Fintech startup, Pngme, closes $15m Series A to increase customer base, expand team

Pngme, a platform that provides financial institutions and fintech startups with users data via its API and machine learning infrastructure, has announced a $15 million Series A.

The round was led by European VC firm, Octopus Ventures. Other participants were Raptor Group, Lateral Capital, EchoVC, Future Africa, Two Small Fish Ventures, and US-based early-stage VC firm for immigrant-founded startups, Unshackled Ventures.

The list concludes with several angel investors, including Hayden Simmons, Dan Kahn, Richard Talbot, and Kyle Ellicott of Intersect VC.

This investment follows a $3 million seed announced in February 2021 and a $500k pre-seed in 2019. The company’s funding now totals $18.5 million since its launch in 2018.

With this funding, the startup is looking to strengthen its team and increase its customer base. To do this, they have hired new executives and are currently hiring members for their engineering, data science, and sales team.

Founded by Brendan Playford (CEO) and Cate Rung (COO), Pngme focuses on providing actionable financial data that helps in building products, improving customer service, and understanding financial behaviour for digital banks and credit platforms.

Currently operating in Kenya, Nigeria, and South Africa, the startup has partnered with traditional banks like Amalgamated Banks of South Africa (ABSA), United Bank for Africa (UBA), and First Bank. And digital financial platforms like Kuda, Umba, Renmoney, TransUnion Africa, and CredPal.

Regarding short-term goals, Rung reveals that they plan to expand their network of third party data connections to other markets and improve on their Insight Library products.

As financial services become more digitised, there is an increased need for a central channel of customer data to build products that suit users’ unique needs. And like Pngme, African startups like Mono and Okra already play in this space.

Per TechCrunch, Playford believes that Pngme stands out due to its machine learning model, which allows it to combine data from traditional banks and other API infrastructure.

“What we do is that we’ve kind of really differentiated ourselves to be not just collecting the data that we can see but also, we can connect to Mono data, Okra data, and we can connect with banks’ data.

“We essentially merge all that data and then put machine learning models on top for the clients. That can be predictive credit models, segmentation models and really positioning ourselves as a data processing infrastructure for banks and fintechs.”

With a mobile Software Development Kit (SDK) and data processing pipeline, they provide a framework that allows financial institutions and fintechs to collect and aggregate financial data on a large scale.

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