At JPMorgan Chase’s 2022 Investor Day, they announced that one of their six strategic priorities for next year is to ‘leverage data and technology to drive productivity and agility.’ They held true to this pledge. Indeed, banks were some of the earliest adopters of data monetization and many today are its biggest proponents.
The most well-known banks globally, including JP Morgan, Citibank, Barclays, and BBVA, are monetizing data. And it’s no wonder: an Accenture study found that banks can increase revenue 1-2% through data monetization. Which, considering that the top three US banks average over $100 billion per year in revenue, is a huge opportunity for additional revenue.
All the same, banking data monetization isn’t just about revenue generation. It’s also about how banks consult their internal data to develop better financial products tailored to their customer pains. It’s about how they’re using proprietary data to develop AI models that save the entire company time and money. It’s about how their data can support fraud prevention, saving the bank huge amounts by mitigating security breaches.
In this article, we’ll look into five key strategies that financial institutions employ to monetize banking data effectively. First, let’s look at how we define banking data monetization.
Banking data monetization is how financial institutions leverage their vast amounts of internal data to create value and generate revenue streams.
Banks accumulate a wealth of data from various sources, including customer transactions, interactions, demographics, and market trends. Put simply, banking data can shed insight into how wealth is distributed, how people are spending their money, and how they’re saving it. When enriched with geographic and demographic context, banking data is a source of rich economic, psychographic and market information. As such, banking data holds immense potential to be monetized through strategic initiatives that not only benefit the financial institution but also improve customer satisfaction and loyalty.
By analyzing customer data, financial institutions can gain insights into customer behavior, preferences, and trends, which can be utilized to develop innovative products and services, enhance customer experiences, and drive targeted marketing efforts. Let’s take a look at some specific strategies available for monetizing banking data.
One of the primary strategies for banking data monetization is the development of personalized financial products and services. By leveraging customer data, banks can customize offerings to match individual preferences and requirements.
For instance, personalized loan options, investment portfolios, or insurance plans can be tailored based on a customer's financial goals, risk tolerance, and lifecycle stage. We might also see banks personalizing financial offerings, such as introducing targeted credit card rewards based on spending habits, dynamic interest rates on savings accounts, and personalized investment recommendations tailored to a customer's risk profile and financial objectives.
This personalization enhances customer satisfaction and loyalty while driving revenue growth for the institution. By using data related to their customers, banks can reap monetary rewards whilst providing a better client experience on an individual level.
Which brings us to strategy 2: how data can be used in combination with AI/ML to improve banking customer service.
Another crucial aspect of banking data monetization involves leveraging artificial intelligence (AI) and machine learning (ML) algorithms to analyze vast amounts of customer data and derive actionable insights. From here, banks can develop amazing features and services for which customers are willing to pay.
For example, AI/ML enables financial institutions to enhance the customer experience by offering personalized recommendations, streamlining processes, and predicting customer needs. When banks provide proactive and predictive services, such as fraud detection, credit scoring, and customer support automation, they’re able to monetize these services, and consequently generate revenue.
Moreover, banks can use data to train algorithms for internal use, which overall improves the banks finances. We’ve seen AI and ML algorithms applied in various banking functions, including chatbots for customer service, algorithmic trading, risk assessment models, and fraud detection systems. These technologies empower banks to make data-driven decisions in real-time, leading to improved operational efficiency and risk management.
There are applications of banking data which have been around longer than AI and ML, however. Let’s look at a third strategy for monetizing banking data: targeted marketing campaigns.
Data-driven marketing strategies aim to maximize the effectiveness of marketing campaigns while minimizing costs. Financial institutions leverage banking data to segment customers based on demographics, behavior, and preferences, allowing for targeted marketing efforts.
These targeted efforts could be extended value chains into a customer’s long-term life plan, based on the individual’s demographic. For example, the bank could market them packages for planning for a wedding, buying property, or further education. This allows banks to better position products like long-term investment packages, savings accounts, and more.
Of course, to be able to do this, banks need to collect and manage consumer data at scale. But the effort is usually worth it an is an effective form of data monetization. It improves the bank’s relationship with the customer and drives healthy margins. By delivering personalized and relevant messages to specific customer segments, banks can increase engagement, conversion rates, and overall marketing ROI.
In strategy 4, we’ll look at probably the most common form of banking data monetization. That is, selling aggregated and anonymized datasets from a bank’s internal data repository to that external stakeholders can benefit from this information.
In addition to utilizing data internally, financial institutions can monetize their data assets by selling anonymized or aggregated data to third-party entities, such as research firms, marketing agencies, or other financial institutions.
The Managing Director of SMB at JP Morgan Chase said, “Small businesses can use this [consumer transaction card] data to figure out exactly where to set prices and store hours and staffing.” This indicates how anonymized data sets contain valuable insights into market trends, consumer behavior, and economic indicators. Such data points are valuable for market research, trend analysis, and decision-making purposes.
For example, a major fast food retailer combined transactional data from a US bank with its own data to make better operational decisions regarding its inventory, opening hours, and personnel. Consequently, they made better demand predictions and kept inventory levels optimal, with less excess inventory wasted. Ultimately, they generated $15 million of extra revenue as a result of their data purchase.
What’s been known in the industry as ‘alternative data’ is synonymous with banking and transaction data. Though ‘alternative data’ is falling out of favor as a name, the kind of data it describes - banking data one such kind - remains extremely valuable. The key to monetizing transaction data effectively is understanding the market and demand for these insights. As Alex Izydorczyk points out, the ‘bleeding edge of consumer spending research relies on both having a deep technical understanding of the structure of the data and a deep domain understanding of what matters to markets’. Which is a crucial part of due diligence and risk assessment that’s applicable to any monetization venture, whether carried out by an financial institution or different entity. On that topic, let’s turn to our final strategy for banking data monetization, which is concerned with identifying and mitigating risks facing the bank.
Effective risk management and fraud prevention are paramount for financial institutions to safeguard their assets and maintain trust with customers. By leveraging advanced data analytics techniques, banks can detect unusual patterns, anomalies, and potential threats in real-time. Machine learning algorithms can analyze transaction data to identify fraudulent activities, mitigate risks, and enhance security measures.
Banks also have the opportunity to use their dataset to ensure regulatory compliance and manage regulatory risks. By analyzing transaction data, banks can monitor and detect non-compliant behavior, suspicious activities, or breaches in internal procedures. This can help banks avoid hefty fines, reputation damage, and potential loss of license.
In the UK alone, banks spend £34.2bn per annum on Financial Crime Compliance (FCC). Utilizing a data-driven approach to compliance can significantly streamline the process, making it far more cost-efficient. Using data and machine learning also greatly reduces the potential for human error in compliance. By leveraging data, bank can automate many aspects of the compliance process, thereby reducing the time and resources required. Furthermore, a data-driven approach provides a more robust and comprehensive view of compliance, allowing for more precise and accurate monitoring. This can lead to improved compliance strategies and a better bottom line - data monetization and compliance all-in-one.
Looking ahead, banking data monetization is expected to play an increasingly significant role in the financial services industry. As technology continues to evolve and data becomes more abundant, financial institutions will need to embrace innovative strategies to harness the full potential of their data assets. Predictive analytics, artificial intelligence, and blockchain technology are poised to reshape the landscape of banking data monetization, offering new opportunities for revenue generation, customer engagement, and competitive advantage.
In summary, banking data monetization offers numerous opportunities for financial institutions to create value, drive innovation, and enhance customer experiences. By adopting personalized approaches, leveraging advanced technologies, and forging strategic partnerships, banks can unlock the full potential of their data assets while addressing regulatory compliance and privacy concerns. Embracing a data-centric mindset will be essential for financial institutions to thrive in an increasingly competitive and digital banking ecosystem.
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