The financial industry is undergoing a massive digital transformation and the banking institutions are no exception to this trend. Facing fierce digital headwinds and pressure to innovate more than virtually any other industry out there, banking institutions are on the road to taking digital transformation to a whole new level. The industry is undergoing a massive transformation driven by many factors like – improved or higher customer experiences (through tailored products and services), new technology-aided processes, new service delivery channels and fierce competition. Digital transformation initiatives within the banking industry are shifting their focus – right from products and processes to customers and their individual requirements. In a study of 107 global financial services decision makers, Forrester found that improving the customer experience was the topmost business requirement that drove the banking companies’ transformation needs, followed by the need to integrate channels and provide innovative new products and services. Data remains at the center of this digital transformation. With initiatives like open banking systems and shift to cloud platforms, special priority should be towards ensuring customer data rights, privacy and security. Efficient data collection and management is crucial to improve processes and banks look forward to automate their data processing requirements with the support of professional data entry services.

So, how to banks proceed further and stay competitive while protecting their customers and complying with regulations is a matter of serious concern. In fact, banking institutions need to update their legacy systems and implement innovative strategies and services that will help transform them in to digital financial organizations. It is important to remain alert about access to information that can be easily leveraged to make timely business decisions, yet fulfilling general regulatory requirements. Banks in the digital transformation era need to have a single exclusive view of all their data across the entire infrastructure. This is the point where the concept of “Data Virtualization” helps facilitate digital transformation. Banks are leveraging data virtualization to enable greater agility in responding to customer needs. Amid the COVID-19 crisis, the global market for Data Virtualization, estimated at US$2.3 billion in the year 2020, is projected to reach a revised size of US$7.2 billion by 2027, growing at a CAGR of 17.8% over the analysis period 2020-2027. As per the Market Impact Survey, published by Report Linker – the United States data virtualization market is estimated to grow at $680.1 million.

Concept of Data Virtualization and Its Benefits

In simple terms, data virtualization is a data consolidation and integration technology. It is the process of abstraction of data contained within a variety of information sources so that they can be accessed without regard to their physical storage or heterogeneous structure. While, most data integration solutions move a copy of the data to a new, consolidated source, data virtualization offers a completely different approach. Rather than moving the data to new format, virtualization provides a real view of the data, leaving the source data exactly where it is. This means it is not essential to pay for the costs of moving and housing the data and yet expanding the benefit of data integration.

When compared with other solutions, the concept of data virtualization is relatively easy to implement as it accommodates existing infrastructure in its existing form. As data is provided in real-time from a variety of systems like transactional processing systems and cloud-based storage systems (that are normally very time-consuming to integrate), it can support a wide variety of uses. By leveraging data virtualization, banking institutions can gain certain benefits –

  • A detailed, 360-degree view into customers’ changing needs and behaviors
  • Timely financial intelligence (to make informed pricing decisions)
  • Improved client reports (that integrate data from multiple sources)
  • Effective fraud detection, with a view into real-time and historical transactions
  • A unified, real-time view of risk across the entire organization


Top Critical Applications of Data Virtualization in Banking Areas

Here discussed are some of the critical capabilities of data virtualization which banking institutions can consider –

  • Risk Reporting and Analytics – Banks often face challenges when it comes to integrating different data sources to obtain a single view of the risk data. This is particularly relevant in areas of regulatory reporting, including the reporting of risk and performance numbers. The primary challenge in this area is the time taken to create such reports combined with the areas of risk that are applicable to the bank such as market, credit, counter-party, or operational risk. Likewise, banks are often called upon to satisfy Basel III requirements, which can be challenging when they are undergoing mergers and consolidations. For such initiatives, data virtualization is perfect choice, as data can be consolidated, in real time, across myriad sources, to match the needs of any report. With data virtualization, data is no longer involves restricted access and stakeholders can produce detailed, integrated reports, with point-and-click easy access. In addition, the technology can also perform data quality checks as it consolidates the data and makes it available to customers.
  • Client Reporting and CRM – Even though client reporting and customer relationship management are two different activities, they are related to one another. While preparing client reports, banks often try hard to improve functionality for customers to earn their trust. Customer relationship management is another area which banks need to focus on. Data need to be integrated in real-time to allow client-initiated reports and reports about clients. In these two areas, data virtualization establishes data as a service that can be readily self-accessed by way of applications or by internal experts or external clients. On the CRM side, the technology allows companies to more effectively analyze customer spending patterns.
  • Managing Liquidity – To manage liquidity effectively across different departments of a banking institution, they need to have ready access to aggregated liquidity positions focused on domains like currency, geography, or applicable products. It is important to compare these figures against standard ratios like the net stable funding ratio (NSFR) and the liquidity coverage ratio (LCR) in a timely manner, to achieve a dynamic view into the organization’s liquidity. Data virtualization makes it possible to combine each department holdings to gain a true aggregated view into the risk elements, support reports (weekly or monthly) with real-time changes. In addition, information can be integrated from other external sources or an organization’s own ERP systems, to track orders and accounts receivable and payable data that help better predict the organization’s cash needs.
  • Customer Propensity Analysis – Identifying the customer’s service needs or mode of engagement in advance is an important aspect in today’s customer-centric market environment. Having a general idea about the customer needs will help offer new products and services that perfectly meet their requirements. These involves empowering banking representatives at the point of customer contact, having a correct customer profile with the most relevant, up-to-date information. Virtualization technology provides banking representatives with a real-time view in to the customer’s financial transactions or needs and thereby offers a product or service that could perfectly suit those requirements.
  • Social Media Integration – Banks are finding innovative ways to enhance their customer’s relationships via social media platforms. However, capitalizing social media data requires banks to quickly integrate it with other sources of data like – sales data stored in CRM applications. Data virtualization easily connects both sources of data, and makes the integrated data easily available for analysis.
  • Multi-Channel Usage Integration – With the advent of mobile banking, most customers interact with their banks online via mobile or other channels like text or social media. However, maintaining reliable information across different channels can be really challenging as banks needs to ensure better integration between different channels to provide a seamless experience. Virtualization offers a real-time view into all applicable communication channels and provides reliable access to analysts to check that each customers experience is consistent across all the prominent channels.
  • Detection of Fraud – Having a detailed understanding about the history of customer behavior (like payment patterns) makes it possible for banks to effectively detect fraudulent activities. Data virtualization can support fraud detection in different ways like – creating consolidated data views that expose patterns (that could be easily missed), facilitating the creation of audit or compliance reports (detailing which individuals have access to which specific data) and providing companies with rich user profiles (which can be used as context to support the real-time identification of fraudulent activity online).
  • Personalized Pricing – When it comes to pricing, all customers want special preferences. When banks recognize a loyal-customer, they understand that such treatment can help improve the customer relationship for the bank’s benefit as well as for the benefit of the customer. But, personalized pricing requires a comprehensive view of the customer, including their financial transaction patterns, usage and referrals. Virtualization makes it possible to automate many of the functions of personalized pricing by maintaining a detailed profile on each customer that accounts for the customer’s usage, referrals, and other information. It also enables predictive pricing interventions. For instance, if a customer inquires about paying off his entire loan amount, there is a high chance that the customer is planning to move elsewhere, and data virtualization enables banks to act proactively based on this information.
  • Mergers and Consolidation – Whenever there is a change to the banking infrastructure, data integration becomes a difficult task. Virtualization of data reduces the impact of such activities and automates access to the data sources. In fact, it creates a view into the data that makes users feel as though the data was in a single place.

Banking institutions are globally accelerating their digital journey, making rapid strides with their digitization efforts. In fact, many banks find the concept of digital transformation challenging as they rely on pre-established systems that are often poorly integrated. With banking industry leveraging data virtualization, that facilitates digital transformation via modern data integration, greater flexibility and efficiency can be achieved. With so many advantages, banks must consider data virtualization technology for creating new records for new clients. This may add up to the data assets of the organization, creating the need for data cleansing services as well as advanced data analytics. Data cleansing services offered by data experts will help in extracting core data regarding customers, employees and other aspects important to a banking organization. They also help the banks use its data to work in a direction that help clearly understand specific needs related to customers as well as the organization, and provide services in a timely and cost-effective manner.

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