According to GSMA Association (GSMA), the sub-Saharan Africa region is at the epicentre of mobile money on the continent, with 835 million registered accounts.
These represent nearly 50% of the global share of US$2,5 billion transactions daily in 2023. The GSMA is a non-profit trade association that represents mobile network operators worldwide. This represents 19% year-on-year growth and accounts for two thirds of global transaction values.
Camil Bennani Smires, in an article entitled How Digital Wallets have Transformed the lives of Millions in Africa, highlighted that Africa continues to be the world’s leader in mobile banking. The disparity between cell phone penetration and financial inclusion has resulted in significant growth in Africa’s digital wallet market, allowing users to manage their money without access to a formal bank account. According to World Bank data, just 54% of African adults have a bank account.
Cell phone penetration in the region reached 61% in 2023. It is the transition from informal to formal financial management that has opened up a whole new world of possibilities for Africa’s unbanked and underbanked. In this installment, we look at how AI can be harnessed for product development in order to advance financial inclusion by Zimbabwean non-bank financial institutions (NBFIs).
AI offers Zimbabwe’s NBFIs a path to broaden financial inclusion while preserving prudent risk management, transparency and regulatory alignment. For Zimbabwe, where informal credit, mobile money and agent networks dominate the financial landscape, AI is key in helping NBFIs design more accessible credit products, deliver affordable payments, support small-scale savers and tailor micro-insurance, agriculture and even health offerings.
The guiding principle is to treat data governance, privacy, fairness and explainability as foundational constraints, embedded from the outset in product design, risk management and governance. Hence this installment integrates a Zimbabwe-specific lens on data provenance, regulatory expectations, delivery channels and macroeconomic realities, outlining how AI can be pragmatically applied within NBFIs’ operating models, partner ecosystems and customer journeys.
Zimbabwe presents a distinctive blend of opportunities and constraints for NBFIs. A large informal economy, prevalent micro and small enterprise economy and a heavy reliance on mobile money create a dense data tapestry that, if responsibly harnessed, could improve credit access and resilience.
OneMoney, Omari, InnBucks, EcoCash and other mobile money ecosystems have embedded agent networks that reach rural and peri-urban populations. These enable onboarding, payments and loan collections in environments with limited traditional banking infrastructure.
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Yet the country’s macro-economic volatility demands product terms, pricing and repayment schedules that are adaptable, transparent and clearly communicated in local currency terms. In this context, AI can help NBFIs differentiate credit risk more accurately using alternative signals such as, mobile wallet histories, merchant cash flows, utility payments and savings behaviour.
The opportunity is not just to approve more borrowers, but to do so in a way that strengthens financial resilience, lowers onboarding friction and sustains responsible growth across urban, peri-urban and rural markets.
Regulatory, governance landscape
The regulatory environment that governs Zimbabwean NBFIs is anchored by the Reserve Bank of Zimbabwe (RBZ). The RBZ supervises non bank financial institutions, including microfinance institutions (MFIs) and deposit taking microfinance institutions (DTMFIs). In parallel, anti-money laundering and countering the financing of terrorism (AML/CFT) obligations shape how customer due diligence, transaction monitoring and reporting are conducted. Data privacy considerations in Zimbabwe are increasingly anchored by the country’s data protection regime, with consent based data processing, user rights and retention controls, complemented by RBZ expectations around data governance for financial service providers.
While regulatory specifics continue to evolve, the practical objective for NBFIs is to implement risk based, auditable AI practices that satisfy licensing requirements, support transaction integrity and protect customer rights. Consumer disclosures, transparent pricing, fair lending standards and accessible grievance mechanisms remain central to regulatory expectations and market trust.
Data, privacy, interoperability
Zimbabwe offers a rich albeit fragmented data landscape. Mobile money activity — primarily through OneMoney, Omari, InnBucks, EcoCash and other wallet ecosystems — provides near real time signals of customer cash flows, repayment patterns and transaction behaviour. Utilities data, merchant cash flows and retail and agricultural cash movements can augment traditional credit signals, especially for individuals and micro-entrepreneurs with thin credit files. Data quality and timeliness can vary across rural and urban areas, requiring careful data governance, robust data provenance and clear ownership across partner networks.
Privacy and consent are paramount, with a need for opt-in data sharing, transparent disclosures about how data informs underwriting and pricing, and robust access controls. Data localisation considerations and regulatory expectations for data sharing and portability should guide architecture and vendor arrangements, while interoperability through channels such as ZimSwitch and RBZ-aligned rails can facilitate secure, compliant data exchange and payments.
A Zimbabwe-focused AI programme for NBFIs rests on a set of pillars designed to fit local market realities. Data strategy and governance form the bedrock: a clear data provenance framework, consent management, data minimisation, retention policies and a centralised feature store with lineage and versioning enable repeatable experimentation and safe deployment across multiple products.
Continuous data quality monitoring and bias auditing are essential to detect representation gaps and potential disparities across rural versus urban customers, gender groups and regional communities. These governance practices must be matched by disciplined model development and operations, with a focus on translating business objectives — expanding access to credit, fair and affordable pricing, smoother onboarding, fraud prevention and compassionate collections, into measurable AI-enabled solutions.
Modelling and analytics in the Zimbabwean milieu should be case-driven. For tabular data typical of credit decisions, gradient-boosting methods and interpretable ensembles often deliver strong performance, while natural language processing and speech models support multilingual customer interactions in English, Shona and Ndebele and assist with document processing in low-connectivity settings.
Privacy preserving techniques, such as differential privacy, federated learning or on-device inference, should be considered where data centralisation is sensitive or restricted by regulation. Explainability and fairness must accompany all high stakes decisions, with local explanations for individuals and aggregated fairness assessments across communities and regions.
Product design and user experience must be inclusive and locally resonant. Interfaces should be accessible to customers with varying literacy and digital capabilities, including those using unstructured supplementary service data (USSD) and feature phones.
Local languages should be embedded, along with culturally appropriate disclosures, pictorial explanations and simplified term sheets that reflect Zimbabwean currency and pricing conventions. A human in the loop approach remains essential for edge cases, disputes and regulatory validation, ensuring that automated decisions are transparent and defensible. The delivery model should emphasise transparency of terms, clear disclosures about fees and repayment schedules, and intuitive mechanisms for customers to understand how data informs terms.
Distribution, operations and service delivery must leverage Zimbabwe’s agent networks and digital channels. AI-enabled tools can assist agents with onboarding decisions, risk flags and guided conversations that improve customer understanding and adherence to terms. Real time risk monitoring should span model performance, drift and operational health across channels, branchless digital channels, USSD, mobile apps and agent networks, so that the institution can respond promptly to changing conditions and customer needs.
Risk management and compliance technologies provide the governance backbone. A formal model risk management programme, encompassing validation, documentation, retraining and independent audits, is essential to maintain credibility with regulators, partners and customers.
AI practices must align with RBZ’s AML/CFT requirements, consumer protection standards and applicable data privacy laws. An ethics and accountability framework, with transparent redress mechanisms for customers affected by automated decisions, reinforces trust and competitive differentiation.
Product development for realities
Underwriting and credit access hold significant potential for inclusive growth. By incorporating alternative data such as OneMoney, InnBucks, Omari, EcoCash transaction histories, merchant cash flows, Zesa utility payments and inferred savings behaviour, NBFIs could extend credit to borrowers with limited formal credit history.
Dynamic pricing and product tiering could tailor loan terms into size, tenure and repayment schedule to observed cash-flow patterns, while ensuring affordability through guardrails that discourage predatory practices. Onboarding and KYC would exploit AI-assisted document verification and identity checks that function in low connectivity environments and with partial IDs, delivering faster, compliant onboarding without compromising security.
In payments and remittance, AI would be able to optimise routing decisions, fee structures and settlement timing to reduce costs for customers and merchants operating in high usage corridors. Fraud detection benefits from context aware models that account for mobile money usage patterns, merchant activity and typical transaction flows, reducing false positives in a high volume, low value payments landscape.
Savings and micro-insurance use cases are well suited to Zimbabwe’s rural and agrarian segments. Predictive nudges and goal-based features would drive saving habits, while micro-insurance offers would be priced and tailored to seasonal risks, such as drought or flood based on micro level usage signals and agrarian calendars.
There would be opportunities for financial literacy and advisory functions to be amplified with AI-powered coaching in local languages, delivering budgeting tips, risk disclosures and practical examples relevant to Zimbabwean livelihoods. Conversational agents would support onboarding, product explanations and basic dispute resolution, with escalation pathways to human agents for complex situations. Identity onboarding and ongoing compliance necessitate lightweight verification strategies that respect privacy, while maintaining controls, with AML/CFT monitoring tuned to Zimbabwe’s risk landscape across wallets and agent networks.
Ndoro-Mukombachoto is a former academic and banker. She has consulted widely in strategy, entrepreneurship, and private sector development for organisations in Zimbabwe, the sub-region and overseas. As a writer and entrepreneur with interests in property, hospitality and manufacturing, she continues in strategy consulting, also sharing through her podcast @HeartfeltwithGloria. — +263 772 236 341.
