Pngme Raises $15million For Financial Data Infrastructure Play
In a bid to ensuring Unbundling financial data through APIs and driving data-driven insights, Pngme has continued with its value-add products in Africa.
This is even as investors have kept rasing more money for the purpose.
Less than a year after its $3 million seed round, the 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, Raptor Group and Two Small Fish Ventures. Other investors like Unshackled Ventures, Future Africa, Lagos-based Aruwa Capital, and The51 participated too. Pngme also received checks from angel investors; some include Hayden Simmons of RallyCap, Plaid’s Dan Kahn, Richard Talbot, ex-COO of RBC Capital Markets 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 Ghana.
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.
“The pain point we solve is the cost of building the infrastructure is very high. And the data science, the data engineering talent, just globally is really hard to find. So building a data infrastructure as a service works really well because it’s a subscription to get those services which you’d normally need a five- or six-person team to build this infrastructure.”