McKinsey Global Institute

Financial data unbound: The value of open data for individuals and institutions

Economies that embrace data sharing for finance could see GDP gains of between 1 and 5 percent by 2030, with benefits flowing to consumers and financial institutions.

As countries around the world look to ensure rapid recovery once the COVID-19 crisis abates, improved financial services are emerging as a key element to boost growth, raise economic efficiency, and lift productivity. Robust digital financial infrastructure proved its worth during the crisis, helping governments cushion people and businesses from the economic shock of the pandemic. The next frontier is to create an open-data ecosystem for finance.

Already, technological, regulatory, and competitive forces are moving markets toward easier and safer financial data sharing. Open-data initiatives are springing up globally, including the United Kingdom’s Open Banking Implementation Entity, the European Union’s second payment services directive, Australia’s new consumer protection laws, Brazil’s drafting of open data guidelines, and Nigeria’s new Open Technology Foundation (Open Banking Nigeria). In the United States, the Consumer Financial Protection Bureau aims to facilitate a consumer-authorized data-sharing market, while the Financial Data Exchange consortium attempts to promote common, interoperable standards for secure access to financial data. Yet, even as many countries put in place stronger digital financial infrastructure and data-sharing mechanisms, COVID-19 has exposed limitations and gaps in their reach, a theme we explored in earlier research.

This discussion paper from the McKinsey Global Institute (download full text in 36-page PDF) looks at the potential value that could be created—and the key issues that will need to be addressed—by the adoption of open data for finance. We focus on four regions: the European Union, India, the United Kingdom, and the United States.

By open data, we mean the ability to share financial data through a digital ecosystem in a manner that requires limited effort or manipulation. Advantages include more accurate credit risk evaluation and risk-based pricing, improved workforce allocation, better product delivery and customer service, and stronger fraud protection.

Our analysis suggests that the boost to the economy from broad adoption of open-data ecosystems could range from about 1 to 1.5 percent of GDP in 2030 in the European Union, the United Kingdom, and the United States, to as much as 4 to 5 percent in India. All market participants benefit, be they institutions or consumers—either individuals or micro-, small-, and medium-sized enterprises (MSMEs)—albeit to varying degrees.

Capturing the full value requires both a level of data standardization and a breadth of data sharing that are not yet current in many economies. Indeed, our research suggests that more than half the potential value remains inaccessible, particularly the value that financial institutions could gain directly through greater efficiency and reduced fraud costs. The use of open data raises questions about user consent, data protection, and cybersecurity. But if these issues are addressed, the innovation such ecosystems could enable would be a spur to economic recovery and broader-based prosperity.

Open financial data create economic value across a wide range of interactions between consumers and financial institutions

Financial data are created or used throughout the life cycle of financial services. They accompany every step of the consumer journey and are used in every action taken by financial institutions as they engage with customers.

Our research identifies seven broad mechanisms through which open financial data can create economic value. Three directly benefit individual and MSME customers: increased access to financial services, greater user convenience, and improved product options. The other four mechanisms directly benefit financial institutions: increased operational efficiency, better fraud protection, improved workforce allocation, and reduced friction in data intermediation.

To size the overall economic value that open financial data can create, we quantify the potential of 24 use cases in banking and payments. We then scale up from these use cases to develop a broader view of the macroeconomic gains.

For consumers in the financial services life cycle, benefits include the following:

  • Increased access to financial services. Data sharing enables customers to buy and use financial services to which they might not otherwise have access. For example, where limited data from traditional documentary sources may disqualify consumers from accessing loans, open financial data can help assess the creditworthiness of borrowers by sourcing rent, phone, and utility bills. Individuals and MSMEs with thin files or no formal records can gain access to formal credit, often for the first time. One Experian study showed that including utility data allowed 20 percent of customers with scant documentation in support of their credit application to move on to become “thick-file” customers. Scaling such gains to an economy-wide level, we find that increased access to credit using alternative data could raise an economy’s credit-to-GDP ratio by 20 basis points in the United States and the European Union. In India, the lift could be as much as 130 basis points, the equivalent of about $80 billion to $90 billion in GDP by 2030.
  • Greater user convenience. Data sharing saves time for customers in their interactions with their financial services provider. MSMEs can provide documentation faster during customer onboarding, for example. Open access to data on available mortgage products, with applications automatically prefilled, allows consumers to apply for loans without needing to use mortgage brokers. This not only eases the process but also enables customers to benefit from the best rates. In the United Kingdom, which introduced its Open Banking system in 2018, startups use open-banking data to enable quick and easy mortgage applications to all participating providers for free, unlike traditional mortgage brokers who charge arrangement fees.
  • Improved product options. Open financial data can broaden and improve the range of product options available to customers, saving them money. For example, an open-data ecosystem makes it easier to switch accounts from one institution to another, helping retail and MSME customers achieve the best yield.

For financial institutions, benefits span the entire life cycle and include the following:

  • Increased operational efficiency. Since most data are still found in physical documents or disparate digitized sources, open financial data could cut costs and make it easier to adopt automation technologies, with the associated efficiency boost. This can improve experience for customers by promoting faster and more transparent interactions with providers. In India, for example, the use of the national digital identification system, Aadhaar, for “know your customer” (KYC) verification of retail consumers reportedly reduced costs for financial institutions from about $5 per customer to $0.70. For mortgage underwriting, sharing borrowers’ data allows standard mortgages to go through automated underwriting. Financial data sharing also helps avoid multiple manual data handoffs that lead to errors, rework, and less efficient outcomes. It significantly reduces the costs associated with remediating bad customer relationship management data, currently estimated at 20 percent of a typical financial institution’s income.
  • Better fraud prediction. The Association of Certified Fraud Examiners estimates total fraud (including but also extending beyond financial services) at more than $4.5 trillion annually, or the equivalent of about 5 percent of global corporate revenue. Fraud in financial services takes multiple guises, including synthetic and traditional ID fraud, payments fraud, and credit application fraud. Real-time access to customer data can support advanced techniques to identify and reduce costs related to these and other types of fraud. Sharing data provides more evidence and clues with which to flag suspicious activity. This helps institutions build out their predictive modeling of fraud and catch cases earlier. Cifas, a not-for-profit fraud prevention organization in the United Kingdom, says that its members reported more than 350,000 cases of fraud in 2019, preventing fraud totaling £1.5 billion.
  • Improved workforce allocation. Companies can use open data to better allocate and target their workforce, assigning staff to the highest-value activities. This helps them better focus their calls on high-risk customers, reduce the time spent monitoring credit of low-risk customers, and ultimately recover more debt.
  • Reduced friction from data intermediation. This mechanism is most relevant to financial institutions before they have direct knowledge of a prospective customer, such as in lead generation or loan origination, and so look to acquire and use data from third-party providers. The missing details can range from basic identification data to more behavioral information. Open data systems enable direct access to data by using application programming interfaces (APIs) for data intermediation, which reduces friction. In the United States, for example, where nearly half of all mortgage providers rely on third-party data for mortgage origination, such as credit data, KYC data, and property valuation data. These data can cost as much as $80 per mortgage application, but with open data for finance, much of this information is becoming more publicly available.

Exhibit 1 demonstrates the potential economic gains by region from an example use case linked to each of the seven mechanisms for creating value. The bubbles in blue represent the largest potential gain.

Open financial data ecosystems can scale to create significant potential economic gains.
We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

The potential value of open financial data ranges from 1 percent to as much as 5 percent of GDP, depending on economic structure and levels of financial access

Aggregating the potential GDP impact across the 24 use cases to the economy level, we find significant value at stake overall and for all market participants. The total potential GDP impact from open financial data in 2030 is highest for India, at 4 to 5 percent. We estimate the impact for the European Union, the United Kingdom, and the United States to be between 1 and 1.5 percent of GDP (Exhibit 2).

The potential GDP impact of open financial data and the share accruing for different market participants varies by region.
We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

The differences are the result of several factors, typically structural features of economies. Notably, emerging economies tend to have lower levels of financial access and less financial depth, which means the lift in value creation they could achieve with open data is large. India has significant unmet need for retail and MSME credit. It thus has higher economic growth potential for every unit of physical capital added, because open financial data improves credit access.

MSMEs also benefit from time saved in opening accounts; this would translate into higher GDP, assuming the time saved was deployed in market-based economic activities rather than, say, leisure. In India, the time saving accruing to MSMEs could make them the largest beneficiary segment, with more than 60 percent of the potential economic value from our use cases accruing to them.

In the United Kingdom and the United States, by comparison, while we estimate that individuals would capture the largest share of value, financial institutions also have sizable value at stake. In the European Union, financial institutions would gain the largest share of value, nearly 45 percent of the total.

For each region, we find differences in the share of GDP impact in each major step in the financial services life cycle and for each of the seven mechanisms.

India stands out, with a greater share of economic value, about 75 percent of the total, coming from the decisioning and onboarding component of the life cycle, particularly linked to increased access to financial services. This is due to the large potential of opening access to credit to currently excluded individuals and MSMEs. Greater user convenience from simplified onboarding processes for new customers, particularly MSMEs, also saves meaningful amounts of time.

In the European Union, the United Kingdom and the United States, the share of economic value from decisioning and onboarding is lower than that in India, while the share from servicing and monitoring is higher, ranging between 35 and 45 percent of the total. In the United States, the value at stake in relation to decisioning and onboarding is higher than in the European Union because of the size of the credit gap for the country’s many MSMEs.

The main difference between the European Union and the United Kingdom is in the breakdown of potential value associated with servicing and monitoring. In the United Kingdom, we estimate nearly half of this value could flow from improved product options, driven by the potential for MSMEs to increase their deposit yields through easier account switching. In the European Union, where the average MSME is less than half the size of those in the United Kingdom and total MSME savings are lower, the relative potential value coming from account switching is smaller.

Capturing the full potential value of open data for finance requires data standardization and breadth of data sharing

The value creation mechanisms require varying levels of data standardization and breadth of data sharing for their potential to be captured (Exhibit 3).

Capturing the
We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

By standardization, we mean the extent to which standardized mechanisms exist for sharing data and the associated cost of access. In some use cases, data sharing occurs only through ad hoc means. For example, consumers wanting to receive automated access to competitive mortgages need to provide the same specific mortgage application data to multiple providers. To operate at scale, other data-sharing use cases require data to be sourced easily through standardized APIs at minimal cost.

Breadth refers to how broadly data are shared and the mechanisms in place that drive that data sharing. Some use cases work when individuals can request specific data to be shared on an ad hoc basis. For example, consumers can benefit from faster mortgage closure when they are able to grant their prospective lender on-off access to the required data. To operate at scale, other use cases require data sharing over time across a wide range of types of financial data, albeit with consumer consent. An example of an ecosystem with broad data sharing is India, where banks must share all consumer data including personal nonfinancial and financial data at the request of consumers via private but highly standardized APIs developed on a publicly built technology ecosystem known as IndiaStack. Consumers can choose to share their data with digital nonbank lenders via an app to secure loans.

From our research, we see that consumers (both individuals and MSMEs) require moderate levels of standardization and breadth of data sharing to reap the benefits of open data. Institutions, by contrast, could only access full benefits when the level of standardization and breadth of data sharing are higher.

Economies vary in their current levels of data standardization and breadth of data sharing

The type of data-sharing ecosystem used in an economy depends on multiple factors including local market conditions, the robustness of existing digital financial infrastructure, and regulation, including consumer protection laws and mechanisms. In the four economic regions on which we focus, we find significant differences in the degree of standardization and breadth of data sharing (Exhibit 4).

Economies have
We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

In the European Union and the United Kingdom, for example, we find a high level of standardization combined with relatively less breadth of data sharing. In such data ecosystems, a limited subset of financial data is available via highly standardized and regulated APIs. The data are accessible and usable for third-party service providers such as financial technology firms and other banks. For example, in the United Kingdom, APIs are mandatory for most account and direct debit data, such as address, beneficiary, and payment time stamp. Access to data outside of the subset is more restricted and at the discretion of each individual financial services provider.

In the European Union, the second payment services directive stipulates that data specific to payments be shared via highly standardized APIs. At the same time, separate data privacy regulations restrict the sharing, harvesting, and use of other financial and nonfinancial data, including, for instance, screen scraping, a prevalent form of data gathering in the United States.

For its part, the United States has a broad range of data sharing, but data standardization is more limited. Financial data aggregators broker data flow between providers and users, with limited consumer control. This is primarily because of a lack of strong federal regulation regarding data privacy and a private-market approach to data sharing. This has made private data aggregators the de facto standard setters for how data are shared.

In India, a relatively broad range of data is shared and there is some degree of standardization. Private APIs in India are built by licensed data aggregators on IndiaStack. Data aggregators broker data sharing across institutions using this standard, and consumers can view the data and share directly with payment systems players.

Only low to moderate levels of economic value are accessible with today’s levels of data standardization and data-sharing breadth

A country’s level of data standardization and breadth of data sharing sets the potential value from open financial data it might access today. Exhibit 5 indicates the potential value the European Union, the United Kingdom, the United States, and India could derive from open data, and their current level of standardization and breadth of data sharing. (Click on the country name to change view).

Exhibit 5

In the European Union, the United Kingdom, and the United States, current data ecosystems leave much of the potential value at stake inaccessible.

Both the United States and the European Union can currently capture only a small fraction of the potential value from open financial data—less than 10 percent, in our estimate. In the United States, the constraint is lack of standardization, while in the European Union it is limited breadth of data sharing. In the United Kingdom, somewhat more value—we estimate between 30 and 40 percent—is currently accessible.

India is better poised to capture value today. Its open data environment positions it to access between 60 and 70 percent of the potential value that open financial data could offer, provided other enablers are in place, as we describe in the next section.

Navigating risk and implementation challenges

Capturing the value from open financial data requires more than sufficient data standardization and breadth of data sharing: users—both consumers and providers—must trust the system, and infrastructure is needed to support it. These factors together provide the groundwork for potential economic gains from open financial data, both those which we size and those that future innovation may make possible.

Well-founded trust. An open-data ecosystem can function effectively only by achieving a level of well-founded trust among all the participants. Without this, market participants—whether individual consumers or businesses—may opt out. Financial data are particularly sensitive, and users are more likely to want to share data if they know what they are sharing and why that sharing is valuable to them. Strong consumer financial protections are also necessary to prevent financial discrimination and foster trust. Absent an automatic or easy mechanism for correction of data errors and updating to reflect changes to life circumstances, problematic data might block an individual or MSME from accessing a needed financial product at a fair price. User trust is also encouraged when threats to cybersecurity are anticipated and mitigated. Breaches can occur during transfer of data, or at any institution involved in the open data ecosystem, such as a bank or fintech. For example, when data transfer is achieved via APIs, a hacker who breaches such an API can hijack any apps that use the interface to collect data. As a result, open APIs require strong customer authentication. Successful data ecosystems tend to have built-in safeguards to ensure privacy and security while giving users access to their personal data, decision rights over who else has access to that data, and transparency about who has accessed it.

Robust financial infrastructure. Financial accounts and digital payment channels, along with digital identification systems with broad population coverage, are critical structural features needed to harness the value of open financial data. High-assurance digital ID facilitates user control of data, privacy protections, security for online interactions, and reduces friction in managing online accounts. Open-data systems without high-assurance digital IDs could make it harder for consumers to keep track of their digital footprint or use their data securely and efficiently. At the same time, digital ID can help provide support strong customer authentication and help control against cyberattacks. For many emerging economies, basic internet access, smartphone penetration, and reliability of electricity are also prerequisites for capturing the full economic value of a data-sharing ecosystem. Shortfalls in these elements limit the value of open financial data that could be captured.

Looking ahead: the role of innovation in open data unbound

Capturing the value accessible today from open financial data will require innovation. At the same time, the more value becomes accessible, through increases in standardization, breadth, or both, the more the potential for innovation will grow, likely beyond use cases we can envisage today.

Market participants will need to develop and scale products and services that address specific use cases, including but not limited to the 24 major ones we profile in this research. That will entail identifying new business opportunities, designing customer value propositions, and scaling new business models across the financial services value chain.

Different types of innovators, from traditional banking incumbents to technology platform-based players and new fintech startups, could all play meaningful roles, focusing on their areas of strength and competitive advantage. The specific types of innovators would vary by market and depend on the structure of the financial data ecosystem. Expanding the boundaries of open-data enablement would make new types of use cases possible, fueling greater innovation and greater value capture.

Explore a career with us

Related Articles