Tebogo G. Tlhopane
Speaker at AMLD Africa 2026
Tebogo G. Tlhopane is a South African entrepreneur and innovation leader working at the intersection of financial systems, regulated manufacturing, and technology-enabled compliance. His work spans fintech, health innovation, and sustainable industrial development, with a strong focus on traceability, transparency, and inclusion within emerging markets.
He is the founder of Zaka, a transaction and enablement platform designed to formalise informal trade by creating verifiable transaction histories, digital identities, and data trails for cash-based businesses — particularly within fresh produce and township economies. The platform supports financial inclusion by enabling compliance-ready participation in formal credit and payment systems, while strengthening AML visibility through structured transaction data.
Tebogo is also the CEO of BioMuti, a health and biotechnology company advancing botanical extraction, indigenous knowledge systems, and product innovation across nutraceuticals, medical applications, and bioplastics. Through BioMuti, he has led the development of traceable production models and data-driven innovation pipelines that integrate regulatory compliance, sustainability, and advanced manufacturing.
Across his ventures, Tebogo applies AI-enabled tools to support transaction monitoring, product traceability, risk analysis, and operational decision-making — not as standalone technologies, but as practical enablers of compliance, accountability, and scale.
His work reflects a broader commitment to building trusted systems that connect informal economies, regulated industries, and emerging technologies into transparent, inclusive, and compliant value chains.
At AMLD Africa
WITS University, Johannesburg, South Africa
Building Applied ML Systems in Data-Scarce Informal Economies
Informal economies represent one of the largest and most complex sources of economic activity globally, yet they remain largely invisible to traditional data systems. This talk explores how applied machine learning can operate effectively in data-scarce, cash-heavy environments by focusing on real-world signal capture rather than conventional datasets. Using Zaka as a case study, the presentation examines how everyday economic behaviour—transactions, movement, repetition, and patterns—can be transformed into structured, machine-readable data without disrupting existing market dynamics. Zaka is a wallet and de-cashing platform operating in informal markets such as fresh produce markets and other cash-intensive sectors, as well as the regulated cannabis social-club ecosystem, which includes over 6,000 stores processing approximately R5 billion per month. The session will unpack the architectural principles behind applied ML systems in informal contexts, including signal ingestion, behavioural modelling, dataset construction, and incremental intelligence generation. Rather than relying on large labelled datasets, the approach emphasises learning from activity, consistency, and scale. The talk also highlights how such systems enable financial inclusion, transaction profiling, credit formation, and compliance-ready intelligence while remaining accessible and practical for real-world deployment across African markets. This presentation is intended for practitioners, researchers, and policymakers interested in applied machine learning, AI infrastructure, and real-world deployment in emerging and informal economic systems.
