RallyCap, Plaid’s Dan Kahn, Richard Talbot of RBC Capital and Kyle Ellicott of Intersect VC. Pngme’s platform caters to fintechs and other financial institutions across sub-Saharan Africa. When the founders, Brendan Playford and Cate Rung, last spoke with TechCrunch, Pngme was heading out of stealth mode in Nigeria, Kenya and South Africa.
Pngme has three core products for clients in these three markets. In addition to its already known API and mobile SDK, Pngme has added a customer management platform. The company says combining the three products will drive its customers’ adoption and use of personalized user experiences and financial products. It’s a highly data-driven user experience. And every fintech or bank wants to provide that same data-driven user experience. From instant loans or savings, or overdrafts, or whatever it might be, it’s all just like a user experience around a product. Most African financial institutions and fintechs are racing to offer fully customized user experiences and financial products tailored to their customers’ needs. To fuel these products and user experiences, data infrastructure is needed. Machine learning models are supposed to be trained to acquire, retain and maximize the lifetime value of a customer. These processes can be expensive and time-consuming, leaving them with the difficult task of choosing between building the infrastructure or serving their customer. Pngme allows financial institutions and fintechs to collect and aggregate financial data at scale. The company says its mobile SDK and data processing pipelines collect alternative financial data and unify it with other data sources to create a holistic picture of an individual’s financial behavior.