10.2.1 The Monopolization of Financial Data

Community governance of data is becoming an imperative. Community members are increasingly demanding not only protection of their interests, notably privacy, voice, and profit-sharing associated with data commercialization, but also data sovereignty. Data, from this perspective, should benefit not only companies that often provide the infrastructure for data collection and management but also all stakeholders involved and the public (Shkabatur Reference Shkabatur2019, 383). Such demands are growing, as current privacy laws are failing to provide significant protection of data subjects’ rights, largely due to information asymmetries, rationality failures, and market power in data markets (Lancieri Reference Lancieri2022), not to mention concentrated control over data infrastructures. These problems are compounded by poor enforcement that relies on consumers’ abilities to bring complaints and is often underfunded (Houser and Bagby Reference Houser and Bagby2023, 127). Furthermore, the environmental cost of data governance imposed on community members and society at large provides additional reasons for the collective governance of data as shared resource. Data governance, including the use of financial technology, requires increasing consumption of energy and water, which worsens the ongoing climate crisis, affecting community members and the public in general. Recognizing this problem further warrants not only community members’ entitlement to the benefits of data but also to participating in its collective governance.

Applying the GKC framework to financial data and fintech reveals several critical considerations. The community involved in creating, collecting, managing, and using shared financial data includes financial companies, investors, creditors, financial consumers, local communities, regulators, and other stakeholders. While these community members appear to somewhat participate in the collective governance of financial data, their involvement in financial data commons is limited in practice. The nature of the resource is such that financial data is non-rivalrous but excludable (Jones and Tonetti Reference Jones and Tonetti2020; Purtova and van Maanen Reference Purtova and van Maanen2024). While it can be duplicated and shared at minimal marginal cost, access can be restricted through technical and legal measures, as well as by social norms and practices (Frischmann et al. Reference Frischmann, Madison, Strandburg, Frischmann, Madison and Strandburg2014, 1–2). And notable power imbalances and information asymmetries exist among community members.

Specifically, financial and fintech companies are increasingly monopolizing the collection, storage, use, and commercialization of financial data to the detriment of other members of the governing community (McIntosh Reference McIntosh2019; Chakravortl Reference Chakravortl2021; Houser and Bagby Reference Houser and Bagby2023). This situation gives rise to a critical social dilemma. Thanks to their control of financial technologies or infrastructures, financial companies absorb ever-growing amounts of financial data, while relying on formal and informal rules to largely exclude data subjects – notably small investors, creditors, and companies as well as financial consumers – from its governance (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 268, 278; Fisher and Streinz Reference Fisher and Streinz2022, 832). Consumer data is overshared with some and undershared with others (Houser and Bagby Reference Houser and Bagby2023, 116). Excluded community members have limited influence over how their data is collected, managed, and used, and are often excluded from governance (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 268). This can be viewed as data subjects’ involuntary contribution, which questions the legitimacy of data commons (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 285–86). Interactions in this context are predominantly transactional, with, for instance, consumers exchanging data for services with minimal transparency or collective decision-making regarding data practices. Such practices by data oligopolies are often driven by commercial interests rather than public benefit. The primary benefits from financial data exploitation accrue mainly to financial and fintech companies, whereas several risks and harms fall on powerless community members such as consumers. While financial data is produced by all, it is largely under private control and mainly serves to maximize the benefits of private data controllers (Fan Reference Fan2021, 1444), sometimes to the detriment of data subjects (MacCarthy Reference MacCarthy2011; Houser and Bagby Reference Houser and Bagby2023, 115). For instance, financial companies may use collected financial data to facilitate predatory and discriminatory lending practices.

These data oligopoly practices undermine the potential for a community governance of financial data. Concentrations of power prevent community governance, as the political economy of commons suggests (Madison Reference Madison2024, 323–24). The GKC framework can thus reveal both the oligopolistic tendencies of financial companies and the need to heed community members’ neglected interests and voices. Otherwise, data commons governance may be deemed ineffective and illegitimate (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 268, 283). Furthermore, financial data oligopolies can erode the attainment of outcomes (Martens Reference Martens2018; Houser and Bagby Reference Houser and Bagby2023, 116) that data commons can favor and that community members may value. These include preventing privacy harms, cybersecurity risks, and financial exclusion and exploitation; expanding access to credit and funding; sharing profits from data commercialization; stimulating financial innovation and enabling collaborative development of solutions; enhancing financial competition and facilitating the rise of new competitors; and reducing barriers to financial data access.

The nature of financial data as a shared resource, community members’ involvement in financial data production and dissemination, and the desired outcomes may call for the need to both address the financial data oligopoly problem and seek the building of effective and legitimate community governance of financial data commons. Barriers to implementation include resistance from financial companies prioritizing profit maximization and proprietary technological platforms that limit community members’ participation in financial data governance. Among other things, it seems imperative to encourage the development of rules that may facilitate the effective involvement of community members in creating financial data commons.

10.2.2 The Promise of Consumer Data Trusts

The activism of community members may play a crucial role in developing financial data commons, challenging the dominance of data oligopolies, and implementing a community governance of financial data. Specifically, financial consumer groups, as one important community member, can actively foster a collective governance of financial data. These groups can represent consumers or evolve into consumer data trusts (e.g., Zarkadakis Reference Zarkadakis2020), serving the interests of consumer data subjects. Data trusts can be defined as governance devices that place an independent fiduciary intermediary between companies and data subjects (Houser and Bagby Reference Houser and Bagby2023). They can be viewed as a form of data commons (Scassa Reference Scassa2020; Madison Reference Madison2024, 328), wherein consumers pool their data, assert their voice as data subjects, and govern it as shared resource. In this capacity, these trusts can play a crucial role in overseeing financial data oligopolies (Delacroix and Lawrence Reference Sylvie and Lawrence2019; Houser and Bagby Reference Houser and Bagby2023), thus revealing the polycentricity of financial data governance (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 270). These contestation efforts can lead to the development of more legitimate new rules-in-use (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 281).

By aggregating consumer data rights and enhancing collective bargaining power, consumer data trusts and trustees acting on behalf of consumers can negotiate more equitable terms, enforce robust privacy safeguards (Delacroix and Lawrence Reference Sylvie and Lawrence2019; Blankertz Reference Blankertz2020; Ruhaak Reference Ruhaak2021; Fisher and Streinz Reference Fisher and Streinz2022, 835; Houser and Bagby Reference Houser and Bagby2023, 140, 144, 152), and ensure a fair distribution of the economic value derived from financial data. Key features of consumer data trusts may include pooled data rights, democratic governance, professional management, and stringent privacy and ethical standards. Taken together, these elements offer greater negotiating power, stricter data protections, and a platform for collaborative decision-making on data policies, including efforts to attain valuable outcomes (Houser and Bagby Reference Houser and Bagby2023, 174). The emergence of such trusts represents a paradigm shift in data governance, addressing the limitations of individual consent models (MacCarthy Reference MacCarthy2011; Delacroix and Lawrence Reference Sylvie and Lawrence2019) and potentially rebalancing power dynamics between individual consumers and data oligopolies (Van Geuns and Brandusescu Reference Van Geuns and Brandusescu2020; Houser and Bagby Reference Houser and Bagby2023, 140). This model not only tackles immediate concerns about data privacy and value distribution but also lays the groundwork for a more participatory and equitable data commons.

Consumer data trusts may be enabled by formal rules and in turn help develop and implement informal rules to pursue outcomes that consumers deem valuable, including social norms, strategies, and practices seeking to protect consumers’ interests and to undo data oligopolies’ rules that are not conducive to building data commons. This combination of data subjects’ participation and voice with legal regulation may address the governance failures of illegitimate and socially problematic commons governance (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 283). For instance, granting a consumer association a right to bring a representative action, utilizing consumers’ right to an explanation of companies’ use of data or financial algorithms or to data portability, and embracing emerging open banking laws and policies legally empower these associations to oversee data oligopolies, including preventing discriminatory or exploitative lending practices (Anidjar and Mizrahi-Borohovich Reference Anidjar and Mizrahi-Borohovich2019, 201), and help build a community governance of data commons. By consolidating the data rights of numerous consumers and enhancing their collective bargaining power, consumer data trusts can negotiate with financial companies to oversee the development and implementation of new informal and formal rules. These rules, while fostering community governance, also safeguard consumers’ interests. This is particularly important as financial companies are likely to resist external oversight and accountability, for instance by opposing financial algorithm and data disclosure and audits.

Data trusts can develop strategies to push for more favorable terms concerning data collection, usage, monetization, and access, including privacy and transparency (Houser and Bagby Reference Houser and Bagby2023: 144, 160). This is consistent with the idea that consumers have some ownership over their financial data and accessing it serves to attain positively valued outcomes (Fracassi and Magnuson Reference Cesare and Magnuson2021: 330, 346; Jurcys et al. Reference Jurcys, Donewald and Fenwick2021). For instance, trusts can develop and promote the enforcement of ethical guidelines for the use of financial data. These guidelines may include prohibiting the use of consumer data for predatory lending practices, ensuring that data is not used to unfairly discriminate in financial decision-making, and requiring transparency about the extent to which consumer data influences investment recommendations. Similarly, data trusts could promote data portability and interoperability standards. Trusts could advocate for standards that make it easier for consumers to transfer their data between different platforms. This could involve requiring financial companies to provide data in standardized, machine-readable formats, ensuring that consumers can easily move their financial data to competing platforms. Additionally, trusts could promote the development of open application programming interfaces (APIs) that foster greater competition within the financial technology sector. These standards could reduce the lock-in effects that help entrench data oligopolies. All these strategies by data trusts can help ensure that powerful financial companies and their financial technology also operate in the best interests of consumers and smaller companies.

Furthermore, consumer data trusts may provide oversight of financial companies, and in particular their financial technologies and data. Financial companies rely heavily on complex algorithms to analyze data and make decisions. Data trusts could demand transparency of how algorithms use consumer data, review them for potential biases or unfair practices, and require human oversight for certain types of algorithmic decisions. This oversight can help prevent algorithmic bias and discrimination (Shkabatur Reference Shkabatur2019, 383), ensure fair treatment of consumers, and exemplify the power of data trusts to “reveal ‘black box’ algorithms that leading technology firms use to concentrate their power and perpetuate injustices” (Houser and Bagby Reference Houser and Bagby2023, 118). Consumer data trusts could also conduct regular audits of financial and fintech companies’ access and use of consumer financial data (Blankertz Reference Blankertz2020; Houser and Bagby Reference Houser and Bagby2023, 179). These audits may involve reviewing data sharing agreements between financial companies and data users, analyzing data flows to ensure they comply with agreed-upon terms, and verifying that data access is limited to authorized purposes (Blankertz Reference Blankertz2020, 16–17). By performing these audits, trusts can help ensure that financial data oligopolies are not overstepping bounds or misusing consumer data. Furthermore, consumer data trusts can set and enforce stricter privacy and security standards than individual consumers or regulators might be able to achieve. This could mean mandating specific encryption and data protection protocols, requiring regular third-party security audits and imposing penalties for data breaches or unauthorized access. The trust’s ability to terminate agreements with companies and withhold access to member data gives it leverage to enforce these standards (Blankertz Reference Blankertz2020, 17).

In terms of value distribution and compensation, consumer data trusts could negotiate for more equitable sharing of profits generated from consumer financial data (Mulgan and Straub Reference Mulgan and Straub2019; Houser and Bagby Reference Houser and Bagby2023, 175). They could, for instance, require financial and fintech companies to share a portion of revenues derived from consumer data analysis, negotiate reduced fees or improved services for trust members, and ensure that insights from aggregated data are made available to consumers. This approach ensures that consumers receive a fairer share of the benefits generated by their data, rather than allowing large financial institutions to monopolize those gains. This aligns with the growth of data marketplace organizations that assist consumers in collecting and selling their data (Blankertz Reference Blankertz2020, 11).

Moreover, consumer data trusts can establish mechanisms for resolving disputes related to data use and for sanctioning violations (Houser and Bagby Reference Houser and Bagby2023, 179). For financial companies, this might involve creating independent arbitration panels to handle complaints, establishing escalating penalties for repeated violations of agreed-upon terms, and providing consumers with the ability to challenge decisions made using their data. These processes offer consumers recourse if they believe their data has been misused.

While the implementation of such strategies and practices will likely face challenges, including resistance from financial companies and technical complexities, they offer a promising path forward. By empowering consumers collectively, consumer data trusts could provide a much-needed counterbalance to the growing power of financial data oligopolies, promoting fairer, more transparent, and more equitable use of consumer financial data. This can help build an effective community governance of financial data that could achieve important social goods such as privacy, quality, security, and innovation while enhancing competition. Collaborative data governance may spur innovation, improve access to financial services, and facilitate the emergence of new market players. Achieving these benefits, however, requires the cooperation of financial companies, regulators, and consumers to address challenges effectively.

Despite their potential, consumer data trusts face practical obstacles, such as achieving the necessary scale, developing effective engagement and internal governance among consumer members, having sustainable funding models, accessing appropriate technologies to manage shared consumer data, protecting against security risks associated with centralizing data within trusts, and navigating technical and regulatory landscapes. In particular, the legal status of consumer data trusts remains uncertain, and trusts’ above-mentioned strategies and practices are likely to require facilitative laws and policies. Nonetheless, consumer data trusts offer a promising avenue for experimenting with participatory and equitable data governance models. Ultimately, consumer data trusts should be viewed as part of a broader data governance ecosystem. Data commons are part of polycentric data governance systems or multiple different governance centers (Sanfilippo et al. Reference Sanfilippo, Frischmann and Strandburg2021, 270; Madison Reference Madison2024, 325). Consumer data trusts’ effectiveness could be enhanced when combined with community members’ cooperative practices, thoughtful regulation, technical standards, and efforts to improve data literacy. As financial companies expand their data practices, novel governance approaches like data trusts will be crucial. Further research and pilot projects are necessary to refine these models and assess their long-term impact.

10.3.1 BlackRock’s Data Collection Capabilities

BlackRock can acquire data through its massive client base via its Aladdin platform without needing countless permissions from clients to hand over various forms of data (Zetzsche et al. Reference Zetzsche, Arner and Buckley2023). The ease of such data retrieval supports BlackRock’s AI and machine learning technologies. Machine learning is an inductive approach to computing, in which AI systems analyze data to find the best solution to a problem, as opposed to following strict instructions coded by the programmers beforehand (Armour and Eidnmuller Reference Armour and Eidenmuller2020). Machine learning is possible if the AI systems can act as a central nervous system and form a neural network with other information technologies and computers to communicate and deliver data between densely interconnected processing nodes, where the data is analyzed and an output created. In order for AI to work effectively, it must have access to as much relevant and accurate information as it can from one centralized database. Since any type of data may have the potential to be relevant in the long run, AI is designed to not be restrictive in data collection.

Similarly, Aladdin is designed to store, centralize, and convert various unstructured data – data mined from numerous sources – into readable data that is easier to analyze and utilize to make effective decisions concerning portfolios, risks, trades, operations, performance, compliance, and accounting management. In other words, it is the central nervous system that is equipped with the ability to connect with other data technologies and extract as much information from them as possible. BlackRock simply needs to obtain permission to initiate data collection from clients’ information databases via the terms and conditions clients must consent to when signing up to use Aladdin. The terms include conforming to Aladdin’s privacy policy, which covers the collection of personal information, including identification data, contact data, electronic monitoring data, financial data, and even sensitive personal information data (information regarding dietary restrictions, sexual orientation, disabilities, and political affiliation, among other things, taken from public sources such as social media or other third parties such as credit card providers). GAFA privacy notices confirm this by stating that third parties are able to gain access to their users’ personal information collected by them; this is part of their own terms and conditions. Hence, the sharing of data between BlackRock and GAFA is inevitable because upon agreeing to Aladdin’s privacy policies, users also consent to the sharing of their GAFA information with BlackRock. Users who consent to data sharing when using GAFA services may unknowingly authorize these companies to provide data to third parties, potentially including BlackRock when GAFA companies engage it for institutional investment services or analytics. GAFA companies themselves are clients of Aladdin, subjecting them to the same data-collecting terms and conditions as other clients, thus providing BlackRock access to even more data (Steele Reference Steele2020, 23).

In addition, the upgrade to Aladdin Data Cloud – an investment platform developed in partnership with SnowFlake – further exacerbates BlackRock’s collection capability by enabling Aladdin to “host all of the [clients’] data – from both inside and outside Aladdin.”Footnote ⁹ This feature is heavily supported by clients who tend to pull “their own data sources from other places” to centralize their data for convenience and to maximize their use of the platform.Footnote ¹⁰ As clients of BlackRock, it is plausible that GAFA provides BlackRock with additional data not initially collected by Aladdin at registration in order to receive better results. When combined with the sheer volume of public market data – global economic data stock quotes, climate data, government decisions, alternative data like geolocation and consumer activity – it is clear that the data BlackRock has access to make investment decisions far exceeds the type of data outlined in Aladdin’s privacy policy. While this can benefit clients, who now have all their information in one place, by enabling them to make more accurate and successful investment decisions, it is also very profitable for BlackRock, which effectively now has all of GAFA’s information in one system.

Thanks to its partnership with Snowflake, the system can now integrate external data into its platform by using Snowflake’s data technology, which can process unstructured and raw data at a rapid speed, converting that raw data into structured data that can be integrated and read by Aladdin.Footnote ¹¹ As part of the agreement, BlackRock has access to Snowflake’s Data Cloud, which can be used in Aladdin’s analysis. This is not the first partnership BlackRock entered into with a view to improving Aladdin Data Cloud. In 2020, BlackRock and Microsoft formed a strategic partnership to host Aladdin on Microsoft’s Azure cloud platform, providing BlackRock with access to Microsoft’s data centers around the world.Footnote ¹² BlackRock was also able to leverage Microsoft’s scale in computing and expertise in cloud computing and data analytics, as part of the collaboration. While the partnership did not give BlackRrock access to other Azure users’ data, BlackRrock can use Microsoft’s computers to collect new data globally. Aladdin’s main cloud partner, Amazon’s AWS, provides another access point into Amazon’s database, including users’ purchasing behavior and other data provided by Amazon’s Internet of Things devices.Footnote ¹³ Being the top e-commerce company in the world that is also advancing into many lines of business, Amazon is providing BlackRock access to a plethora of information (Khan Reference Khan2017, 754–767).

Aladdin can acquire even more data from portfolio companies through BlackRock’s investments. This starts even before the purchase of any shares, when BlackRock is given access to prospective companies’ information, which may include all types of information that can be used to assess the opportunities and risks of investing in those companies.Footnote ¹⁴ When applied to GAFA, this can include any type of users’ information. As the head of the product group for Aladdin, Jody Kochansky noted almost a decade ago, given that untapped data – such as GPS locations, customers’ online behavior, and transaction histories – is tremendously valuable for quality investment analysis, the strategy is to access as much untapped data as possible.Footnote ¹⁵ BlackRock’s Aladdin can thus collect increasing amounts of data from all the four platforms before and after investing in them.

While BlackRock has utilized its Aladdin platform to collect data directly from GAFA in new and unconventional ways in order to optimize its AI technology and big data collection capacities, it also employs an indirect method of data collection: the exploitation of shareholder power to control corporate decisions and push for joint ventures, which ultimately aid its direct data collection. BlackRock’s investments across vast segments of the market – it is among a small group of institutional investors that own large stakes in most US corporations (Azar et al Reference Azar, Schmalz and Tecu2018; Backus et al 2021) – allow it to influence the way companies manage their data. Aladdin’s success has largely expanded BlackRock’s client base, which has increased the capital at its disposal, and enables it to invest massively in GAFA shares: BlackRock is one of the largest institutional shareholders in Google, Amazon, Facebook (Meta), and Apple.Footnote ¹⁶ This position means that its voting power and voice in corporate decisions is significant. For example, Larry Fink convinced Mark Zuckerberg to change Facebook’s platform design to be more “purpose-driven,” namely, to focus more on promoting posts from friends than from advertisers (Serafeim Reference Serafeim2018). This was a costly move for Facebook, whose main revenue relies on its advertising model.

10.3.2 Caveats

The considerations in the previous section provide insights into the technical, legal, and managerial rules-in-use that underpin BlackRock’s unparalleled data collection capabilities. The difficulty in collecting evidence is compounded by the fact that the precise data management procedures employed by BlackRock and GAFA are kept under seal, thanks to intellectual property law, trade secrets, out of court settlements, not to mention the considerable complexity of the software (Pasquale Reference Pasquale2015, 67, 103).Footnote ¹⁷ The public cannot investigate the contractual terms of joint ventures, nor can they criticize how the companies’ algorithm collect and analyze data, even if companies provide public disclosure in “plain language” to comply with consumer protection law (Pasquale Reference Pasquale2015, 7–8, 51, 103). Even internal stakeholders, who may be aware of BlackRock’s business practices, and thus are great sources of information, are unable to share their observations with the public due to their contractual obligations to BlackRock. For example, BlackRock required departing employees to sign separation agreements to receive severance payments that included provisions waiving rights to whistleblower compensation for disclosing wrongdoings. This discourages whistleblowers, as it waives any right to recovery of incentives for reporting of misconduct.Footnote ¹⁸ Even though the Securities and Exchange Commission successfully removed this particular clause in a 2017 lawsuit against BlackRock, the terms of the agreement suggested that BlackRock may have been conducting acts that it wanted to stay behind closed doors, and there certainly is no guarantee that it will stop trying to prevent whistleblowers from speaking out in the future. Hence, the opacity surrounding the operations of powerful companies such as BlackRock, and its attempts to maintain a black box, makes it almost impossible to gather empirical evidence to prove BlackRock’s strategy of creating a data monopoly. Aladdin’s lack of transparency highlights the need for public accountability and raises concerns about potential risks and harmful impacts on stakeholders, including financial consumers.

10.4.1 Potential Harms to Financial Consumers

One of the most important concerns with financial data oligopolies like BlackRock’s Aladdin is their potential impact on financial consumers – ordinary individuals who use financial services, invest their savings, or rely on credit. These retail consumers often have little awareness of the extent to which their financial well-being is affected by the operations of powerful data platforms behind the scenes. The first obvious concern relates to data privacy and surveillance. Aladdin appears to collect vast amounts of data about consumer activity to identify patterns and trends in consumer consumption.Footnote ¹⁹ Aladdin must continue to scale not just the amount of data but also the types of data it collects and analyzes to maintain its competitive advantage (Zetzsche et al. Reference Zetzsche, Birdthisle, Arner and Buckley2020).Footnote ²⁰ The more BlackRock collects nonfinancial data, such as GPS locations and online behavior, the greater the risk to consumers that their information is being misused, for commercial purposes or other harmful objectives. The massive centralization of data within a small number of platforms also makes them lucrative targets for cyberattacks or any other form of data breach, potentially exposing consumers to identity theft or financial fraud. Any failures of Aladdin can cause severe harm to consumers (Fraser Reference Fraser2022).

Furthermore, pervasive data surveillance enabled by financial platforms could lead to discriminatory practices. For instance, financial companies may use insights from these platforms to engage in “digital redlining,” where certain consumers or even smaller companies are excluded from financial services based on data-driven risk assessments (Friedline and Chen Reference Friedline and Chen2021). Digital redlining occurs when lenders use algorithmic models based on data analytics to exclude specific groups of consumers from financial products, often based on factors like zip codes, income levels, race, or even social media activity. While such practices may not be explicitly illegal, they can result in discriminatory outcomes that disproportionately affect minority or low-income communities. Platforms like Aladdin, which consolidate vast amounts of consumer data and apply sophisticated algorithms, could inadvertently reinforce these biases if not carefully monitored. If data models incorporate biased assumptions or historical data that reflects past discriminatory practices, they may perpetuate exclusionary patterns in lending. This can lead to financial exclusion and exploitation, as marginalized groups are either denied access to fair financial products or steered toward higher-cost, riskier alternatives. Over time, this exacerbates the wealth gap and deepens systemic financial inequalities.

Moreover, there is the risk that financial data oligopolies may facilitate financial exploitation of consumers (Das Reference Das2016, 27; Reynolds Reference Reynolds2017, 18–21), including predatory lending (US Senate 2018, 51; US Congress 2019, 2). Abusive loan terms can be imposed on borrowers, often targeting vulnerable individuals such as low-income consumers, those with poor credit histories, or minority communities. Financial data platforms like BlackRock’s Aladdin, which aggregate vast amounts of consumer financial data, may indirectly contribute to such exploitative practices, if their insights are misused or if the centralization of data leads to greater disparities in how consumers are treated by financial institutions. Financial data oligopolies may enable the exploitation of consumer data by identifying financially vulnerable individuals (US Congress 2019, 16–17). With access to granular data on consumers’ spending patterns, credit history, income, and even social behaviors, financial companies using platforms like Aladdin can build detailed profiles of potential borrowers. While this data can be used to assess creditworthiness and manage risk, it can also be used to target consumers with exploitative loan products. For example, lenders might use data insights to offer high-interest payday loans or subprime mortgages to individuals who are unlikely to qualify for traditional credit and can least afford such predatory products, but must accept them due to lack of alternatives.

Companies using platforms like Aladdin may develop highly optimized pricing strategies based on consumer data, allowing them to extract maximum profit from specific segments of the population. This can lead to the proliferation of high-fee financial products such as payday loans, subprime credit cards, or predatory auto loans, which impose significant costs on consumers, including hidden costs. Financial companies may employ data-driven marketing strategies that specifically target low-income consumers with unfavorable terms, taking advantage of their limited access to competitive loan products. The use of sophisticated data analytics makes it easier for lenders to prey on financially vulnerable individuals, further exacerbating financial inequalities. Financial companies may leverage data insights to identify consumers who are less financially literate or more likely to overlook the fine print in loan agreements. These consumers may be saddled with loans that come with hidden fees, balloon payments, or punitive interest rates, making it difficult for them to repay their debts and trapping them in cycles of debt. In this way, financial data oligopolies may enable the use of data to exploit the most financially vulnerable consumers (US Congress 2019, 9, 16–17; Bartlett et al. Reference Bartlett, Morse, Stanton and Wallace2022), undermining their financial well-being.

Additionally, financial data oligopolies may facilitate reduced competition and consumers’ loss of autonomy and choice in the financial market (US Congress 2019, 8). Aladdin and similar platforms wield enormous influence over how financial companies evaluate risk and allocate capital. This concentration of power can lead to reduced competition in the financial services industry, as smaller firms struggle to compete with the data analytics capabilities of larger companies using Aladdin. Consumers may have fewer choices when it comes to where they invest or borrow, as market concentration reduces the number of available financial products tailored to their needs.

The centralization of financial data also risks creating pricing and access disparities for financial consumers (US Senate 2018, 51; Bartlett et al. Reference Bartlett, Morse, Stanton and Wallace2022). Large financial companies using Aladdin’s platform benefit from economies of scale and superior data analytics, allowing them to optimize their operations and pricing strategies. Smaller financial companies without access to such powerful tools may struggle to compete, leading to higher costs for consumers in certain sectors. This can create a financial landscape where large, well-capitalized companies offer better rates or products, while smaller players may charge more for less competitive services. Wealthier consumers who are more likely to engage with larger companies may enjoy better financial products, while low-income consumers or those in underserved markets may be forced to pay higher prices, leading to worsen financial inequalities (Bartlett et al. Reference Bartlett, Morse, Stanton and Wallace2022, 55–56).

To illustrate how financial data platforms like BlackRock’s Aladdin can undermine the position of financial consumers as a community member, erode the community governance of financial data commons, and hinder the attainment of outcomes that community members may value, consider Jio BlackRock, a 50:50 joint venture that combines BlackRock’s expertise with data provided by Jio Financial Services (JFS). While this partnership aims to deliver tech-enabled access to investment solutions for millions of investors in India (BlackRock 2023), it will also harness vast amounts of data to assist JFS in expanding its lending business to consumers.Footnote ²¹ Yet, this effort to democratize financial services may pose risks. Reliance Jio, India’s telecom giant and the parent company of JFS, has an extensive digital footprint in India, managing data from telecom, retail, and digital services. Integrating this data with financial data and insights from BlackRock’s Aladdin creates immense potential for personalized services, but also raises significant concerns about privacy, data sharing, and profiling. As JFS plans to use advanced data analytics as a basis for consumer lending, leveraging information from credit bureaus, account aggregators, and consumer patterns,Footnote ²² this comprehensive data ecosystem may help develop detailed consumer profiling and surveillance, which can lead to concerns about targeted advertising, overreach, and misuse. These partnerships could exploit combined consumer data to push high-risk financial products, create opaque credit-scoring mechanisms, implement price discrimination, or engage in other unfair practices that can harm consumers’ interests (Pandit and Javed Reference Pandit and Javed2021, 215–216). Moreover, the reliance on technology and data-driven models may unintentionally exclude populations with limited digital literacy or access to reliable internet.

For example, in rural areas where smartphone penetration remains low, the digital-first approach of the joint venture could marginalize less tech-savvy consumers. Furthermore, data-driven credit scoring could lead to biased assessments. For instance, individuals lacking substantial digital footprints or with limited formal financial histories might face higher interest rates or outright rejection of financial products. This perpetuates existing inequities rather than addressing them. The BlackRock-JFS venture combines extensive consumer data with AI for decision-making. If not carefully designed, these systems may encode biases, leading to discriminatory practices. Algorithms might prioritize customers in urban centers or specific socioeconomic brackets, creating systemic inequalities. There is also the risk of unfair terms and predatory practices. The competitive advantages of BlackRock’s financial expertise and Jio’s expansive consumer base could pressure smaller financial institutions, potentially reducing competition. A monopolistic or oligopolistic environment might allow JFS to dictate unfavorable terms to consumers, such as high fees or restrictive product conditions. The partnership’s promise of “low-cost” financial products could also obscure hidden costs or cross-selling practices that burden consumers.

The history of numerous consumer complaints against the Reliance Jio group suggests that concerns about potential unfair practices facilitated by BlackRock’s Aladdin platform in partnership with JFS may have merit. For instance, there have been documented cases of service deficiencies, such as slow internet speeds and connectivity issues, excessive charges, price hikes, delayed refunding, and poor service to rural consumers, which have led to consumer dissatisfaction and legal complaints against Reliance group.Footnote ²³ These instances highlight a possible pattern of unfair practices harming consumers’ interests that could extend into the financial services domain if not addressed. While the regulatory framework in Western countries is stronger than in India, the likelihood of unfair practices that harm financial consumers is nonetheless real.

10.4.2 Consumer Data Trusts and the Building of Data Commons

As we have seen, financial consumers, as stakeholders in the governance of financial data commons, have the potential to play a pivotal role in overseeing financial data oligopolies and contributing to the development of the community governance of financial data commons. This engagement can represent a shift toward more participatory models of data governance, aligning with emerging concepts of data stewardship and digital citizenship and departing from disclosure–individual consent models. Specifically, consumer associations can evolve into more formalized entities, such as consumer data trusts (Delacroix and Lawrence Reference Sylvie and Lawrence2019; Blankertz Reference Blankertz2020), which may act as intermediaries aggregating and representing consumer interests vis-à-vis data oligopolies. These trusts can leverage technical and legal expertise, as well as enhanced bargaining power, to oversee financial data oligopolies, including negotiating more favorable terms of data use on behalf of consumers. This approach addresses the power imbalance between individual consumers and financial companies.

The potential of consumer data trusts in commonizing and governing financial data, and holding financial data oligopolies to account, is exemplified by a recent court decision that underscores the growing recognition of collective consumer rights and the legal standing of entities representing these collective interests. The decision in Meta v Federation of German Consumer Associations (2024)Footnote ²⁴ illustrates the role that consumer associations can play in overseeing companies, including data oligopolies and their financial technologies. The ruling clarified the legal standing of entities that may bring a complaint for data privacy breaches, and established that authorized entities, such as consumer associations, can bring representative actions on behalf of data subjects when their rights are infringed during data processing. In reaching this conclusion, the court interpreted Article 80(2) of the GDPR and broadened the scope of representative actions.

The ruling enabled consumer associations to challenge not only data breaches but also the inadequate provision of information, expanding the oversight that such entities can exercise. This gives consumer associations the ability to play a proactive role in shaping data practices, rather than merely reacting to data breaches. Moreover, by establishing clearer standards for transparency in data processing, this case sets a benchmark for the quality and accessibility of information that companies must provide to consumers. The decision reaffirmed Meta’s obligation to provide clear, concise, and easily accessible information about the purposes of data processing and the recipients of the data. The case also carries significant cross-border implications. Although it involves a German consumer association challenging a global company like Meta, the case highlights how consumer associations or similar entities can operate across borders to protect consumer rights in a globalized digital economy. By lowering barriers for individuals, the ruling makes it easier for consumers to have their rights protected through consumer associations’ representative action without needing to bring complex legal challenges themselves.

This decision of the European court provides some basis to suggest that representation may allow for the gradual creation of data trusts (Ruhaak Reference Ruhaak2020, 17). Consumer associations such as the German one can potentially act similarly to a consumer data trust by aggregating the interests of individual consumers to bring a representative action against giant tech companies, such as Meta. While the existing legal framework, including Article 80 of the GDPR and this court decision, needs to be expanded to clearly permit data trusts (Ruhaak Reference Ruhaak2020, 17; Blankertz Reference Blankertz2020, 26; Iwan-Sojka Reference Iwan-Sojka2024; Ruhaak Reference Ruhaak2020, 17), these formal rules may provide some initial legal basis for empowering consumer associations to act as data trusts with a view to holding tech giants accountable. Collective action by consumer associations to oversee financial companies, including data oligopolies, may help address the information and power asymmetries between consumers and tech companies. The court’s decision does not go far enough in detailing or legalizing consumer associations’ powers and oversight role, but it may enable such associations to develop strategies or practices, or informal rules, aiming at supervising financial companies and their financial technologies, including eventually BlackRock’s Aladdin.

Formal rules grounded in the court’s decision empower consumer associations acting like consumer data trusts to contribute to building a community governance of financial data, overcoming barriers to data commons created by data oligopolies. Consumer associations or consumer data trusts can, for instance, help curb data oligopolies’ self-serving rules, such as partnerships with tech companies and data-related liability exclusion agreements. Ultimately, consumer associations can rebalance power dynamics, enhance transparency, and protect consumers’ interests. These associations or trusts can go far in promoting the development of formal and informal rules that favor the attainment of important social goods – such as preventing financial exclusion, exploitation, surveillance and privacy invasion – that the governing community values. Recent developments in open banking laws and policies, which grant financial consumers greater legal powers (rights to access, transparency, data portability, switching, and protection against the misuse of their data), expand the formal rules governing financial data. These advancements reinforce consumers’ potential to engage in the community governance of financial data commons and further empower consumer associations or consumer data trusts to hold data oligopolies accountable.

It will be important to explore the feasibility and effectiveness of this potential of consumer associations acting as data trusts. It will be critical to think about internal governance structures and trustees’ duties, and other facilitative formal and informal rules-in-use. Addressing questions pertaining to the use of technology to manage shared consumer data and implement their oversight practices (e.g., enforcement technologies) will be crucial, especially in the fact of data oligopolies’ opposing strategies and control over data infrastructures.