Digital technology and internet use continue to cause structural changes in many sectors of the economy, including the financial services industry. Traditional banking practices have been substantially altered, raising questions about the future usefulness of banking halls. Banks have seized on the digitization trend by either investing in their own internal skills to transform and use technology in their processes, or they have partnered with other institutions to facilitate technology adoption.

Consumer patterns and tastes have shifted as a result of digital banking. Customers have raised their need for convenient and personalized banking that takes into account their individual spending and consumption patterns, and banks are doing their utmost to meet this demand.

Other new competitors in the financial services field, such as neobanks, have recognized this requirement from the customer’s perspective and positioned themselves as superior service providers by producing financial solutions tailored to specific market niches. One advantage that these new entrants have is the capacity to use either internal or external data to better understand their customers’ particular demands and design goods that meet them. Banks can also leverage on their own generated data or from other parties to better position themselves to their current and target customers.

Emerging Trends In Data Monetization in Financial Services 

Data monetization is the process of obtaining a direct or indirect economic benefit from large pools of data. Organizations can either sell or grant access to their large pools of data to third parties in exchange of monetary or some other economic benefit. On the other hand, they can use this data for their own objectives such as improving their customer experience or internal processes.

Financial service providers -develop data from two points; internally (this can be referred to as zero- or first-party data), and from external sources (second- or third-party data). Internally generated data is developed from questionnaires, polls or surveys that the financial institution may conduct with its customers.

For example, when logging out of a banking platform they may ask you to rate your experience. Additionally, this internal data can be developed when the bank analyses the behavior of its clients. For example, the bank may analyze how their clients spend their money and how often they receive payments into their accounts in order to develop useful insights.

Externally generated data tends to come from other parties outside the financial institution, and the company does not have a direct relationship with the people whose data they are viewing. These external parties can also be using this data for their own internal processes for example retail companies. One example of third-party data sharing is here in East Africa where we have NCBA bank entering into a partnership with a telco, Safaricom to offer credit and savings options to Safaricom’s customers via Mshwari.

In order to analyze the risk profile of these customers, NCBA bank gets access to their mobile money spending and consumption habits and is then able to extend credit to them without these customers having a direct relationship with NCBA. Additionally, a financial services firm can get access to this data from a third-party data aggregator who collects data from different sources. Financial institutions primarily use externally generated data to compliment their internal data and develop a wholistic view of their target markets.

For financial institutions monetizing their data is a clear formula for developing stronger relationships with their customers. A study done by Accenture in 2020 showed that the trust customers had with their banks reduced in 2020 compared to previous years due to a reduction in human interactions. The human aspect in financial services cannot be ignored as people prefer to talk to another person when choosing financial products such as a loan or a mortgage.

This reduction in human interaction is as a result of increased online banking that became a greater necessity in that year due to limitations on movement as a result to the Covid-19 pandemic. Banks can leverage on the information that they collect regarding their customers to offer personalized experiences to their customers when they interact with them.

In addition, banks can draw insights on their customer earning patterns and develop products that help them invest and save their money better. In the eyes of the consumer, this shows that the bank is concerned for their well-being and not just interested in earning fees. Neobanks leverage heavily on educational products that touch on saving and investing not only as an additional value add to their customers, but as a way of attracting them.

The new trend of open banking has allowed financial service players to share their internal data with third parties. Banks can also monetize these relationships for direct financial gain or strategic partnerships with third parties who mostly tend to be fintechs. Banks can share information regarding their customer’s credit card spending locations that can enable these fintechs such as payment service providers to offer location-based advertising that is relevant to the customers. On the other hand, the payment service providers can share information about the merchants that they are working with and based on the transactional information, a financial institution can extend short term working capital to the merchants.

With increased sharing of data between parties and financial institutions looking to monetize their own internal data, risks around data handling and customer privacy increase significantly. Around the world, regulators are putting into action regulation that will guide this. Payment Services Directive (PSD2) in Europe came into force in 2019. On the other hand, the Central Bank of Kenya in its strategic plan for 2021 to 2025 has acknowledged the growth and potential for open banking; and will be developing standards that the financial industry can follow to ensure data sharing is done correctly.

Read Also: Data Monetization in Healthcare: Trends and Opportunities

Internally, financial institutions should have a Governance, Risk & Compliance approach to management and oversight when developing their own internal capacities. This will enable them to handle and monetize customer data while being cognizant of existing compliance and privacy best practices.

Unlocking Financial Benefits Through Data Monetization

Data monetization enables businesses to harness their data assets and artificial intelligence (AI) capabilities to generate meaningful economic value. This value exchange system use data products to improve business performance, establish a competitive edge, and handle industry difficulties in response to market demands.

Financial benefits include higher income from the development of adjacent industry business models, access to new markets to build additional revenue streams, and the expansion of existing revenue. Cost optimization can be accomplished through a mix of productivity increases, infrastructure savings, and operational expenditure reductions.

In 2023, the global data monetization market was valued at USD 3.5 billion, and experts project it to reach USD 14.4 billion by 2032, demonstrating a compound annual growth rate of 16.6% from 2024 to 2032.

Treating data as a strategic asset

Data is one of the most valuable intangible assets for organizations. Therefore, adopting a holistic approach that prioritizes data-driven business transformation helps optimize value extraction. This transformation harnesses the power of data within the organization, enabling enterprise-wide cost optimization and unlocking net new direct revenue opportunities.

When it comes to data optimization, most organizations focus solely on infrastructure cost reduction. However, those that embrace data-driven business transformation strategies can multiply the benefits by considering revenue growth potential, optimizing costs across infrastructure, development, maintenance and enhancing data security and compliance.

Critical aspects of data-driven business transformation are the overall data monetization strategy and how data products are used. Data insight and AI automation drive cost optimization with predictive maintenance, process automation and workforce optimization. AI automation substantially reduces data security and compliance risks by proactively identifying and analyzing the severity, scope and root cause of threats before they impact the business.

The net effect of data-driven business transformation is increased compliance, productivity and effectiveness via automation across different business units, such as sales, marketing and services. This leads to revenue uplift through opportunities to create new services and channels.

Identifying data products

Industries across the board are experiencing a surge in enterprise data volume, presenting both challenges and opportunities. These challenges, along with specific industry needs and use cases, influence the types of data products organizations or markets require.

Data products are assets developed from a company’s internal data sources or by combining internal and public data, augmented with AI to extract unique insights that help drive business decisions. Managed as products, these data assets come with defined service contracts, repeatable delivery methods and a clear value proposition.

The banking industry, for example, faces the following challenges:

• Competition from agile and innovative financial technology and challenger banks.
• High degree of regulatory control.
• Need to protect sensitive information.
• Organizational data silos that impede a unified customer experience.
• Pressure to increase margins and identify new revenue streams.

To address these challenges, organizations create relevant use cases that address their specific needs, as well as the needs of the market at large. The following sample use cases show associated data products and corresponding financial benefits.

Use Case	Improve lending decision-making to reduce risk	Drive behavior-based recommendations and personalization	Develop customer service strategies based on comprehensive customer data
Data Product	Economic climate risk analysis	Customer behavior insights	Unified view of customer economic data
Financial Benefits	Improved market share predictability and revenue growth. Reduced costs through risk mitigation.	Enhanced understanding of customer preferences. Increased revenue growth through personalized product offerings. Improved user experience.	Increased customer lifetime value through tailored services. Reusable, integrated data across organizational silos.

Data products can be created for internal use across various functions or business units. When an organization shares its data internally and consistently to improve efficiency and achieve qualitative or quantitative benefits, it is referred to as internal data monetization.

Data products can also be created for wider external consumption across multiple organizations and ecosystems. When data is shared externally to achieve strategic and financial benefits, it is referred to as external data monetization.

AI-driven data platform economics

An AI-driven organization is one where AI technology is fundamental to both value creation and value capture within the business model. A data monetization capability built on platform economics can reach its maximum potential when data is recognized as a product that is either built or powered by AI.

In the collection-led model, data from external and internal sources, such as data warehouses and data stores, is fed into analytical tools for enterprise-wide consumption. At the enterprise level, business units identify the data they need from source systems and create data sets tailored exclusively to their specific solutions. This leads to a proliferation of organizational data and added pipeline complexity, which can pose challenges in upkeep and use for new solutions, directly affecting costs and timeliness.

As enterprises shift from collection-led to product-led models, data products are created by using external and internal data sources, along with analytical tools. Once developed, these data products can be made available to business units within the organization for real-time data sharing and analytics. Also, these data products offer opportunities for monetization through ecosystem partnerships.

In a platform-driven approach, business units build solutions by using standardized data products and combining technologies to reduce work, simplify the enterprise data architecture and decrease time to value.

The data platform offers data-enriched data products that use machine learning, deep learning and generative AI. Those AI-driven data products can virtualize and integrate disparate data sources to create domain-specific AI models using proprietary enterprise data. Data platform services enable data products to be provided as SaaS services, a single data mesh deployed across the hybrid cloud and authenticated, secure and audited data product delivery.

When organizations connect their valuable data and AI assets to wider user groups, they can use the multiplier effect from the consumption and evolution of data products, as well as the market reach from scalable cloud distribution.

The economic impact of data monetization

Organizations usually develop a business case spanning 3 to 5 years to gain a comprehensive view of short-, mid- and long-term economic benefits. Successful cases address market demands to remain competitive, foster scalability, and constantly pursue cost optimization and revenue enhancement opportunities.

In an example organization with USD 2 billion in revenue, the baseline revenue from data is USD 5 million (0.25% of the overall revenue). If the organization follows the traditional approach, revenue from data might grow by 10% year-on-year, from USD 5 million to USD 6.7 million in three years, just 1.34 times the baseline revenue.

In contrast, data monetization can act as a force multiplier and contribute to upwards of a 1% increase in a company’s revenue. With data monetization capabilities, revenue from data could potentially grow from USD 5 million to USD 20 million in 3 years, representing a fourfold increase compared to the baseline revenue.

According to recent economic impact reports, the cost of building a data monetization capability is less than the baseline revenue from data. Therefore, an organization might allocate a portion of its existing data revenue in the first year to build a data monetization capability.