Role of Data Virtualization in the Banking Industry – Key Points of Focus

The financial sector is currently navigating a storm of digital headwinds. Banking institutions are under pressure to innovate faster than virtually any other industry today. Legacy infrastructure often blocks progress, but data virtualization in the banking industry is the differentiator. It allows institutions to bridge the gap between old mainframes and modern cloud applications without the risks of massive data migration. Banks can achieve the agility needed to compete in 2025 by decoupling information from physical storage.

Improving customer experience was identified as the top driver for transformation among global financial decision-makers in a recent study. However, information is still the main resource for this engine. With the stricter regulations of 2025 such as DORA (Digital Operational Resilience Act) and open banking mandates, the focus has shifted. Banks must ensure that data rights, privacy, and security are maintained while speed remains a priority.

Data Virtualization in the Banking Industry

Data virtualization is a type of data integration technology. It is the process of abstracting data contained within a variety of information sources so that they can be accessed without regard to their physical storage or heterogeneous structure.

Most traditional data integration solutions, such as Extract, Transform, Load (ETL), operate by physically moving a copy of the information to a new, consolidated repository. This creates redundancy, latency, and “data swamps.”

The “Netflix vs. DVD” Analogy

To help illustrate the differences between information access and traditional warehousing, consider a comparison between accessing a movie and purchasing a DVD:

• Traditional Warehousing (ETL): This is similar to purchasing a DVD. You have to visit the shop, procure the particular disc, bring it to your place, and keep it on the rack before you get to see it. In case the studio publishes a Director’s Cut, your edition is outdated.

• Data Virtualization: This is like Netflix. You do not own the physical file, and you do not store it on your device. Instead, you stream the content on-demand from the source. It is instantaneous, requires zero storage space on your device, and you are always viewing the most current version available.

Compared to other data integration methods, this method is generally easier to implement as it supports existing infrastructure in its current state. Because it provides details in real time from a variety of systems, including transactional processing systems and cloud application storage systems, it can support a wide range of use cases.

There are several advantages to using data virtualization in the banking industry:

• A Comprehensive, 360-Degree View: Instantly combine records from checking, savings, loans, and credit cards to understand a customer’s total financial health.
• Timely Financial Information: Make pricing decisions based on live market insights rather than yesterday’s batch reports.
• Enhanced Client Reports: Integrate information from multiple sources into one dashboard without manual compilation.
• Fraud Detection: View real-time and historical transactions simultaneously to identify anomalies.
• Integrated Risk Assessment: Assess risk exposure across the entire organization in real time, not only at month’s end.

Critical Applications for 2024-2025

While it is clear that data virtualization is an increasingly important technology, it is also becoming a mission-critical one in the next couple of years. Banks are no longer simply storing information; they are activating it. The focus is shifting from creating a “Logical Data Fabric” that spans the entire enterprise to achieving that goal. Below are some of the top applications for this technology over the next year or so:

Risk Reporting and Analytics

Many banks struggle to get a comprehensive picture of risk information because it is difficult to integrate disparate information sources. Regulatory reporting is an area where this is especially relevant. The primary issue is the amount of time it takes to create reports, along with the complexity of the various types of risk (market, credit, counterparty, etc.) associated with a bank.

• The 2025 Context (DORA & Basel IV): With the full force of the European Union’s Digital Operational Resilience Act (DORA) now affecting global standards, and the upcoming implementation of Basel IV capital requirements, regulators are requiring evidence of resilience. They are looking for evidence that a bank can withstand a shock at this moment in time, not at a previous point in time.

• The Solution: Compliance teams can use virtualization to connect directly to source systems to generate risk reports on demand. Since the information is not being moved or copied, there is no “lag time.” Stakeholders can create detailed, integrated reports using point-and-click access. This ensures capital reserves are optimized against actual, real-time risk exposure.

Client Reporting and CRM

Client reporting and customer relationship management (CRM) are two sides of the same coin. While many banks strive to provide improved functionality for their customers to build trust, if the information is stale, trust is lost. In order to create client-initiated reports and reports about clients, information must be integrated in real time. In these two areas, virtualization creates “Data as a Service” (DaaS).

• A High-Net-Worth Example: A high-net-worth client contacts their relationship manager. They want to know their total liquidity across their business accounts in London, their personal accounts in New York, and their investment trusts in Singapore.

• Without Virtualization: The manager states, “I’ll have to pull those reports and e-mail you tomorrow.”

• With Virtualization: The manager accesses a dashboard that queries all three geographies immediately. They give the client the response in seconds. This level of responsiveness is the standard to retain wealth management clients.

• CRM Impact: On the CRM side, the data integration technology allows companies to evaluate customer spending habits. It feeds real-time information into the CRM, so when a customer walks into a branch, the teller knows they just submitted an online mortgage application 20 minutes prior.

Managing Liquidity

For banks to effectively manage liquidity across different departments, they require immediate access to aggregated liquidity positions that focus on specific domains such as currency, geography, or product type. They need to compare these figures to standard ratios such as the Net Stable Funding Ratio (NSFR) and the Liquidity Coverage Ratio (LCR) on a timely basis. Data virtualization allows banks to aggregate departmental holdings to develop a true aggregated view.

• Real-time Treasury: In a volatile interest-rate environment, cash is king. Treasury teams cannot wait until the end-of-day batch processes to make decisions. Virtualization allows treasury teams to combine information from external market feeds and the organization’s own ERP systems.

• Predictive Cash Flow: By tracking orders and accounts receivable in real-time, banks can better forecast cash flow requirements. This allows banks to invest excess liquidity in a more profitable manner instead of letting it sit idle as a cushion against future uncertainty.

Customer Propensity Analysis

Knowing a customer’s service needs or preferred method of interaction is essential in today’s customer-centric marketplace. Providing customers with general knowledge of their service needs allows banks to provide new products and services that exactly fit their needs.

• Proactive vs. Reactive: This allows banks to empower their representatives at the point of customer contact. Virtualization technology provides representatives with a real-time view into customers’ financial transaction history.

• Example: If a customer’s transaction history indicates that they received a deposit from a “maternity benefit” fund, the system recognizes a life event. The virtualization layer feeds this to the marketing engine, which can then proactively offer a college savings plan or life insurance. This is not spam; it is timely and relevant financial guidance based on data.

Social Media Integration

Banks are finding creative ways to enhance their customer’s experiences through social media platforms. However, to leverage social media data, banks must rapidly integrate it with the sales data stored in their CRM applications. Virtualization integrates unstructured data (tweets, reviews) with structured banking data.

• Sentiment Analysis: If a customer complains about a failed transaction via Twitter, the bank’s social listening tool captures it. Virtualization links that tweet to the customer’s actual bank account record. When the customer calls the helpline five minutes later, the representative already understands why the customer is upset. This seamless integration keeps customer retention alive.

Multichannel Usage Integration

Most customers engage with their banks through multiple channels such as mobile, web, text, and social media. Maintaining accurate information across each channel can be complicated. Virtualization provides a real-time view of all relevant communication channels, ensuring the ‘state’ of the customer is synchronized everywhere.

• The Synchronization Problem: In legacy systems, a customer may update their phone number on the mobile app. However, the change does not appear in the branch system until the nightly batch update occurs.

• The Fix: Virtualization queries the single source of truth. If the customer updated the app, the branch will see the update immediately. Consistency is key for both user experience and security (for example, OTP delivery).

Fraud Detection

Understanding how customers have acted historically has enabled banks to detect fraudulent transactions in an effective manner. Banks also have an advantage; however, fraudsters are now utilizing artificial intelligence to simulate legitimate users.

Data virtualization aids the fraud detection process in three key areas:

• Pattern Exposure: It delivers integrated data views which expose channel-based patterns that could otherwise be lost within siloed environments.

• Compliance Reporting: It enables the creation of audit reports identifying specifically which information was accessed by whom.

• Contextual Profiling: It produces rich user profiles (device ID, geolocation, etc.) for real-time scoring purposes. For example, if a customer initiates a credit card transaction in Paris yet their mobile banking app is geolocated in New York, the virtualization layer flags the difference and blocks the fraud instantly.

Personalized Pricing

All consumers wish to receive unique preferences when it comes to pricing. Allowing banks to recognize and reward loyalty to a customer results in a stronger relationship between the two. Creating a customized price for a consumer, however, requires a complete picture of the consumer’s spending habits and financial transactions.

Virtualization simplifies this through the creation of a full-picture profile for each individual customer.

• Predictive Pricing Intervention: As an example, if a customer contacts the bank to pay off the full amount of a loan, there is a good chance the customer is contemplating refinancing the loan with a competing bank. With the aid of data virtualization, banks are able to take proactive measures. Using the customer’s historical data, the system can provide the customer with a custom retention offer—perhaps a lower interest rate—to retain the customer.

Mergers and Consolidations

Integrating the IT infrastructure of a newly acquired bank is a significant challenge that banks face during mergers and acquisitions (M&As).

• The “Virtual” Merger: Virtualization reduces the challenges associated with M&As. Rather than attempting to merge the data from the newly acquired bank, the acquiring bank simply overlays a virtualization layer on top of both systems.

• Instantaneous Value: This creates an environment where users perceive the data to exist in one location. The acquiring bank is able to begin offering cross-sell opportunities on “Day 1” of the merger rather than waiting until “Year 3”.

Role of Artificial Intelligence: Document Processing and Data Entry

While the best virtualization technology available today cannot resolve “bad” data, banks around the globe are rapidly moving forward with their digital transformation efforts. To enable virtualized views of data, banks require clean, digitized input. This is where Artificial Intelligence (AI) becomes a required component of the 2025 technology stack.

Document Processing via AI

Banks are document-driven organizations. They process millions of loan applications, tax returns, identification documents, and invoices annually. Until recently, this data was either captured on paper or PDF formats. AI document processing is the key to realizing the full potential of this data.

• How it Works: AI document processing is not like traditional OCR (Optical Character Recognition), which only captures the letters contained within a document. It recognizes context. For example, it can analyze a complex tax form, determine the “Adjusted Gross Income” field regardless of the format, and capture the value contained within that field.

• Integration with Virtualization: After the data is captured by AI document processing, it does not remain in a database. The data virtualization layer captures the data and provides access to the data for both the risk scoring engine and the loan officer’s dashboard simultaneously.

AI Data Entry

Manual data entry is slow, expensive, and error-prone (“fat-finger”). AI data entry solutions are transforming the way banks operate internally.

• Efficiency: Thousands of account-opening forms can be processed in minutes, whereas manual processing may require days.

• Accuracy: These models learn over time. For example, if a model encounters a handwritten form, it can accurately read the form and flag only the most ambiguous cases for human review.

• Foundation: By automating the input of data into the system, these solutions ensure that the data being provided to the data virtualization layer is clean and standardized. This reduces the need for subsequent retroactive data cleansing services and ensures that the “real-time view” the bank depends upon is accurate.

Future Outlook

As we move towards 2025 and beyond, the reliance of banks on data will continue to expand. The global market for data virtualization is expected to increase substantially due to the growing need for agility.

A new trend is emerging in the direction of “Data Mesh” architectures. In this model, decentralized teams manage their own data products (e.g., the Lending Team manages the Loan Data), and the data virtualization layer functions as the glue that binds the data products together. This prevents the bottleneck of a centralized data team and enables faster innovation.

With so many advantages, banks should consider data virtualization as a foundational layer of their modern architecture. Through the combination of virtualization accessibility and AI precision, banks can ultimately deliver the seamless, real-time experiences that their customers expect.