A Success Story with a Blind Spot

The integration of Financial Technology (FinTech) in Ghana has significantly expanded financial inclusion. Mobile money and digital financial services have brought millions of previously unbanked Ghanaians into the formal financial system; globally, the World Bank’s Global Findex survey documents how mobile accounts have driven a historic surge in account ownership across Sub-Saharan Africa, with the region leading the world in mobile money adoption (Demirgüç-Kunt, Klapper, Singer, & Ansar, 2022).

Industry data tell the same story from the supply side: Sub-Saharan Africa consistently accounts for the majority of the world’s registered mobile money accounts and transaction values (GSMA, 2024). Ghana’s regulators deserve credit for enabling this, the Payment Systems and Services Act, 2019 (Act 987) created a modern licensing regime for electronic money issuers and payment service providers, and the Bank of Ghana’s FinTech and Innovation Office and regulatory sandbox have given innovators a structured path to market (Republic of Ghana, 2019).

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This is a genuine development success story. But success creates its own frontier of risk. The same smartphones that deliver payments now deliver investment products: micro-investment apps, digital brokerage, robo-advisors, and at the unregulated fringe, speculative schemes and crypto platforms. As these services deploy Machine Learning (ML) to optimise user engagement, a critical regulatory blind spot is emerging: the exploitation of behavioural biases through algorithmic design.

Ghana has painful institutional memory of what happens when retail financial enthusiasm outruns protection; the widely reported gold-investment scheme collapses of the late 2010s wiped out household savings on a national scale. The next such episode may not need a fraudster at all—only an engagement algorithm doing exactly what it was built to do.

From Nudge to Algorithmic Nudge

Behavioural economics has long recognised that there is no neutral way to present a choice. Choice architects, those who design the environments in which decisions are made, inevitably influence outcomes through defaults, framing, salience, and feedback, a reality that grounds the case for designing choice environments that help rather than harm (Thaler & Sunstein, 2021). The insight cuts both ways. The same architecture that can nudge a worker into a pension can nudge a novice into a leveraged trade.

What digital platforms add is scale, personalisation, and relentless optimisation. A paper pamphlet nudges everyone identically, once. An ML-driven app observes each user’s behaviour in real time, what they tap, when they hesitate, which notifications they open, and continuously tunes its interventions to maximise a target variable. When that target is trading volume or session time rather than investor welfare, the app becomes a machine for discovering and pressing each user’s particular behavioural buttons. Regulators have coined a term for these techniques, “digital engagement practices” encompassing push notifications, gamified elements, leaderboards, streaks, and social features (U.S. Securities and Exchange Commission [U.S. SEC], 2021). The question for Ghana is not whether these practices will arrive; on many platforms they already have. The question is whether our rules will meet them.

The Behavioural Vulnerabilities of Retail Investors

In my ongoing research on financial literacy and retail investment decision-making in Ghana, overconfidence consistently emerges as a primary driver of suboptimal investment outcomes. When retail investors interact with highly intuitive, gamified trading applications, they frequently conflate interface simplicity with financial expertise. This overconfidence leads to excessive trading, inadequate diversification, and the gradual erosion of household wealth.

Two structural facts amplify the danger. The first is the global financial literacy deficit: decades of research show that large majorities of adults, especially the young, women, and lower-income groups, cannot correctly answer basic questions on compound interest, inflation, and diversification, and that this illiteracy predicts costly financial behaviour (Lusardi & Mitchell, 2023). The second is that digital platforms concentrate precisely these users. Ghana’s new digital investors are disproportionately young, mobile-first, and encountering capital markets for the first time through an interface engineered to feel like entertainment. Vulnerability and exposure arrive together.

The empirical evidence on what happens next is now robust. Analysing data from the zero-commission platform Robinhood, Barber, Huang, Odean, and Schwarz (2022) show that the app’s design, simplified displays, curated “Top Mover” lists, channelled user attention into a small set of stocks, producing intense herding episodes in which large numbers of users piled into the same securities within hours; on average, those attention-driven buying waves were followed by significantly negative returns for the buyers. The interface did not merely host the bias; it manufactured the coordination that made the bias expensive.

The Experimental Evidence: Design Is Destiny

Archival studies can struggle to separate the effect of design from the type of person a platform attracts. Experimental research closes that gap, and its findings should concentrate regulatory minds. In a randomised experiment published in Management Science, hedonic gamification features such as confetti animations and achievement badges increased trading volume, with participants of lower financial literacy disproportionately drawn to gamified platforms and disproportionately prone to noisy trading strategies; strikingly, price-trend notifications improved learning for investors with accurate beliefs but reinforced the mistakes of those with inaccurate beliefs (Chapkovski, Khapko, & Zoican, 2026).

A companion experiment shows that digital nudges encouraging users to hold volatile assets significantly amplify risk-taking, with the effect strongest in high-volatility environments and among inexperienced, low-literacy traders (Chapkovski, Khapko, & Zoican, 2025).

Regulators have replicated the pattern at scale. The UK Financial Conduct Authority built an experimental trading app and tested digital engagement practices on more than 9,000 consumers: push notifications and prize draws increased trading frequency by roughly 11–12 per cent and raised the share of trades placed in riskier investments, with larger effects among participants with low financial literacy, women, and the young (Financial Conduct Authority [FCA], 2024). This followed the FCA’s earlier warning that game-like design features; points, badges, leaderboards, frequent market notifications, risk blurring the line between investing and gambling-like behaviour (FCA, 2022).

Even robo-advisors, often marketed as a corrective to human bias, carry documented pitfalls alongside their promises, including opaque model constraints and conflicts embedded in their design (D’Acunto, Prabhala, & Rossi, 2019). The conclusion across methods and markets is the same: the interface is not neutral. It is an active behavioural instrument, and it acts most strongly on the least equipped.

The American Warning

The United States offers Ghana a compressed preview of the regulatory learning curve. In 2019, the U.S. SEC adopted Regulation Best Interest, obliging broker-dealers to place retail customers’ interests ahead of their own when making recommendations (U.S. SEC, 2019). Platform design promptly posed the question the rule had not anticipated: is a push notification a recommendation?

In December 2020, Massachusetts securities regulators filed a complaint against Robinhood alleging that its gamified design encouraged inexperienced customers toward risky, excessive trading. In June 2021, FINRA imposed what it announced as the largest financial penalty in its history—approximately US$70 million, on the same firm for, among other violations, harm caused to millions of customers through misleading communications and system failures (Financial Industry Regulatory Authority [FINRA], 2021).

The January 2021 “meme stock” episode then exposed how far platform design had outrun the framework; the SEC staff’s post-mortem specifically flagged gamification and other digital engagement practices as areas requiring regulatory attention (U.S. SEC Staff, 2021). Later that year, the Commission issued a formal request for public comment on digital engagement practices used by brokers and advisers to shape retail behaviour (U.S. SEC, 2021). By 2023, the SEC had proposed rules targeting conflicts of interest that arise when firms use predictive data analytics to steer investor behaviour toward the platform’s revenue interests rather than the investor’s welfare (U.S. SEC, 2023).

The trajectory, from fiduciary principle, to enforcement, to design-level scrutiny, to proposed rules on the algorithms themselves is unmistakable: regulators in the world’s deepest capital markets now treat algorithmic nudging as a core investor-protection issue, not a design curiosity. Ghana can traverse this learning curve by reading it rather than reliving it.

Ghana’s Regulatory Setting: Prepared on Paper, Exposed in Practice
Ghana’s formal architecture is stronger than it was a decade ago. The Securities Industry Act, 2016 (Act 929) gives the SEC Ghana broad authority over market conduct; Act 987 brought payment providers and electronic money issuers under Bank of Ghana licensing (Republic of Ghana, 2019); and the sandbox gives both regulators a window into innovations before they scale. But nothing in this architecture speaks to design-level conduct. There is no Ghanaian definition of an “algorithmic recommendation,” no standard governing engagement features on investment platforms, no requirement that a robo-advisor disclose how its model works or whose revenue it optimises, and no supervisory practice of inspecting an app’s behavioural architecture at licensing.

A platform could satisfy every current licensing requirement while running an engagement engine empirically shown, in the studies above to increase risky trading among exactly the users Ghana’s financial inclusion agenda has just brought into the market.
To protect retail investors without stifling innovation, I propose three regulatory contributions, each expanded below: mandated positive friction, algorithmic transparency standards, and RegTech-enabled surveillance.

Proposal 1: Mandating “Positive Friction” via Machine Learning

Regulators should require digital investment platforms to implement “positive friction.” Using their own ML capabilities, platforms should be required to detect impulsive, highly concentrated trading behaviours, rapid-fire orders, portfolio concentration in a single volatile asset, trading spikes immediately following push notifications, session patterns consistent with problem-gambling profiles. Upon detection, the application must trigger a graduated response: first, a mandatory, data-driven risk disclosure presented at the moment of decision; at higher risk thresholds, a short comprehension check confirming the investor understands the product’s risk; at the highest thresholds, a brief cooling-off interval before the trade executes. The U.S. SEC’s exploration of such mechanisms provides a viable template (U.S. SEC, 2021).

Two design principles matter. First, timing: the experimental finding that trend notifications reinforce the mistakes of investors with inaccurate beliefs (Chapkovski et al., 2026) underscores why disclosure must interrupt the decision itself, not sit in onboarding documents no one reads. Second, asymmetry: positive friction must be distinguished from “sludge” the FCA’s Consumer Duty explicitly condemns excessive frictions that stop consumers acting in their own interest (FCA, 2022). The rule should therefore prohibit platforms from adding friction to protective actions (withdrawals, account closure, complaint filing) while requiring it for risk-escalating ones. Friction, correctly aimed, is not paternalism; it is the digital equivalent of the pause a good human broker would impose.

Proposal 2: Establishing Algorithmic Transparency Standards

As ML-driven robo-advisors scale in Ghana, the SEC must define what constitutes an “algorithmic recommendation” and mandate plain-language disclosures. Investors must understand the parameters, constraints, and revenue incentives of the ML models managing their capital—precisely the opacity problem the robo-advising literature identifies (D’Acunto et al., 2019). Disclosure should be layered: a one-screen summary for retail users stating what the algorithm optimises, what it ignores, and how the platform earns money from user activity; and a structured technical filing to the regulator. For the latter, the “model card” format—a short standardised report disclosing a model’s intended use, performance, and known limitations, offers a ready-made template that Ghanaian rules could adopt wholesale rather than invent (Mitchell et al., 2019).

Critically, transparency must extend to conflicts. Where a platform’s revenue rises with user trading activity, and its algorithms are tuned to raise that activity, the conflict is structural, the very conflict the U.S. SEC’s predictive data analytics proposal targets (U.S. SEC, 2023). Ghana can pioneer a West African framework, potentially through ECOWAS capital markets integration structures, that requires FinTechs operating across the sub-region to disclose how algorithmic recommendations are generated, preventing the “black box” effect that obscures fiduciary duties and denying regulatory arbitrage to platforms that would shop for the lightest regime.

Proposal 3: Deploying RegTech for Market Surveillance

Regulators must utilise AI defensively. The SEC Ghana should partner with quantitative analytics firms and academic researchers to deploy ML models that monitor retail trading platforms for predatory engagement patterns, the same category of conduct the U.S. SEC’s proposal targets (U.S. SEC, 2023). The supervisory metrics are readily specifiable: portfolio churn rates by user cohort, concentration in volatile assets among first-year investors, the latency between push notifications and executed trades, and the distribution of realised returns across literacy proxies. Platforms should be required, as a licensing condition, to grant the regulator structured access to anonymised interaction data, turning the sandbox from a one-time gate into a continuous supervisory instrument.

This approach converts supervision from reactive complaint-handling to proactive pattern detection, and it scales regulatory capacity precisely where headcount cannot. It also creates a virtuous research loop: anonymised supervisory data, shared under agreement with Ghanaian universities, would generate the local evidence base, who trades, how engagement features affect them, what interventions work, that neither the FCA’s London experiment nor American archival studies can supply for a mobile-money-first market.

Anticipating the Objections

Three objections will be raised. First, that regulation will chill innovation. The evidence points the other way: the UK moved from warning (FCA, 2022) to experiment (FCA, 2024) to supervisory expectations without extinguishing its FinTech sector, and clear rules give compliant Ghanaian firms certainty, itself a competitive asset when courting institutional partners. The direction of global travel is set; the only question is whether Ghanaian firms build to the standard early or retrofit expensively later. Second, that positive friction is paternalistic. But choice architecture is unavoidable—every app design nudges somewhere (Thaler & Sunstein, 2021); the policy question is never whether to influence investors but whose interests the influence serves. Requiring that the architecture not be tuned against its users is the opposite of paternalism: it is the precondition of genuine choice.

Third, that the SEC Ghana lacks capacity. This is precisely why the proposals sequence disclosure and licensing conditions, which leverage the platforms’ own capabilities, before supervisory technology, and why academic partnership is built into the surveillance proposal. Capacity is not a precondition for starting; it is a product of starting.

An Implementation Roadmap

In the first twelve months, the SEC Ghana should publish a guidance note defining “algorithmic recommendation” and “digital engagement practice” for the Ghanaian market, drawing directly on the U.S. and UK texts (U.S. SEC, 2021; FCA, 2022), and should require sandbox applicants with investment products to document their engagement features and behavioural testing. In the second phase, spanning years two and three, positive-friction capabilities, layered algorithmic disclosure, and structured data-access undertakings should become standard licensing conditions for digital investment platforms, with model-card filings for any ML system that generates recommendations (Mitchell et al., 2019).

In the third phase, the SEC should stand up its supervisory analytics capability with university partners, publish thematic findings, initiate ECOWAS-level harmonisation, and enforce,visibly against platforms whose engagement architecture demonstrably damages the users it targets. Sequenced this way, the regime asks platforms first to look, then to show, then to answer.

Implications for Policy, Practice, and Research

For regulators, the implication is that investor protection in a digital market must be behavioural, not merely disclosural. The SEC Ghana should issue guidance defining “algorithmic recommendation” and “digital engagement practice” before gamified platforms reach scale, drawing directly on the U.S. SEC’s framework and the FCA’s experimental evidence (U.S. SEC, 2021, 2023; FCA, 2024). The Bank of Ghana, through its FinTech and Innovation Office, can operationalise this at the licensing stage: making positive-friction features and algorithmic transparency disclosures a condition of authorisation is far cheaper than enforcement after household wealth has been eroded. Regulating design early also gives compliant FinTechs certainty, which is itself pro-innovation.

For FinTech firms and platform designers, the evidence should reframe interface design as a fiduciary act. Features that measurably inflate trading volume and risk-taking among the least financially literate users (Chapkovski, Khapko, & Zoican, 2026; Barber, Huang, Odean, & Schwarz, 2022; FCA, 2024) are not neutral growth tactics; they are latent regulatory and reputational liabilities, as the record American penalties demonstrate (FINRA, 2021). Firms that voluntarily adopt positive friction, plain-language model disclosures, and internal behavioural audits will be better positioned when regulation arrives, and better trusted by the customers whose long-term wealth their business models ultimately depend on.

For investors and financial educators, the practical lesson is that ease of use is not evidence of expertise, and engagement is not advice. National financial literacy programmes—whose importance the global evidence base places beyond dispute (Lusardi & Mitchell, 2023)—should explicitly teach how engagement features such as notifications, streaks, and badges are designed to shape behaviour, converting the overconfidence channel identified in my research into a teachable moment rather than an exploitable one.

For researchers, Ghana currently lacks platform-level evidence of its own. Empirical work linking user-interaction data from Ghanaian trading and micro-investment apps to portfolio outcomes, extending the experimental and archival findings from advanced markets (Chapkovski et al., 2025, 2026; Barber et al., 2022; FCA, 2024) to a mobile-money-first environment would give the SEC Ghana the local evidence base its rulemaking will require, and the supervisory data-sharing arrangement proposed above is the most direct route to generating it.

Conclusion

Ghana’s FinTech revolution has delivered genuine financial inclusion, but inclusion without protection is an unfinished project. This paper has traced the mechanism, from choice architecture, through the documented behavioural vulnerabilities of retail investors, to the experimental and regulatory evidence that gamified, algorithmically optimised platforms measurably increase risky trading among the least equipped users, and has shown that Ghana’s current framework, strong on licensing, is silent on design. It has proposed three guardrails; mandated positive friction, algorithmic transparency standards anchored in model-card disclosure, and RegTech-enabled market surveillance, sequenced in a roadmap that respects the SEC Ghana’s real capacity constraints.

The choice before Ghanaian regulators is not between innovation and protection—it is between shaping the behavioural architecture of our digital markets now, or repairing the damage later at far greater cost, as the American enforcement record and Ghana’s own history of retail investment losses both attest. The financial inclusion gains of the past decade (Demirgüç-Kunt et al., 2022; GSMA, 2024) are too valuable to squander on an avoidable crisis of retail investor exploitation. By acting before—rather than after—our own gamified trading boom, Ghana can build a digital financial ecosystem that is both innovative and fundamentally protective of its investors, and in doing so set the regulatory standard for West Africa.

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