Virtual Access to Mainframe Data is Essential for Financial Service Providers to Maximize Digital Transformation at Scale

Artificial Intelligence (AI) and Generative AI (GAI) may be the number one priority for financial services CIOs in 2024. However, modernizing and integrating the mainframe environment into the new financial services ecosystem to keep pace with AI and other digital transformation requirements shouldn't be too far down on the list.

Some analysts have been predicting for the past 5-10 years that mainframe computers are outdated and the financial service providers need to move their data and applications to cloud to be successful in their digital transformation journeys. In some cases, from a "fit for purpose" standpoint, this makes sense. However, the appetite for mainframes in general and the appetite for mainframes in the financial services industry just seems to continue to grow. According to several recent reports by well-known international IT research firms, mainframes are used by 71% of the world's largest companies with over 65% of all core IT workloads processed on mainframes. In the financial services industry, 90% of all credit card transactions are processed on mainframes. An IBM z16 mainframe can handle 19 million business transactions per day. That’s a lot of credit card payments, stock trades, and other business-critical transactions. Scalability, reliability, and security are essential for these kinds of mission-critical workloads and therefore the mainframe is going to continue to be the platform of choice for years to come. In fact, according to an article published on the Digital Journey website entitled, "Mainframe Market in Size 2023 | Technological Advancements in Report with New Forecast 2028, SWOT & Value Chain Analysis till 2028," the mainframe market size is estimated to be worth $5.3B in 2023 and is forecast to a readjusted size of $6.2B by 2028 with a CAGR of 2.6%.

However, conceding the fact that mainframes are still the best place for financials services companies to house their critical data and run their core applications doesn't address the need for these same companies to provide real-time access to mainframe data and applications, integrate it with the new cloud based data and applications ecosystems, and apply modern artificial intelligence to create and drive new and innovative business use cases at scale.

Acknowledging the accelerating requirements for AI and more accurate and real-time Large Language Models (LLMs) as an obvious driver, the digital transformation use cases requiring real-time access to data and applications in general are just too compelling to ignore from a business perspective:

Fraud Detection: Real-time access to mainframe data can help credit card companies identify and prevent fraudulent activities. They can monitor transactions in real time and flag any suspicious activities for immediate investigation.

Customer Service: Real-time data can help customer service representatives provide accurate and up-to-date information to customers. This can improve customer satisfaction and loyalty.

Risk Management: Credit card companies can use real-time data to assess the risk level of each transaction. This can help them make informed decisions about whether to approve or decline a transaction in real-time.

Credit Scoring: Real-time access to mainframe data can help credit card companies update credit scores in real time. This can help them make better decisions about credit limits and interest rates.

Marketing and Sales: Credit card companies can use real-time data to identify buying patterns and preferences. This can help them tailor their marketing and sales strategies to individual customers.

Billing and Payments: Real-time data can help credit card companies process payments and generate bills more efficiently. This can improve customer satisfaction and reduce operational costs.

Compliance: Real-time access to mainframe data can help credit card companies comply with regulatory requirements. They can monitor transactions in real time and generate reports for regulatory bodies.

Dispute Resolution: Real-time data can help credit card companies resolve disputes more efficiently. They can quickly access transaction details and make decisions based on accurate and up-to-date information.

Customer Segmentation: Credit card companies can use real-time data to segment their customers based on their spending habits, credit scores, and other factors. This can help them tailor their products and services to different customer segments.

Product Development: Real-time access to mainframe data can help credit card companies develop new products and services. They can identify trends and opportunities in the market and respond quickly to changing customer needs.

Mobile Payments: The rise of smartphones has given birth to mobile payment solutions like Apple Pay, Google Pay, and Samsung Pay. Mainframes provide the backend infrastructure that ensures the security and reliability of mobile transactions.

Contactless Payments: Contactless payment methods, such as Near Field Communication (NFC), have gained popularity. Mainframes enable these contactless transactions by securely processing the data exchanged between the card or mobile device and the payment terminal.

Tokenization: To enhance security, credit card information is often tokenized. Mainframes generate and manage these tokens, ensuring that sensitive card data is never exposed during transactions.

Blockchain and Cryptocurrencies: While still emerging in the credit card industry, blockchain and cryptocurrencies have the potential to disrupt traditional payment systems. Mainframes can be adapted to support blockchain-based transactions and digital currencies.

The IT vendor community isn't ignoring these requirements. Rocket Software , IBM , 微软 , 谷歌 , Amazon Web Services (AWS) , 甲骨文 , BMC Software , VirtualZ Computing , and a very long list of startups are all offering innovative and sophisticated solutions to address the need for real-time access to mainframe data and applications. The mainframe is going to be around for a long time as a major component of the enterprise IT ecosystem and the core platform running our financial services industry. As IT professionals, we need to accept this fact and welcome the opportunity to continue to innovate, transform and provide the best IT ecosystems possible to enable and support new business use cases for our clients at scale.