The term “data-driven banking” refers to all activities that leverage data to provide a range of banking services.
Through the structural and targeted use of digital tools, data-driven banking makes a decisive contribution to achieving important results: in defining risk (drawing on richer and more comprehensive information in real time), in identifying new opportunities for growth (through insights that can help make more accurate decisions in a timely manner), and in developing personalized ways for banks to interact with their customers (turning the knowledge gained about an individual customer into a strong competitive advantage).
Financial services institutions that invested in advanced data management systems were able to increase the quality of their performance, improve the customer experience, and ultimately to increase profits. In the data-driven banking paradigm, the business objective no longer resolves itself into simple incremental gain but, through advanced data analysis, aims to identify ever-new, often hidden or as yet unexplored opportunities.
Before we look at the present of data-driven banking and look ahead to the future of the industry, let’s pause for a moment to discover the somewhat universal nature of the relationship that binds banks and customers.
Information, knowledge, trust: it all starts with data
While “data-driven banking” is not a new concept, in today’s world, it has assumed an unprecedented importance. To grasp its true significance, we must consider data-driven banking in relation to another concept that has always been central to marketing in this industry, that of “trust.”
Banks and financial institutions base the relationship with their customers on trust. On the customer side, “trusting” is first and foremost about sharing a range of sensitive information. Based on the knowledge gained from this information, the bank formulates hypotheses and designs specific solutions. If the foundation of the decision-making process is provided by shared knowledge, we could say that trust is the resource that fuels the very existence of banking.
What has changed with the advent of the internet is not the substance of the relationship between banks and customers, but rather the structure, extent, and intensity. The channels opened up through mass digitization have greatly increased the amount of data that banking organizations can access, leading to a real paradigm shift.
The most advanced evolutionary step in data-driven banking—the one we experience every time we access our bank’s online services—is a direct consequence of digital transformation. The most macroscopic effect of this somewhat epochal shift is the explosion of opportunities for industry players to deepen their knowledge of their target audience. Preferences, needs, buying patterns—a wealth of information contained in Big Data that tells us what and how much people are willing to pay. It is precisely because of data-driven banking that banks can holistically manage all of this information with a unique approach that integrates state-of-the-art methodologies and technologies.
The promise that has underpinned banking industry dynamics until now has been updated by data: the need to listen to needs and urgencies, to create offerings that benefit the customer, and to propose increasingly personalized services. In order for them to generate value (and build customer loyalty), data—both unique data and third-party data—must be managed strategically, handled in accordance with security procedures and regulations, interpreted correctly, and communicated through messages that are transparent, clear, understandable, and possibly interesting. To carry out all these key activities, the banking and finance market has opened up to so-called “FinTechs,” companies from the IT sector who are able to establish themselves relatively quickly as useful, and sometimes indispensable, players in guiding, innovating, and improving the core processes of “traditional” companies.
The emergence of FinTechs in data-driven banking
Within a data-driven banking context, FinTechs, whether startups or large technology corporations, use technological innovation as a lever to impact existing business models and to redefine the operational and operating logic of an increasingly crowded and competitive market. They can rely on incredibly efficient digital tools and a wealth of expertise that differentiates, enriches, and enhances the services that banks already offer.
The spread of FinTech has led to a change in consumer expectations, so much so that to keep up with competitors, banks have to redesign their customer experience completely. The distributed and natively digital nature of FinTech companies also helps to cope with crisis or emergency situations by shortening the distance between company and customers by implementing alternative modes of communication and agile and effective solutions, such as those set up to execute digital payments.
Digitization, which is a necessary premise of data-driven banking, has given an extraordinary boost to innovation in traditional financial services, for example, by simplifying access methods and streamlining or speeding up operations such as opening a bank account, applying for a loan, or making payments. This small revolution, which has had an impact on the revenues and relevance of many traditional providers, has also produced an important effect of social inclusiveness: it has made it possible to reach previously neglected or excluded targets.
From FinTech to open banking: data-driven banking to unleash the potential embedded in data
While FinTechs are entering the market without the burden of legacy systems and are able to use advanced technologies such as the cloud, AI, and ML to their full potential right away, they also have to contend with some structural limitations. For example, they cannot rely on domain knowledge and they lack the historical data needed to provide in-depth and accurate analysis. To resolve this conflict, the entire banking industry’s approach to data was fundamentally redesigned in 2019 following the introduction of Payment Services Directive 2.
The PSD2 directive required all European banks to open their APIs up to other players in the industry (APIs are Application Programming Interfaces, the software intermediaries that allow two applications to talk to each other), effectively marking the birth of Open Banking, the production framework where players in the banking ecosystem share data flows with each other.
The benefits of data-driven capabilities
Today, we all want to enjoy products and services that are fluid, easy to use, quickly available, and cost-effective. And, we expect to establish relationships with our bank that are rewarding, emotionally engaging, or even “fun.” To achieve higher quality customer experiences, we are willing to share data and information, even sensitive information (e.g., leaving reviews, enabling geolocation, creating accounts on social platforms).
Banks and other players in the industry segment the target audience using the data we provide (e.g., through customer profiling, analysis of transaction patterns, current and past behaviors), so that they get detailed information in real time. They can then predict (e.g., through predictive analytics) the products or services we will purchase in the immediate future and design offers that are best suited to us.
On the one hand, our willingness and tolerance translates into increasing amounts of data from various channels and third-party sources and, on the other hand, it translates into the creation of new data-driven functionalities that banks and financial institutions implement to improve their services (thanks in part to the intervention of FinTech in the aspects of the process that are most amenable to automation).
There are numerous benefits that data-driven banking functionalities can take advantage of to increase the value of financial services. These are the main ones: versatility, efficiency, personalization, increased revenue, accuracy of assumptions, and better risk management.
To increase revenue, financial services companies can use the data collected on customers to create new and innovative products and services, including in collaboration with non-banking institutions.
Collecting and optimizing data—which data-driven banking is based on—enables banking organizations to simplify and optimize their internal processes, including using artificial intelligence and machine learning solutions. As a result of data-driven banking, operational costs are reduced and overall performance levels increase. The availability of properly processed customer data reduces operational risks. This is because information coming in real time helps remove critical issues upstream and enhance automation. Synergistically using offline and online channels also enables increased customer numbers.
One of the most significant benefits of collecting and optimizing customer data is the personalization that these analytics activities allow. Banks can use the data collected to tailor their products and services to the personal needs of increasingly profiled and circumscribed targets. Tailored pricing, services focused on specific customer needs, in-depth content chosen to increase empowerment and financial well-being: these are just some of the initiatives that personalization can accomplish, directly and indirectly impacting both brand awareness and revenue.
With the results of increasingly sophisticated data analytics, often based on artificial intelligence, banks can visualize recurring behaviors and market trends and measure real-time efficiency of internal processes. In this way, they are able to identify their customers’ willingness to pay and to rethink their strategy for creating offers and products that are able to take advantage of the knowledge generated by the data.
By greatly increasing the accuracy of pricing models and reducing the need to formulate an indefinite set of assumptions in search of the “best” ones, banks and other financial organizations gain a significant competitive advantage: they anticipate market developments with more informed business initiatives and are able to both retain and acquire new customers, ultimately maximizing revenues.
More accurate assumptions
Thanks to data-driven banking, companies can make more informed decisions that influence a range of crucial activities: from promoting measures to prevent financial crimes (even very sophisticated ones) to helping financial institutions detect fraud, from expanding credit decisions to improving funding strategies to forecasting liquidity needs.
More accurate assumptions play a decisive role in mitigating risk, reducing costs, and maximizing sales because they enable the creation of predictive models. Based on this, banks can develop cross-selling offers that are truly relevant to the individual customer.
Improved risk management
By relying on data, banking and finance players minimize risk while operating in compliance with various regulatory authorities.
Maximize information assets to improve engagement process and strengthen customer relationship
To enable the implementation of data-driven banking initiatives and to support the possibilities that AI, ML, and Blockchain offer, you must redesign the data value chain so that it touches every stage of the process, from acquisition to storage, processing to sharing. This reorganization and restructuring, while extremely complex, can be successfully tackled today with new data capture and structuring tools, state-of-the-art cloud-based data stores, and analytical techniques for identifying connections between random data. Together, these tools and techniques can help organizations transform growing volumes of data into assets that can be used in automated, more complete, faster, and accurate decision-making processes.
By maximizing the value of information assets, market players (banks, financial institutions, FinTech) make the process of engaging new prospects and strengthening a relationship with existing customers more efficient and effective.
Data-driven banking enables a solid competitive advantage over the short and long term by focusing investment on two fronts:
- The consolidation of the information assets embedded in the data through the implementation of specific data governance strategies;
- The increase in the quality of the customer experience by fully exploiting the company’s existing information, through the creation of an open, interactive, personalized communications system.
If the new dynamics introduced by open banking enable a progressive expansion of the available information assets, data analysis is the first indispensable step in data-driven banking that is destined to influence the present and future of the industry.