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A Comprehensive Overview of Open Data with Use Cases: How Open Data Impacts Financial Institutions, Fintechs, and SMBs

The world of data is undergoing a fundamental shift. Open data, which refers to data that is available to anyone to use, share, and modify, is changing the experiences and capabilities available in the banking and financial services sector, and particularly the business customers they serve. 

As datasets previously kept under lock and key become increasingly accessible, the potential for innovation, partnerships, and personalized customer experiences is vast. The open data movement, spearheaded by the UK with increasing adoption in the US, represents a significant change in finance technology and how data is perceived and utilized, with far-reaching implications for financial institutions, fintechs, SMBs, and consumers.

Why is Open Data Important?

Broadly, open data encompasses a variety of sources beyond the financial sector, including non-financial businesses like healthcare, retail, government, and more. Open data enables the integration of financial and non-financial data, facilitating the creation of more innovative, efficient, and tailored financial products and services. 

Open data can create profound economic value by enabling new products, services, and business models. For instance, estimates of the value of open banking transactions worldwide—powered by the advent of open data—project growth of more than 500% between now and 2027, from $57 billion to $330 billion. 

 The Open Data Ecosystem: Key Players and Dynamics

The open data ecosystem is a network of interconnected participants. Each plays a crucial role in unlocking the potential of open data. 

As traditional stewards of consumer and business data, financial institutions are opening up their datasets through APIs. APIs allow third-party providers (TPPs)—particularly ISO 27001 certified TPPs like 9Spokes—to securely access, integrate, and leverage information and financial data. Fintechs, in turn, use these services to develop innovative solutions that address the evolving needs of consumers and small and medium-sized businesses (SMBs).

In this way, the open banking ecosystem creates a circular flow of data and value. End users, including consumers and businesses, engage with financial institutions and fintechs to access products and services. These organizations store the resulting financial data, and as the data stores grow, third-party providers step in to facilitate connectivity between their platforms and their APIs. This process enables consented (sometimes referred to as “permissioned”) visibility into the datasets, empowering the development of third-party apps, digital banking solutions, targeted products, and additional services.

Regulators also play a vital role in this ecosystem, setting the policy direction, regulatory requirements, and ground rules to maximize the benefits of open data while protecting consumer privacy and security. Their goal is to widen and accelerate the flow of financial data, fostering innovation and competition in the market.

The Open Data Value Chain: From Embedded Journeys to Transactional Rails

Open data has disrupted the traditional value chain in banking, giving rise to a more partnership-driven approach. The era of closed consolidation, where a handful of vertically integrated institutions dominated the market, is dissolving in the face of an open ecosystem standard. This new paradigm focuses on increased specialization and a layered value chain comprising embedded finance journeys, intelligent services, financial products, capabilities, and transactional rails.


The commoditization of banking and financial services, spurred by favorable market conditions, higher customer expectations, and regulatory support for open data policies, has made building new companies and developing digital product experiences easier than ever. As a result, the profitability equation for industry leaders has shifted towards optimizing the relationship between customer acquisition cost (CAC) and lifetime value (LTV). Businesses that reliably acquire customers at a cost lower than their lifetime value can invest more in sales and marketing to boost profitability. This principle remains valid irrespective of which category of the value chain the company lies in—even if it straddles multiple.


These insights were noticeable even in 2021, as the World Fintech Report of the same year accurately outlined the phased profitability journey fintech companies undergo today (seen in the graphic above). 

The open banking movement, powered by open data, is upending financial service industries. Fintechs, in particular, have taken center stage as a prominent source of innovation and disruption—in no small way spurred by the global financial crisis of the late 2000s

Today, nascent fintech companies first must strive for product-market fit in one (or more) of the five categories outlined at the start of this section (embedded finance journeys, intelligent services, financial products, capabilities, and transactional rails). Once achieved, the goal is to diversify and grow a loyal customer base. Building or joining existing ecosystems and increasing the surface area of products or services will be crucial as partnerships—not buyer-vendor relationships—become increasingly vital.

The financial solutions market continues to expand, and collaboration will eclipse competition as a business imperative. End users will gravitate towards finance services and finance products offering the best customer experience, supported by ecosystems of dozens or hundreds of firms working together.

The Market Potential of Digital Innovation

In 2023, the fintech sector faced challenges, including a venture capital (VC) slowdown, regulatory scrutiny, and macroeconomic pressures. Despite these issues, fintechs continued to innovate and find growth opportunities. 

In fact, despite the turbulence of 2023, annual fintech revenues were projected to grow more than sixfold from 2021 to 2030, reaching $1.7 trillion. Specifically, banking fintechs’ revenues—lending, payments, deposits, trading, and investments—are expected to grow from 4% to 13% market penetration (at a 22% CAGR) of banking revenue pools by 2030, representing a quarter of global banking valuations.

Such findings continue to drive optimism in the sector. By partnering with TPPs like 9Spokes, which introduce cost-effective operating methods through a more efficient infrastructure and simplified processes, SMBs, fintechs, and financial institutions can leverage data to identify and ease customer friction points. Such partnerships also empower organizations to deliver high-quality, service-focused digital experiences.

Using Open Data to Enhance Customer Targeting

Organizations can leverage open data to find the best customers for their campaigns, as open data enables fintechs, financial institutions, and SMBs to target their ideal customer profile (ICP).

Financial service providers can increase efficiency by using an open data platform serviced by a single API to identify and target current SMBs and customers who meet specific characteristics. This eliminates the need for manual input selections, identification processes, and direct app integrations.

An open data platform can provide a wide range of information and metrics from multiple inputs, such as apps used by the customer for accounting, banking, and sales. All available data is consented to, meaning customers must opt in or give permission before sharing their data. However, once acquired, this data allows for precise targeting of key customers.

Findings and processes can then be established for future decisions, ultimately resulting in improved customer segmentation to present products, services, and solutions in the future.


Open Data as a Public Good

Economists define a public good as non-rivalrous and non-excludable. The non-rivalrous property describes how consumption of the good does not diminish the quantity available to others; the non-excludable property describes how the good is accessible to anyone and that nobody can restrict its access.

The non-rival nature of data, enabled by cloud computing and simultaneous access, means that consumption by one party does not diminish the quantity available to others. The key differentiator will be the granularity of the data. While personally identifiable information will remain excludable due to privacy and ownership rights—its access and use requiring the acquisition of consumer consent—the ever-increasing pool of aggregated and anonymized datasets will become more accessible, shifting data closer to being a public good.

The other end of the spectrum is aggregated and anonymized datasets. These undergo the same rigors that data from a double-blinded clinical trial experience. Consent from these datasets is often captured en masse by a public institution or private enterprise. These usually manifest as public APIs and can be found on platforms like GitHub or RapidAPI

A few examples include Yahoo Finance API—which offers real-time low latency data for the stock market, cryptocurrencies, and currency exchange rates—and Google Books, which enables full-text searches, book information retrieval, viewability, and eBook availability.

Open Data as a Utility

While the case for using and seeing open data as a public good is strong, another likely outcome involves the perception that data will transform from an esoteric resource into a utility. After all, data already powers a significant part of our everyday experiences. 

Under this light, it’s not difficult to imagine that open data will follow a similar path to electricity or internet access. Both were highly valued and scarce resources in the early days, but have now become ubiquitous fixtures in everyday life.

In the earliest days of the internet (or ARPANET, as it was then known), globalized asynchronous online communication was a niche technology accessible only to the armed forces and academics. 

Investments in research and development drove costs down (thanks to key players like IBM, Microsoft, and Apple), setting the stage for Tim Berners-Lee’s invention of the internet, as we know it today, and mainstream adoption. 

As with any advancement, this technology encountered and caused issues, like the dotcom bubble, and ongoing challenges, such as the inequitable distribution of internet speed. However, the general narrative of internet access has shifted. Once considered a luxury, today, internet access is increasingly viewed as a fundamental human right. It has become a utility—an accepted and expected layer of work and life.

The advent and evolution of internet access offer strong comparisons to the open data movement of today, and we can already see evidence that it is following the same trajectory. 

Leveraging SMB Data: Opportunities for Lenders

Open data empowers lenders to better evaluate creditworthiness, tailor offerings to specific market segments, and accelerate service delivery while improving customer experiences.

Access to an SMB’s financial and performance datasets—alongside the ability to regularly parse these datasets and feed them into credit origination and monitoring engines—gives lenders using this technology a clear, competitive advantage. 

Linking a business’ financial and operational performance data seamlessly in credit applications will reduce the time-to-decision journey for lenders. If a loan is approved, the integration with the datasets can continue to feed the credit risk monitoring systems, moving lenders from reactive to proactive risk management.

But this isn't a "winner-take-all" situation. The market for SMB financing is fragmented. E-commerce versus brick-and-mortar, B2C versus B2B, selling services or selling goods—these are but a few dimensions of differentiation that would benefit from careful consideration by lenders, especially concerning which business datasets can best predict creditworthiness.

Example: E-commerce Businesses

Lenders can gain valuable insights by accessing various datasets through integrations with platforms like QuickBooks Online, Sage Business Accounting, and FreshBooks for accounting data; Shopify, Stripe, and Square for sales data; specific banks or open banking aggregators for banking data; Mailchimp, Facebook, and Instagram for marketing and social media data; and Talenox, ADP, and Gusto for HR and payroll data. 

This data-driven approach to credit experience, from application to origination to servicing, will vary for different business segments. And competition is expected to increase as more lenders explore augmenting their credit products with open datasets.

However, given the fragmented nature of the small business financing market, the focus should be on delivering the best experience to each market segment. As a trusted and ISO 27001 certified, third-party service provider, 9Spokes can help financial service organizations connect to their customers' tech stacks and access always-on data from major business application categories, enabling them to build end-to-end customer experiences that improve brand stickiness and business banking bottom-line.

Harnessing Open Data for Enhanced Decisions, Continuous Monitoring, and Risk Management

Permissioned or consented data can accelerate decision engines, particularly useful for industries like insurance and financial services. The value of accelerated and transparent decisions, enabled by open data, broadly falls into two buckets: speedier service delivery paired with a better customer experience and the diversification of data sources that feed a decision engine to minimize risk exposure.

What Is the Role of Decision Engines?

Decision processes drive three primary goals:

  1. Qualify customers for their suitability to a product or service at speed
  2. Accelerate service delivery and improve the customer experience
  3. Minimize the risk a business faces by diversifying the data sources feeding a decision engine

How Does Open Data Power Decision Engines?

A key tenet of open data is the connectivity between different datasets hosted in different places. If a business wants to source specific datasets to feed into their decision engine, there are two steps they need to take:

  1. Ensure the connectivity of the dataset with the decision engine.
  2. Gain the consent from customers to use their datasets as part of the decision process.

Using open data creates a seamless experience for customers, manifested as a fast, checkbox-type interaction in most digital experiences. The result is that high-quality data is sourced in real time from customers’ data platforms and business apps to feed into decision engines.

Business models between the different players in the service sector—such as retail, banks, computer services, recreation, media, communications, and more—will naturally differ. For example, a life insurance provider can minimize risk by feeding diversified datasets into a model. Yet, pulling specific or relevant customer data on a case-by-case basis is equally important to provide personalized customer service.  

And even between similar businesses, data requirements may vary. For example, the underwriting models between life insurance providers are not all the same, resulting in different requirements for various insurance applications.

Open Data-Powered Decision Engines in Banking and Financial Services

Decision-making with speed and high accuracy has become a fundamental requirement for banking and financial services, especially as customer expectations evolve. A decision usually occurs at the point of application for a financial product. 

The typical case is either a loan application is rejected or approved. Assuming the latter occurs, a loan agreement is drawn up with the service provider, and funds are transferred to the customer. What follows is often a passive loan monitoring program that involves manual checks and ad-hoc follow-ups to ensure the banks' investment is performing as expected. In this model, the customers' ability to repay is only assessed once, when they apply. 

With open data, however, financial institutions can dynamically assess their portfolio risk on an ongoing basis and adjust if needed.

In 2018, Brex, a provider of business credit cards and cash management software, entered and disrupted the market by introducing a dynamic underwriting model. Brex assessed the risk against its portfolio of business credit card holders every single day by leveraging real-time data. What this means for Brex customers is that the interest rate on their credit card reflects the projected financial health of their business with predictive analytics—determined by Brex’s underwriting model. If key metrics or ratios trend negatively, the next day's assessment will reflect it.

In contrast, other providers of business credit cards don’t reassess their portfolio risk daily, and therefore, incur higher risk. Brex’s model is made possible by open data—such as bank APIs that connect to current account feeds and transaction history. 

What Is the Role of Continuous Monitoring?

Permissioned data feeds enable continuous monitoring, which allows businesses to personalize engagement and mitigate financial and regulatory risks. Continuously monitoring customers and business partners serves two primary goals:

  1. Deliver greater value by personalizing engagement.
  2. Minimize the financial and regulatory risk businesses face when conducting normal business operations.

How Does Open Data and Consented Data Empower Continuous Monitoring?

Consumers and businesses store their data across various digital and non-digital services. The last decade saw the digitization of previously offline records, and so the proportion of non-digital data stores has significantly decreased. Open data is the key to connecting varying datasets not only to each other, but also to entirely new services in development. 

Businesses seeking to continuously monitor their customers' financial health must gain consent to connect specific datasets into their systems. This method specifically filters the datasets for information the organization cares about to unlock an ongoing view of any consenting customer's business and financial health.

In the banking and financial services sector, regular customer data monitoring can lead to proactive risk management and flexible loan services. For example, lenders can dynamically assess their portfolio risk and adjust interest rates based on a borrower's evolving financial health, as demonstrated by companies like Brex.

Open Data’s Impact on Onboarding Processes

An onboarding experience serves two primary purposes:

  1. Accelerate the time-to-value for the customer (the time it takes from when a customer purchases a product or service, to when they start deriving value).
  2. Minimize the risk businesses face when setting up new customers.

Onboarding experiences help increase the velocity of a customer’s acquisition, thereby reducing a business's churn risk. The faster a customer is convinced that the product or service is right for them—and the smoother the set up is—the less likely they are to look elsewhere.

Onboarding experiences powered by open data further streamline customer acquisition and reduce churn risk. By leveraging consented access to customer data during the application process, financial institutions can accelerate time-to-decision, reduce the risks they face, provide a seamless experience, and minimize manual intervention. At the same time, customers will onboard faster and make more confident, data-driven decisions with greater ease. As open data infrastructure matures, smooth and swift onboarding processes will become ubiquitous in the competitive lending market.

For example, if a small business applies for a term loan at a bank, they’ll need to provide financial (and perhaps other) datasets depending on what inputs the lender uses in their risk models. Open data enables the loan applicant to connect their business bank account, accounting software, POS system, and other systems in the finance stack, which is used natively as part of the application process. When this process is powered by open data feeds, the experience is fast and seamless. 

Consider the alternative, where an applicant must export the same information from each system and then email it to the lender. Leveraging open, consented data eliminates the need for manual intervention and allows a faster, automated decision process.

The Future of Open Data Utilization

As the open data movement gains momentum, it is clear that the future of data utilization lies in the ability to harness consented customer data to drive innovation, personalization, and risk management. Financial institutions and fintechs that can effectively leverage open data to deliver superior user experiences and evolving SMB customer needs will emerge as market leaders.

Open data is reshaping financial and non-financial industries, creating new opportunities for collaboration, customer service, and innovation. And as the amount of data we generate grows exponentially—180 zettabytes by 2025—striking the right balance between accessibility and protection will be crucial.

The open data movement is poised to reshape and redefine how we interact with data in our daily lives. As we navigate this new era, embracing the power of open data while prioritizing privacy and security will be the key to unlocking its full potential for businesses and consumers alike.