The Algorithm That Knows Your Credit Score: Why MTN, Safaricom and Airtel Are Betting Billions on AI

The Algorithm That Knows Your Credit Score–Mobile money transactions reached $2 trillion globally in 2025, with Africa leading the surge. MTN, Safaricom and Airtel are deploying AI for fraud detection, credit scoring and super‑app personalisation. Our deep‑dive analysis reveals how AI is transforming African digital finance – and the infrastructure, regulatory and trust challenges that remain.

Executive Introduction

The Algorithm That Knows Your Credit Score

Mobile money has already reshaped the financial landscape of Africa more profoundly than any innovation since the introduction of the banknote. In 2025 alone, registered mobile money accounts across the continent surpassed 800 million, and annual transaction values soared past the $2 trillion mark globally, with Africa accounting for the overwhelming majority of that volume. Sixty percent of providers now report that interoperability and consumer protection frameworks have directly supported their platforms. The continent is no longer a passive adopter of digital finance; it is its undisputed heartbeat.

Yet the most transformative phase of Africa’s mobile money revolution is only just beginning. The technology that brought peer‑to‑peer transfers and bill payments to the palms of millions is now being supercharged by artificial intelligence. Machine learning algorithms are scanning mobile money trails to build credit scores for individuals who have never stepped inside a bank branch. AI systems are detecting fraudulent messages before they ever reach a customer’s phone, shifting the security paradigm from reactive rule‑based monitoring to real‑time behavioural analysis. Chatbots are answering customer queries in local languages across WhatsApp and SMS at 2am, while predictive models are optimising agent networks, routing cross‑border payments and personalising loan offers.

The major players are moving decisively. MTN Group’s fintech arm processed over $500 billion in transaction value in 2025, serving 69.5 million active users across 16 markets. Safaricom has unveiled My OneApp, an AI‑powered super‑app that merges M‑PESA and its core telecom interface into a single intelligent ecosystem – what the company calls “FinTech 2.0”. Airtel Africa is already deploying AI for mobile money fraud prevention, spam detection and energy optimisation, while investing in large‑scale data centres across Nigeria and Kenya to keep Africa’s data within the continent.

This profile examines how artificial intelligence is quietly but fundamentally re‑engineering every layer of Africa’s mobile money value chain. It analyses the mechanisms – from behavioural fraud detection to alternative credit scoring, from agent network optimisation to conversational customer service – that are driving the shift. It weighs the emerging challenges of algorithmic bias, fragmented regulation and infrastructure gaps. And it maps the trajectory of an industry where AI is no longer a futuristic add‑on but an operational necessity. For the 800 million registered mobile money users across Africa, the future of digital finance is not a distant vision. It is already being coded, trained and deployed – one algorithm at a time.

How AI Is Reshaping the Mobile Money Ecosystem

Fraud Detection – The Shift from Rules to Behaviour

For years, fraud detection in mobile money relied on static rule‑based systems – simple thresholds that flagged transactions exceeding a certain amount or unusual locations. Fraudsters quickly learned to operate just below these thresholds, cycling through large volumes of phone numbers or email addresses in ways that traditional monitoring could not detect. The result was a steady erosion of trust. Cyber fraud losses across Africa have risen sharply, with multi‑step fraud attacks growing by 180 per cent globally, testing the limits of conventional defences.

Artificial intelligence has fundamentally altered this calculus. Instead of waiting for a rule to be violated, AI‑driven systems establish a baseline of normal transaction behaviour for each user – their typical transaction size, frequency, time of day and geographic location – and then flag deviations from that baseline in real time.

MTN has been at the forefront of this shift. In early 2026, MTN deployed artificial intelligence and machine learning technologies to detect and block fraudulent Mobile Money messages before they reach customers. According to Abdul‑Majeed Rufai, MTN’s Senior Manager for Fintech, the AI‑driven solution enables real‑time detection of suspicious patterns, allowing the operator to act swiftly to prevent financial losses. The system can identify attempts to mimic official transaction alerts, even when fraudsters manipulate message formats or introduce spelling errors.

Airtel Africa has followed a similar path. In Rwanda, the operator unveiled a new AI‑powered system designed to detect fraudulent text messages used in mobile money scams involving impersonation. When suspicious messages are detected, customers receive them with a warning label advising caution – a simple but effective intervention that leverages AI to pre‑empt fraud rather than merely investigate it after the fact.

The broader ecosystem is also responding. Anti‑fraud technology providers such as Orca Fraud have raised $2.35 million to expand payment monitoring across emerging markets, using machine‑learning models trained on real‑world emerging‑market data to detect fraud across channels in real time. SmartComply has equipped banks with AI‑driven financial crime detection platforms designed specifically for African regulatory environments, offering behavioural pattern recognition and anomaly detection.

Perhaps most strikingly, Flexifai – a payment technology provider specialising in high‑friction markets – reported that a deployment in Ghana raised payment conversion rates from 43 per cent to 73 per cent within 30 days, using pattern‑based fraud detection and real‑time routing as the primary drivers. The provider noted that mobile money‑based systems in emerging markets have historically lacked the anti‑fraud monitoring infrastructure that card‑based environments typically carry. AI is filling that gap – not incrementally, but dramatically.

Credit Scoring – Reaching the Unbanked Majority

The most profound economic impact of AI on African mobile money is unfolding in the quiet, unglamorous world of credit scoring. Across the continent, fewer than 30 per cent of adults have a formal credit history. Traditional banks have little data on which to base lending decisions, and millions of individuals – particularly those in the informal sector – remain effectively invisible to the formal financial system.

AI is changing that by extracting predictive signals from alternative data that had previously been considered noise. Mobile money transaction history, smartphone usage patterns, utility bill payments, airtime recharge behaviour and even call data records are being fed into machine learning models that generate credit scores for individuals who have never taken a formal loan.

Zambian fintech eShandi has built an entire lending platform around this principle. The company uses AI‑powered credit scoring to offer loans to underbanked individuals and small and medium‑sized enterprises, bypassing the need for traditional documentation required by conventional banks. “These sources include mobile money transaction history, smartphone usage, and transaction behaviour on eShandi’s platform,” the company’s head of credit explains. What were once merely logs of daily spending have become the raw material for financial inclusion.

Nigerian fintech VeendHQ has launched Vida AI, a platform that applies machine learning to alternative data sets – utility bills, smartphone usage, spending patterns – to evaluate creditworthiness. The system delivers instant approvals and fraud prevention for banks, lenders and merchants. By enabling real‑time risk profiling and quicker decision‑making, Vida AI can streamline the loan approval process while minimising default rates.

In South Africa, TransUnion Africa has partnered with MTN and Chenosis to launch CreditVision Telco Data Score, a first‑of‑its‑kind credit scoring solution that uses mobile phone call data records to help millions of South Africans with limited or no formal credit history gain access to financial services. The score is built on anonymised, permissioned mobile behavioural data, sourced through TransUnion’s partnership with the country’s leading telcos.

Zambian online bank Lupiya has built a completely online loan application process that enables quick assessment and disbursement of financing, thanks to its credit scoring system based on advanced machine learning models. Ghana‑based digital lender Fido secured $5.5 million in debt funding from impact investor Symbiotics in early 2026 to scale its AI lending operations in Ghana and Uganda.

The market potential is substantial. Modern SME lending platforms integrating open banking, AI‑powered risk models and digital infrastructure can reduce the cost‑to‑serve by 60‑80 per cent, making small‑ticket lending economically viable across Sub‑Saharan Africa. For the millions of Africans who use mobile money daily but have never qualified for a bank loan, AI‑driven credit scoring is not a convenience. It is a gateway.

Agent Network Optimisation – The AI Behind the Agent

The agent network – the hundreds of thousands of small‑scale entrepreneurs who facilitate cash‑in and cash‑out transactions – is the physical backbone of African mobile money. Yet agent networks are notoriously inefficient. Urban areas are often overcrowded with agents competing for thin margins, while rural communities remain underserved. Agent liquidity shortages – running out of cash for withdrawals or having too much idle cash – are chronic problems that drive agent inactivity.

AI is beginning to solve these inefficiencies. Fastagger, a solution targeting Ghana’s mobile money ecosystem, enables telcos to layer intelligent AI services, including agent and merchant analytics, SME credit scoring and fraud detection, directly on user devices, without relying on the cloud. By processing data locally, the system can provide real‑time recommendations to agents about optimal cash balances, peak transaction hours and potential credit risks.

MobiFin’s geo‑fencing solution combines real‑time merchant alerts with business intelligence, notifying agents the moment a customer enters a designated area. The solution addresses the operational challenges that make agent networks expensive to manage. Canza Finance has launched CAPP, an autonomous AI protocol that uses “Mobile Money Bridge Agents” to unify the connection of 156 mobile payment systems across Africa, enabling over 400 million unbanked users to access the digital economy using only their mobile phones.

These are not futuristic pilots. They are live deployments addressing the most concrete constraints of mobile money distribution. When an agent knows precisely how much cash to hold, when to expect peak demand and which customers are most likely to default, the entire system becomes more efficient – and the cost of serving the last mile falls.

Conversational AI – The 24/7 Customer Service Revolution

The 9‑to‑5 service window is dead. African mobile money users expect answers at midnight, on public holidays, during network outages. They expect to check balances, dispute transactions and apply for loans without waiting on hold.

Conversational AI has become the default solution. MTN’s MoMo chatbot fields mobile money queries over WhatsApp and SMS, handling routine interactions such as balance checks, transaction confirmations and basic inquiries. In South Africa, digital‑first banks including Capitec and TymeBank route large volumes of customer support through automated assistants. Nigerian startup Lodum AI Banking allows users to carry out banking transactions, manage payments and access lifestyle services simply by chatting or speaking – without downloading a mobile application.

The key insight is not technological novelty but practical scale. Chatbots in African banking are not about originality. They are about solving everyday problems at scale. When implemented thoughtfully, they help users understand financial systems, complete tasks with confidence and remain engaged over time. For mobile money operators, the economics are compelling: a chatbot subscription costs a fraction of a human call centre agent, scales instantly and works across every time zone.

Voice‑enabled, multilingual AI is the next frontier. HocPay has unveiled AI features enabling real‑time fraud detection using behavioural analytics, 24/7 customer support through intelligent chatbots, and personalised financial recommendations based on user behaviour. The platform addresses the fragmentation that has historically limited access to digital financial services – inconsistent customer experience, fragmented data and limited support channels.

The Super‑App Shift – From Wallet to Intelligent Financial Ecosystem

The most ambitious AI deployment in African mobile money is not about improving a single function but about reimagining the entire platform. The mobile money wallet is evolving into an intelligent financial ecosystem – a super‑app that knows its user, anticipates their needs and offers services before the user even asks.

Safaricom has taken the most significant step in this direction. In April 2026, the Kenyan telco unveiled My OneApp, a redesigned AI‑powered platform that merges M‑PESA and the MySafaricom App into a single intelligent ecosystem. The company describes the move as a major step in its transition to “FinTech 2.0” – a cloud‑native, AI‑first platform that enables real‑time monitoring, fraud detection and automated issue resolution.

The strategic ambition behind My OneApp goes far beyond convenience. Safaricom is moving toward hyper‑personalised finance, deploying AI that instantly analyses a user’s digital behaviour to offer incredibly specific financial products – tailored payment plans, personalised savings goals, micro‑insurance bundles and dynamic credit limits. The company is also expanding M‑PESA’s cross‑border payment capabilities, integrating AI to ensure reliability, security and personalisation.

MTN Group has signalled a similar trajectory. At its inaugural FinTech Summit in Johannesburg in 2025, the company outlined a vision of mobile money as a platform that enables users not only to make payments but also to access loans, savings and digital commerce tools. MTN Mobile Money Zambia has already partnered with JUMO to bring an AI‑powered overdraft service to the market, positioning the platform as a comprehensive lending marketplace.

Airtel Africa is investing heavily in the infrastructure required for this transition. The company has launched a next‑generation, cloud‑native mobile money platform designed to deliver greater scalability, agility and resilience. The cloud‑native architecture will enhance system uptime, reduce operational costs and support future innovations such as open APIs, advanced analytics and AI‑based financial solutions.

Cross‑Border Payments – AI as the Interoperability Engine

Cross‑border payments in Africa have historically been slow, expensive and opaque. The fragmentation of payment systems – 156 distinct mobile money platforms across the continent – has made seamless integration a distant dream. AI is beginning to change that.

United Bank for Africa (UBA) has made its AI‑powered chatbot, LEO, the first in Africa to facilitate cross‑border payments. Integrated with the Pan‑African Payment and Settlement System (PAPSS), LEO enables real‑time local currency transfers across African countries through widely used platforms such as WhatsApp and Facebook Messenger. Customers can now transfer funds across borders without leaving the chat interface – a profound reduction in friction.

Canza Finance’s CAPP protocol represents a different but equally significant approach. By using autonomous AI agents to unify 156 mobile payment systems, the protocol enables over 400 million unbanked users to access the digital economy without barriers, using only their mobile phones. The core innovation – Mobile Money Bridge Agents – acts as a universal translator between fragmented payment systems, allowing value to move across borders as seamlessly as within them.

These developments matter because cross‑border remittances are a lifeline for millions of African families. By reducing friction, AI‑powered cross‑border payments lower costs, speed settlement and increase transparency. For the African diaspora and the families that depend on their remittances, this is not an abstract efficiency gain – it is a direct increase in disposable income.

New Business Models – Bundling AI Tools Through Mobile Money

The convergence of AI and mobile money is also giving rise to entirely new business models. The most notable is the bundling of AI creation tools as a mobile money service – a development that turns mobile wallets from a payment rail into a distribution channel for advanced digital services.

In February 2026, M‑PESA Ethiopia and Gebeya Inc. launched the Dala AI Bundle, marking the first time an African mobile money operator has packaged AI‑powered creation tools as a mainstream product. Subscribers can now pay for advanced AI capabilities – tools that were previously accessible only to those with international credit cards – directly through their M‑PESA wallets. For the first time, ordinary Ethiopians can buy AI creation tools without needing a credit card, using only their phone and M‑PESA account.

The partnership signals a shift in how mobile money platforms are positioned within Africa’s digital economy. They are no longer merely conduits for value transfer; they are becoming gateways to the broader digital economy. As AI tools become essential for productivity, creativity and entrepreneurship, the ability to pay for them through mobile money could become a significant competitive advantage – and a new revenue stream for the platforms themselves.

Challenges and Constraints – The Roadblocks Ahead

For all the promise of AI in mobile money, the path forward is not frictionless. Four structural challenges will determine whether AI becomes a tool of inclusion or an engine of exclusion.

Infrastructure Gaps – AI systems require computing power that is still concentrated in a handful of African markets. Data centre capacity is limited, and reliable electricity remains a challenge in many regions. Mobile money platforms that rely on cloud‑based AI models can face latency issues that degrade user experience. Edge computing – processing data on the device itself – offers a partial solution, but scaling it across millions of low‑end smartphones is nontrivial.

Data Fragmentation and Privacy – For AI credit scoring to work effectively, data from multiple sources – banks, mobile money operators, utility companies, telecoms – must be integrated. Yet African data protection regimes are fragmented, with 45 countries having enacted data protection legislation but enforcement varying widely. The lack of harmonised cross‑border data sharing rules makes it difficult to build models that work across multiple markets. There is also the risk of algorithmic bias: if training data reflects historical exclusion, the model will perpetuate it.

Regulatory Uncertainty – Regulators across Africa are grappling with how to supervise AI in financial services. The Bank of Ghana’s CISD 2026 directive, which sets governance standards for AI in fraud detection and credit scoring, is an early model. But other markets lag. The World Bank estimates that fewer than 20 African countries have national AI strategies, and those that do often lack implementation capacity. Without clear rules on data localisation, algorithmic accountability and consumer redress, mobile money operators will face rising compliance costs and legal uncertainty.

Trust and Transparency – The Publican AI crisis at Ghana’s ports in early 2026, where importers rejected an opaque AI customs valuation system as a “black box”, offers a cautionary tale for mobile money. If AI systems deny loans, flag transactions as fraudulent or charge higher fees without transparent explanations, users will lose trust. The challenge is to design AI systems that are not only accurate but also explainable – and to build grievance mechanisms that allow users to contest automated decisions. As a global survey found, 92 per cent of Ghanaians regard AI tools as important, but trust remains fragile. A single algorithmic failure can set back adoption by years.

Talent Shortages – Building and maintaining AI systems requires data scientists, machine learning engineers and cloud infrastructure specialists – roles that are in short supply globally and even scarcer across Africa. While initiatives such as the One Million Coders programme in Ghana and various tech hubs across the continent are expanding the talent pipeline, the gap between supply and demand remains wide. Mobile money operators must compete not only with each other but also with global tech firms for the same limited pool of expertise.

Future Outlook – Three Scenarios for AI and Mobile Money in Africa

The trajectory of AI in African mobile money will be shaped by three variables: the pace of infrastructure investment, the depth of regulatory harmonisation and the ability of operators to build user trust.

Scenario One – Gradual Integration (65 per cent probability).

In this base case, AI adoption proceeds steadily but unevenly. Fraud detection and credit scoring become standard across major mobile money platforms, driving significant improvements in security and financial inclusion. Agent network optimisation and conversational AI are deployed in urban and peri‑urban areas but reach rural communities slowly. Super‑app ambitions are realised partially – in Kenya, Ghana and Nigeria – but not continent‑wide. Cross‑border payment friction declines incrementally, not dramatically. AI‑bundled services remain niche. Total mobile money transaction value grows at 15‑20 per cent annually, and AI contributes to a 10‑15 per cent reduction in fraud‑related losses. Financial inclusion rates rise from current levels (approximately 80 per cent in Ghana, lower in other markets) to 85‑90 per cent by 2030.

Scenario Two – Accelerated Breakthrough (25 per cent probability).

The $1 billion‑plus AI investments from MTN, Safaricom and Airtel trigger a virtuous cycle of innovation. Open API frameworks and harmonised data protection regimes enable seamless cross‑border data sharing and model training. Super‑apps become the dominant mobile money interface across multiple markets, with AI‑driven hyper‑personalisation becoming the norm rather than the exception. Agent networks are optimised in real time, reducing the cost of serving rural customers by 30‑40 per cent. Cross‑border payments become near‑instant and near‑free, integrated with PAPSS and multiple regional payment systems. AI‑bundled services – from content creation to small business tools – become significant revenue streams. Mobile money transaction value reaches $4‑5 trillion by 2030, and AI‑driven credit products extend formal lending to an additional 200‑300 million previously unbanked Africans.

Scenario Three – Stagnation and Fragmentation (10 per cent probability).

Infrastructure investments are delayed. Regulatory fragmentation persists, with some countries imposing restrictive data localisation requirements that hinder cross‑border innovation. A high‑profile AI failure – a credit scoring model that systematically excludes a particular demographic, a fraud detection system that blocks legitimate transactions en masse – erodes user trust. Public and political backlash leads to over‑regulation, increasing compliance costs and slowing deployment. The gap between the “Big Four” markets – Nigeria, Kenya, South Africa and Egypt – and the rest of the continent widens. Mobile money transaction value continues to grow but at a slower pace (10‑12 per cent annually), and AI adoption stalls. The promise of AI‑driven financial inclusion remains unfulfilled for the majority of Africans.

Conclusion

The marriage of artificial intelligence and mobile money is not a distant promise. It is already reshaping how Africans borrow, save, pay and protect themselves against fraud. In Zambia, an AI‑powered credit scoring model opens formal lending to individuals who have never held a bank account. In Ghana, a behavioural fraud detection system intercepts scam messages before they reach a trader’s phone. In Ethiopia, a mobile money wallet now sells AI creation tools as a monthly subscription. In Kenya, an AI‑powered super‑app knows its user well enough to offer a personalised loan before the user even asks.

The numbers capture the scale of the opportunity. Over $500 billion in annual fintech transaction value across MTN’s platforms alone. A market projected to reach $16.5 billion by 2030. Sixty percent of providers reporting that interoperability and consumer protection frameworks have directly supported their platforms. Fraud detection systems that raise payment conversion rates from 43 per cent to 73 per cent in 30 days. Credit scoring models that reduce the cost‑to‑serve by 60‑80 per cent.

Yet the road ahead is not smooth. Infrastructure gaps, data fragmentation, regulatory uncertainty, trust deficits and talent shortages all threaten to slow the pace of adoption. The Publican AI crisis in Ghana – where an opaque customs valuation system was rejected as a “black box” – is a warning: AI systems that are not explainable, accountable and contestable will face resistance, not adoption.

The question is no longer whether AI will transform mobile money in Africa. It is already doing so. The question is whether the transformation will be inclusive, transparent and sustainable – or whether it will entrench existing inequalities and create new ones. For the 800 million registered mobile money users across the continent, the answer will be written not in algorithms alone, but in the policies, investments and governance frameworks that surround them. The technology is ready. The rest of the ecosystem has work to do. And the clock is running.

Frequently Asked Questions (FAQ)

Q1: How is AI being used in African mobile money today?

AI is being deployed across five primary applications in African mobile money: real‑time fraud detection using behavioural analysis; alternative credit scoring using mobile money transaction data, smartphone usage and utility payments; agent network optimisation to manage liquidity and service coverage; conversational AI chatbots for 24/7 customer support; and cross‑border payment routing to reduce friction and cost.

Q2: How much money moves through African mobile money platforms annually?

Global mobile money transaction value reached $2 trillion in 2025, doubling over four years, with Africa accounting for the overwhelming majority of volume. Active 30‑day accounts rose by 15 per cent to 593 million. MTN Group’s fintech arm alone processed $500.3 billion in transaction value, serving 69.5 million active users across 16 markets.

Q3: Which mobile money operators are leading in AI adoption?

Safaricom has launched My OneApp, an AI‑powered super‑app merging M‑PESA and its core telecom interface into a single intelligent ecosystem. MTN Group has deployed AI for fraud detection and partnered with JUMO on AI‑powered overdrafts in Zambia. Airtel Africa is using AI for mobile money fraud prevention, spam detection and energy optimisation, and has launched a cloud‑native mobile money platform to support AI‑based solutions.

Q4: How does AI improve mobile money fraud detection?

Traditional fraud detection used static rule‑based systems that fraudsters could learn to evade. AI‑driven systems establish a baseline of normal transaction behaviour for each user – typical transaction size, frequency, time of day, geographic location – and then flag deviations in real time. MTN has deployed AI to detect and block fraudulent Mobile Money messages before they reach customers.

Q5: Can AI help unbanked Africans access credit?

Yes. AI‑powered credit scoring uses alternative data – mobile money transaction history, smartphone usage patterns, utility bill payments and airtime recharge behaviour – to generate credit scores for individuals with no formal credit history. Zambian fintech eShandi, Nigerian platform VeendHQ, and Zambia’s Lupiya bank are all using AI to extend credit to previously excluded populations.

Q6: What is a “super‑app” in the African mobile money context?

A super‑app is an integrated digital ecosystem that combines mobile money payments, lending, savings, insurance, digital commerce and telecom services into a single intelligent interface. Safaricom’s My OneApp – which merges M‑PESA and MySafaricom into one platform – is the leading example. The super‑app uses AI to personalise financial products based on user behaviour.

Q7: How does AI reduce the cost of mobile money lending?

Modern SME lending platforms integrating open banking, AI‑powered risk models and digital infrastructure can reduce the cost‑to‑serve by 60‑80 per cent, making small‑ticket lending economically viable across Sub‑Saharan Africa. AI automates credit assessment, fraud detection and loan monitoring, replacing manual processes that are expensive and slow.

Q8: What is the Publican AI crisis and why does it matter for mobile money?

In early 2026, Ghana’s port authority deployed Publican AI, an opaque customs valuation system that importers rejected as a “black box” because they could not understand how valuations were determined or appeal decisions. The backlash is a cautionary tale for mobile money: if AI systems deny loans or flag transactions without transparent explanations, user trust will erode.

Q9: What are the biggest barriers to AI adoption in African mobile money?

The five main barriers are infrastructure gaps (limited data centre capacity, unreliable electricity), data fragmentation (45 different data protection regimes), regulatory uncertainty (fewer than 20 African countries have national AI strategies), trust and transparency deficits (lack of explainable AI and grievance mechanisms), and talent shortages (scarcity of data scientists and machine learning engineers).

Q10: How large is the AI market in Africa and how fast is it growing?

Africa’s AI market was valued at $4.5 billion in 2025 and is projected to reach $16.5 billion by 2030, a compound annual growth rate of approximately 27 per cent. African AI startups raised over $40 million in 2025, a 78 per cent year‑on‑year increase, though the continent still captures less than 0.05 per cent of global AI funding.

Q11: How are AI and mobile money enabling cross‑border payments in Africa?

United Bank for Africa (UBA) has made its AI chatbot LEO the first in Africa to facilitate cross‑border payments, integrated with the Pan‑African Payment and Settlement System (PAPSS). Canza Finance’s CAPP protocol uses autonomous AI agents to unify 156 mobile payment systems, enabling over 400 million unbanked users to access cross‑border digital payments using only their phones.

Q12: What is the future outlook for AI and mobile money in Africa?

The most likely scenario is gradual integration, with AI adoption proceeding steadily but unevenly across the continent. Fraud detection and credit scoring will become standard, super‑apps will scale in major markets, and cross‑border payments will improve incrementally. A genuine breakthrough – reaching the $16.5 billion AI market target – would require accelerated infrastructure investment, regulatory harmonisation and a concerted effort to build user trust in automated systems.

Esther Aku-Sika

Esther Aku-Sika is a content writer and social media strategist who helps brands and startups grow through intentional storytelling and practical marketing strategies. With a keen eye for trends and audience behavior, she shares business insights, content strategies, and real-life lessons to help entrepreneurs build visibility and turn ideas into income. Through her writing, she simplifies complex concepts and equips readers with actionable steps to grow in today’s digital space.