Data for Finance: Can Better Data change the Cost of Capital?

Weak information does not remove risk. It makes risk harder to measure, easier to overprice and more expensive to finance.

Image generated by Google Nano Banana 2 model on Magnific, ‘Can Better Data Change the Cost of Capital?’

In African financial markets, the cost of capital is not only determined by the risks that exist, but also how clearly those risks can be identified, measured and priced.

Lenders, investors, insurers and other providers of capital assess a range of risks when determining the price and terms of financing. These include credit risk, currency volatility, political and regulatory uncertainty, liquidity constraints, execution risk and the strength of legal and institutional frameworks. All of these can legitimately influence the cost of capital.

Financing costs are not only driven by underlying risk. They can also reflect information asymmetry, where the lender or investor does not have sufficient reliable information to fully assess the quality of a borrower, transaction, project or market. When the available evidence is incomplete, fragmented or difficult to verify, uncertainty increases and that uncertainty can be shows up in pricing.

The distinction is important because a higher financing cost sometimes reflects not only greater risk but also limited visibility into that risk.

Where information is incomplete, fragmented, outdated or difficult to verify, financiers have little confidence in the robustness of their risk assessment. That uncertainty is reflected in the terms of financing through higher margins, stronger collateral requirements, shorter tenors, lower exposure limits, more extensive due diligence and slower decision-making.

The result is that the cost of capital can increase even when the underlying risk itself has not materially changed.

This is the central argument of this essay:

Better data can influence the cost of capital by reducing information asymmetry and improving the quality of risk assessment, differentiation and pricing.

Better data does not remove macroeconomic volatility, currency risk, policy uncertainty, credit risk or institutional weakness. Nor can it substitute for stronger economic fundamentals.

What it can do is improve the quality, timeliness and reliability of the evidence available to financial decision-makers.

This distinction matters because financing decisions reflect both the risks that can be observed and the uncertainty created by gaps in information. Where those gaps are significant, lenders and investors are more likely to adopt a cautious pricing stance, often through higher margins, tighter terms or reduced exposure.

What shapes the Cost of Capital?

Image generated by Google Nano Banana 2 model on Magnific, ‘What shapes the Cost of Capital: How risk, market conditions and data influence the price of finance.’

At its simplest, the cost of capital is the minimum rate of return required by lenders and investors to compensate for risk, uncertainty, liquidity and prevailing market conditions.

For a business, this may be reflected in the interest rate on a loan. For a government, it may be the yield investors demand on sovereign debt. For an infrastructure project, it may be the return required by lenders and equity investors. In trade finance, it may appear in the margin, fees, tenor and collateral requirements attached to a facility.

The principle is straightforward: the greater the perceived risk of providing capital, the greater the return financiers are likely to require.

That assessment is influenced by a wide range of factors, including inflation, benchmark interest rates, currency volatility, sovereign risk, liquidity conditions, regulatory stability, contract enforceability, governance and broader market sentiment.

But the quality of information available to assess those factors also matters.

Where lenders and investors have reliable, timely and verifiable information, they are better able to distinguish between different levels and types of risk. Where information is incomplete, inconsistent or difficult to validate, financiers typically respond by tightening terms.

Margins may increase, collateral requirements may become more demanding, tenors may shorten, exposure limits may fall and due diligence or the approval process become more extensive and rigorous.

This is where better data begins to influence the cost of capital.

It does not make an inherently risky borrower, project or market safe. But better data can reduce uncertainty around the borrower, transaction, project or market, giving capital providers a stronger basis for assessing risk and setting terms with greater confidence.

That has important implications for how financing is priced.

The stronger the evidence, the more accurately lenders and investors can price the underlying risk, rather than adding a premium for what they cannot confidently assess.

The Cost of Incomplete Information in Financial Decision-Making

Image generated by Google Nano Banana 2 model on Magnific, ‘When uncertainty becomes part of the Cost of Capital: How Incomplete Evidence Changes Financial Decisions.’

One of the challenges in financial decision-making is that limited information about a borrower, transaction, project or sector can be interpreted as evidence of higher risk.

A borrower with incomplete records is not automatically a poor credit risk. A trader with fragmented documentation is not necessarily more likely to default. An infrastructure project with little historical performance data may still have strong commercial fundamentals. A farmer without formal yield records may still have a consistent production history; what changes is the financier’s level of confidence.

When risk can be identified and assessed with reliable evidence, lenders and investors have more options. They can price it more accurately, structure collateral appropriately, use guarantees or insurance, diversify exposure or apply other forms of mitigation.

Where the evidence is weak, those decisions become harder to make with precision and financing terms are more likely to reflect the uncertainty around the risk as well as the risk itself.

This is especially relevant in African markets, where a significant share of economic activity is still not captured consistently within formal data systems.

A business may be profitable but have limited audited financial records. A farmer may have years of productive history without reliable yield data. A trader may have a strong execution record, but that evidence may sit across banks, customs platforms, logistics providers and paper documentation. An infrastructure project may have sound commercial fundamentals, yet limited comparable performance data. An SME may generate healthy cashflows while still having only a modest formal credit footprint.

The underlying activity is real. The challenge is that the evidence around it is often incomplete, dispersed or difficult to verify. That weakens the financier’s ability to assess risk with confidence and can lead to more conservative pricing, tighter terms or reduced exposure. In practice, financing terms do not only reflect on the underlying risk, but also the uncertainty created by gaps in the available information.

The Information Premium

Image generated by Google Nano Banana 2 model on Magnific, ‘How Data gaps increase the Cost of Capital: When Financial Decisions depend on fragmented evidence.’

Risk premiums are a familiar feature of financial markets. They represent the additional return lenders and investors require as compensation for exposure to risk.

But financing costs can also include another, less visible component: the cost created by incomplete data.

This information premium arises when capital providers do not have enough reliable, timely and verifiable data to assess a borrower, transaction, project, sector or country with confidence.

It can emerge from weak financial records, incomplete credit histories, fragmented transaction data, unreliable collateral information, inconsistent trade documentation, limited project-performance data, outdated sector statistics, incomplete public registries or poor visibility into repayment and claims behaviour.

In these circumstances, financiers are not only pricing the risk they can identify. They are also pricing the uncertainty created by what they cannot verify.

The effect can be material. Borrowers face higher margins than their underlying risk profile would justify. SMEs struggle to access affordable credit despite strong commercial activity. Traders may be required to provide additional documentation or guarantees. Infrastructure projects may face longer and more expensive due diligence. Entire sectors can be treated as broadly high-risk because there is insufficient evidence to differentiate stronger opportunities from weaker ones.

The central point is simple: the quality of information affects the quality of risk pricing.

Where evidence is stronger, capital providers can assess risk with greater precision. Where evidence is weak, they are more likely to apply broader assumptions and more conservative financing terms.

A portion of Africa’s cost of capital, therefore, may reflect not only the risks inherent in its markets, but also the cost of incomplete visibility into those risks. That is where better data becomes more than a reporting asset.

It becomes part of the financial infrastructure required to price capital more accurately.

Scenario 1: SME Finance

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Across African markets, SMEs contribute significantly to economic activity, even so, many still face persistent barriers to affordable finance. These barriers appear through higher lending rates, lower credit limits, shorter tenors and more demanding collateral requirements.

While smaller businesses can be more exposed to cashflow volatility, market shocks, concentration risk, weak governance and operational vulnerability; their financing outcomes are also shaped by how much reliable data lenders have to assess those risks.

A viable business may generate strong sales but have limited audited accounts. A retailer may show consistent transaction activity but have only a modest formal credit history. A manufacturer may have recurring supplier relationships, purchase orders and inventory movement, while the supporting records remain fragmented across different systems. A trader may have healthy turnover but operate partly through informal channels that are not fully visible to the lender.

In these cases, the challenge is not necessarily weak commercial performance. It is weak visibility into that performance.

When lenders cannot build a sufficiently complete view of the business, they are more likely to respond conservatively. They may require additional collateral, reduce the amount they are willing to lend, shorten repayment periods or apply higher pricing to compensate for the uncertainty. Better data can improve that assessment.

A complete view of SME performance lives within their day-to-day operations: transaction histories, payment behaviour, invoice records, tax information, supplier relationships, customer concentration, inventory movement, digital payment activity and repayment history. When these fragmented data points are connected responsibly, they can provide lenders with a stronger basis for assessing creditworthiness beyond traditional collateral. With consent-governed alternative data, previously invisible operating cashflows can be translated into verifiable credit evidence and stronger borrower profiles.

The objective is not to increase the volume of data available to lenders. It is to improve the quality of the evidence used to assess the performance of a business. With stronger, more connected data, financiers can distinguish more accurately between SMEs that are genuinely high-risk and those that are commercially viable but poorly represented in traditional credit records.

For many SMEs, affordable finance not only depends on business performance but on whether that performance can be captured, verified and presented in a form the financing system can trust.

Scenario 2: Trade Finance

Image generated by Google Nano Banana 2 model on Magnific, ‘Trade Finance: How better transaction visibility can strengthen financing confidence.’

Trade finance depends heavily on the quality of information. Before committing capital, a financier requires confidence in the parties involved, the goods being traded, the validity of the documentation, the movement of the shipment, the payment obligations and the risks associated with the transaction and the trade corridor.

This creates a particular challenge in African trade, where a single transaction may involve multiple institutions and service providers, including exporters, importers, banks, insurers, freight forwarders, customs authorities, standards agencies, ports, warehouses and logistics operators.

Each participant may hold part of the information required to assess the transaction. The problem arises when that information is held in separate systems, recorded in different formats, updated at different times, or difficult to verify across institutions.

For a financier, those information gaps matter.

If the identity or history of a counterparty cannot be verified easily, the lender has less confidence in the transaction. If shipment status is difficult to track, exposure becomes harder to monitor. If documentation is inconsistent or repeatedly validated manually, processing takes longer. If there is limited historical data on a trade corridor, product, buyer or exporter, the financier has less evidence on which to assess performance and potential loss.

None of these factors necessarily means that the underlying transaction is high-risk. But collectively, they reduce the financier’s ability to assess that risk with precision.

The response is often reflected in the structure and price of the facility. Additional documentation may be required. Due diligence may become more extensive. Collateral or guarantees may increase. Approval timelines may lengthen. Pricing may become more conservative, and appetite for smaller or less-established traders may reduce.

This is where stronger trade data can materially improve financing decisions.

Reliable business identity, digital trade documentation, shipment and logistics data, counterparty history, customs and standards information, payment performance, corridor-level intelligence and trusted data exchange between institutions can provide a more complete view of the transaction.

The objective is not simply to digitise more trade documents. It is to create a reliable evidence base around the transaction so that financiers can understand what is happening, verify key facts more efficiently and monitor risk throughout the life of the facility.

This matters particularly for smaller exporters and traders, whose track records may exist across transactions, suppliers, logistics providers and payment systems but may not yet be consolidated into a form that financiers can use confidently.

Better trade data will not eliminate genuine risks. Buyers can still default. Shipments can still be delayed. Currency movements can affect settlement. Documentation can still be disputed. Regulatory and corridor risks can still materialise.

What better data can do is reduce the amount of uncertainty surrounding those risks.

And when financiers can assess a transaction with stronger evidence, they are better positioned to price the actual risk involved rather than applying a broader premium for limited visibility.

In trade finance, better visibility does not remove risk. It improves the basis on which that risk is understood, structured and priced.

Scenario 3: Sovereign and Institutional Credibility

Image generated by Google Nano Banana 2 model on Magnific, ‘Sovereign and institutional credibility: When reliable public data strengthens market confidence.’

The cost of capital is shaped not only at the level of individual borrowers and projects. It is also influenced by the quality and credibility of the wider institutional environment in which financing takes place.

Governments, public institutions and state-owned enterprises all access capital, while infrastructure and development programmes depend heavily on the confidence of lenders and investors. That confidence is informed by a range of factors, including fiscal performance, debt sustainability, reserve adequacy, policy consistency, revenue mobilisation, procurement quality, institutional effectiveness and the credibility of public reporting.

Better data does not remove macroeconomic or sovereign risk. Inflation, currency volatility, debt pressure, weak reserves, political uncertainty and policy instability remain material factors in financing decisions.

What stronger data can do is improve the quality of evidence available to assess those risks.

Clear and timely debt reporting can improve visibility into fiscal obligations. Reliable public financial data can strengthen confidence in budget execution and revenue performance. Transparent procurement and project data can provide greater insight into how public funds are allocated and how effectively projects are delivered. Consistent reporting on arrears, contingent liabilities and public-sector payment performance can also help investors form a more complete view of institutional risk.

Over time, the consistency and credibility of this information matter.

Markets are more able to assess risk when the underlying evidence is current, transparent and verifiable. Where data is incomplete, delayed or difficult to reconcile, uncertainty increases and investors may respond by requiring a higher return or limiting their exposure.

This does not mean that better reporting alone will reduce sovereign borrowing costs. Financing conditions remain heavily influenced by macroeconomic fundamentals, global liquidity, monetary policy, geopolitical developments and broader investor sentiment.

But weak information can amplify existing concerns.

At the sovereign and institutional level, better data therefore contributes to something finance values deeply: credibility.

And credibility, when built consistently through evidence, can influence how risk is perceived and ultimately how capital is priced.

Scenario 4: Agriculture and Climate Finance

Image generated by Google Nano Banana 2 model on Magnific, ‘Agriculture and Climate Finance: Using better data to price specific risks instead of the entire sector.’

Agriculture presents a genuine financing challenge because its risks are often both significant and highly variable.

Production can be affected by rainfall, pests, input costs, storage conditions, market access, commodity prices, logistics constraints and climate events. Farmers may also have limited collateral, fragmented financial records and little formal insurance coverage. These factors can make agricultural lending and investment difficult to assess and expensive to structure.

The problem is that where the underlying data is weak, those risks are often assessed too broadly.

A lender may have limited visibility into a farmer’s repayment behaviour or historical production. An insurer may lack reliable claims data. An investor may not have enough information on crop yields, irrigation access, market linkages or regional weather patterns. A climate finance provider may be working with incomplete evidence on soil conditions, resilience measures or adaptation outcomes.

Without this information, very different agricultural risks can become grouped together and priced in much the same way.

A rain-fed maize farmer in one region does not carry the same risk profile as an irrigated horticulture producer supplying under a long-term offtake agreement. A farmer with reliable storage, established buyers and a strong repayment history should not necessarily be assessed in the same way as one without those protections. But financiers can only make those differences meaningful when the supporting evidence is available.

This is where better data can materially improve the quality of agricultural risk assessment.

Historical yield data, rainfall and temperature patterns, soil and land information, satellite imagery, repayment behaviour, commodity price trends, warehouse records, buyer contracts, logistics performance, insurance claims and climate exposure data can all provide a more complete view of the underlying risk.

Used well, this information can support more precise lending terms, better insurance pricing, more targeted guarantees and stronger climate finance structures. It can also help financiers identify which risks require mitigation and which are already being managed effectively.

Better data does not make agriculture inherently low-risk.

What it can do is prevent a broad perception of sector risk from replacing a more specific assessment of the farmer, crop, location, market arrangement and financing structure.

The more precisely agricultural risk can be understood, the more precisely it can be financed.

Scenario 5: Infrastructure and Project Finance

Image generated by Google Nano Banana 2 model on Magnific, ‘Infrastructure Finance: When better project evidence strengthens the case for long-term capital.’

Infrastructure finance depends on a different kind of evidence because the financing horizon is longer, the capital requirement is larger, and the risks are more complex.

For lenders and investors, the question is not simply whether a borrower can meet a near-term obligation. It is whether a project can generate sustainable value over many years and whether the risks around construction, demand, revenue, regulation, operations and repayment are sufficiently understood.

That makes the quality of project information critical.

Financiers need credible data on expected demand, projected revenues, tariff structures, construction costs, contractor performance, regulatory conditions, public-sector payment behaviour, environmental and climate exposure, operating performance and governance arrangements.

This is particularly important across African infrastructure markets, where financing needs remain substantial across power, transport, ports, logistics, digital infrastructure, water, housing, healthcare and industrial development.

Where the evidence base is weak, incomplete or difficult to verify, financiers have less confidence in the project’s assumptions and execution capacity. That uncertainty can translate into higher required returns, more stringent financing terms, additional guarantees, longer due diligence periods or, in some cases, a decision not to finance the project at all.

Better data can improve the quality of project preparation and reduce that uncertainty.

Reliable feasibility studies, demand data, revenue histories, geospatial information, climate data, procurement records, contractor performance, public payment histories, operational benchmarks and comparable project outcomes can all strengthen the basis on which a project is assessed.

This matters because a compelling project concept is not the same as a financeable project.

Capital providers need evidence that the assumptions behind the project are credible, that execution risks have been identified, and that expected revenues and repayment capacity can be supported by more than projections alone.

If Africa is to attract deeper pools of long-term capital, stronger project data and more consistent performance records will be essential. So will greater transparency around how projects are prepared, procured, delivered, monitored and paid for.

Better data will not make every infrastructure project bankable. But it can improve the quality of due diligence, reduce avoidable uncertainty and give stronger projects a better chance of being assessed on their actual fundamentals rather than on the gaps in the information available.

What Better Data can change

Image generated by Google Nano Banana 2 model on Magnific, ‘What better data can change: From broad risk assumptions to more precise financial decisions.’

Better data does not remove risk, but it can materially improve the quality of financial decision-making by giving lenders, investors and other capital providers a clearer basis for assessing it.

At the borrower level, stronger information can support more accurate credit assessment by helping financiers differentiate between genuinely weak borrowers and those whose risk profiles are simply poorly understood. It can also improve risk segmentation, allowing SMEs, farmers, traders, projects and public entities to be assessed on their specific characteristics rather than through broad assumptions that may not reflect their actual circumstances.

Better data can also strengthen collateral assessment. More reliable asset registries, ownership records and valuation data make it easier to establish what collateral exists, who owns it and how much it is worth. Similarly, stronger transaction and performance data can improve post-disbursement monitoring, enabling lenders to identify changes in repayment capacity or operating performance earlier rather than relying predominantly on information collected at origination.

The benefits extend to risk-sharing mechanisms. Guarantees, credit enhancement structures and insurance programmes can be designed more effectively when institutions have better evidence on defaults, recoveries, claims, losses and portfolio performance. This makes it possible to target support more precisely rather than applying the same risk-sharing approach across very different borrowers or sectors.

Better information can also reduce the time and effort required to reach a financing decision. Where key information is readily available, current and verifiable, fewer resources need to be spent reconstructing borrower histories, validating records or reconciling conflicting information. This can shorten due diligence and approval cycles while improving the quality of the decision itself.

Most importantly, stronger evidence can support more accurate pricing. When financiers can understand the underlying risk with greater confidence, there is less need to compensate for uncertainty through broad premiums, tighter terms or excessive risk buffers.

The same principle applies beyond individual lending decisions. Capital markets depend on credible disclosure, consistent reporting and comparable information. Governments and public institutions similarly strengthen their credibility when investors can access reliable data on fiscal performance, debt, revenue, project delivery and institutional outcomes.

This is why data should be understood as part of the financial infrastructure of a market.

It influences how risk is assessed, how capital is allocated, what financing terms are offered and ultimately which businesses, projects and sectors are able to attract funding.

Better data does not determine where capital must go. It gives capital providers a stronger basis for deciding where it can go, on what terms and at what price.

The Limits of Better Data

Image generated by Google Nano Banana 2 model on Magnific, ‘Where Data ends and structural risk begins: Better information cannot replace strong economic fundamentals.’

Better data can improve how risk is understood and priced, but it does not remove the underlying economic and institutional conditions that create risk in the first place.

It cannot by itself resolve inflation, currency depreciation, political instability, weak contract enforcement, poor governance, shallow capital markets or genuine default risk. Nor can stronger information make an unviable project viable or persuade investors to overlook deteriorating macroeconomic fundamentals.

That is an important qualification.

The case for better data should not be overstated. Information quality is one part of the financing environment, not a substitute for sound economic policy, institutional strength, legal certainty or credible governance.

Better data improves the quality of the evidence available to assess risk. It allows lenders to evaluate borrowers more precisely, investors to compare opportunities more confidently, insurers to underwrite more accurately, and policymakers to demonstrate performance with greater credibility.

In other words, better data does not eliminate structural risk. It reduces the additional uncertainty created when that risk cannot be clearly observed or verified.

That matters because the two can become intertwined in pricing. A genuinely risky environment may justify a higher cost of capital. But weak information can add another layer of caution on top of that risk, making financing more expensive than the underlying fundamentals alone would require.

Better data cannot remove the risks that exist. It can, however, reduce the additional cost created by not being able to assess those risks with sufficient confidence.

Building the Data Foundations for more accurate Risk Pricing

Image generated by Google Nano Banana 2 model on Magnific, ‘Building the Data Foundations for Better Risk Pricing: From Fragmented Records to Trusted Financial Evidence.’

If better data is going to influence the cost of capital, the answer cannot be a collection of isolated digital initiatives. Africa needs stronger financial data infrastructure: systems that make reliable information easier to capture, verify, connect and use across the financing ecosystem.

That starts with stronger borrower data. SMEs, cooperatives, traders, corporates, project sponsors and public entities need better ways to build reliable financial histories over time. This includes stronger accounting records, digital transaction histories, credit histories, invoice data, repayment behaviour and, where appropriate, responsibly governed alternative data.

It also requires better public registries. Business identity, ownership, collateral, land, licensing, tax, procurement, legal status and asset records all influence how easily a financier can verify a borrower or transaction. Where these registries are incomplete, outdated or disconnected, the consequences show up in slower due diligence, weaker collateral assessment and higher verification costs.

A third requirement is transaction-level visibility. Payments, invoices, trade flows, supply-chain activity, repayment patterns, claims behaviour and customer relationships all provide useful signals. But those signals only become valuable when they are structured, accessible, governed and capable of being verified across institutions.

Africa also needs stronger sector-level datasets. Agriculture, trade, logistics, energy, manufacturing, healthcare, housing and infrastructure all carry different risk profiles. Better sector data allows financiers to move away from broad assumptions and assess opportunities with more precision.

The credit ecosystem itself also needs to deepen. Credit bureaus, digital identity, open finance frameworks, consent-based data access, collateral registries and stronger credit assessment systems all help create a more complete view of borrowers and improve the reliability of financial decision-making.

There is also a need for better data around guarantees and risk-sharing mechanisms. Development finance institutions, insurers, banks and public guarantee schemes need clearer evidence on defaults, recoveries, claims, losses and portfolio performance. Without that information, guarantees can become too broad or poorly targeted, rather than being used strategically to address specific financing gaps.

For infrastructure and public investment, project performance data is equally important. Better records on project preparation, execution, delays, cost overruns, demand, revenues, contractor performance and operational outcomes can strengthen due diligence and help investors compare opportunities more confidently. And none of this works without governance and trust.

Data used in financial decision-making must be accurate, secure, consent-based, interoperable, auditable and responsibly governed. More data is not automatically better data. Poor-quality or poorly governed information can create new risks, distort decisions and undermine confidence in the very systems meant to improve financing.

The objective is not to generate more financial data, it is to build an environment in which reliable evidence can move through the financial system with enough quality and trust to support better risk assessment, stronger decision-making and more accurate pricing.

The Strategic Case for Better Financial Data

Image generated by Google Nano Banana 2 model on Magnific, ‘The Strategic Case for Better Financial Data: Pricing African Opportunity with Stronger Evidence.’

The conversation about financing Africa often centres on the need for more capital. That need is real. The continent requires deeper pools of long-term finance, stronger SME lending, more trade finance, greater infrastructure investment, more climate finance and broader participation in productive sectors. But the availability of capital is only part of the equation.

Capital also depends on the quality of the evidence available to assess opportunity and risk. Investors and lenders need to understand who they are financing, what the underlying economics look like, how risks are distributed, what protections exist, and how confidently expected returns can be assessed.

This is why data matters.

Africa does not only need more financing. It also needs stronger evidence around the businesses, projects, transactions and markets seeking that finance.

A viable SME should not appear weaker simply because its records are fragmented. A legitimate trader should not attract a higher risk premium because documentation sits across disconnected systems. A commercially sound infrastructure project should not become more expensive because performance data is limited. A productive farmer should not be treated as broadly high-risk because localised agricultural data is weak. And a country should not face additional scepticism because institutional information is incomplete, delayed or difficult to verify.

Better data will not remove the risks that exist in African markets. Nor should it be presented as though it can.

What it can do is make those risks easier to identify, assess and differentiate.

That matters because when reliable evidence is limited, financing terms often include an additional margin for uncertainty. Stronger information can reduce that uncertainty and give capital providers a firmer basis for pricing the underlying risk itself.

The strategic case for better financial data is therefore not simply about making capital cheaper.

It is about improving the quality of financial judgement.

And where risk can be assessed with greater confidence, capital can be allocated more efficiently, financing structures can be more appropriate, and pricing can more closely reflect the underlying economics of the opportunity.

Better data will not eliminate Africa’s risk premium. But it can help ensure that African businesses, projects and markets are not also paying an avoidable premium for what the financial system cannot see clearly enough.