Tony Bishop, Senior Vice President of Platform & Solutions, Digital Realty
October 10, 2024

The evolution from the digital economy to the data economy is remaking financial services organisations while transforming how they create and deliver value. This process has changed how these firms create, process, and store information more than ever before.

In the last few years, this has led to an explosion of data followed by an acceleration of digital transformation, and financial services firms are now challenged to launch Hybrid IT, data, and artificial intelligence (AI) initiatives.

Despite these technological shifts, it’s imperative to realise all these infrastructure initiatives can be leveraged to:

Enrich customer experiences
Increase margins and competitive advantages
Expand growth opportunities

These goals are essential to any industry but are especially mission-critical for financial services firms.

What must you do to achieve these goals in an era of explosive data growth, accelerating digital transformation, and Hybrid IT, data, and AI readiness?

You must rethink and reinvent your IT infrastructure to focus on optimising data exchange.

Let’s take a closer look at what that means and why it’s necessary.

Data Gravity intensity is forcing a fundamental shift in IT infrastructure architecture strategy within the financial services industry

Consider the massive data sets generated within the industry. According to the McKinsey Global Institute, if global banking incorporated generative AI to assist with managing their productivity, they could realise between $200 billion and $340 billion in value annually, or 2.8% to 4.7% of total industry revenue.¹

We conducted our own research, built a global database, and cracked the code on measuring, quantifying, and forecasting the growing intensity of the enterprise data creation lifecycle and its impact on IT infrastructure. It’s a megatrend called Data Gravity, which continues to amplify at an explosive rate.

Now combine that megatrend with the acceleration of digital transformation and Hybrid IT, data, and AI initiatives. The impact of these forces on financial services firms leaves them grappling with significant opportunities and challenges to conquer, including:

Growth and competition: Continued margin pressures, data monetisation goals, and FinTech competition
Complexity and cyber risk: Changing customer preferences and growing fraud losses
Regulations and compliance: Regulatory deluge, data-centric regulations, and unrelenting cyber threats
Mergers and acquisitions: Scaling by consolidation, diversification in lines of business, and acquisition of disruptors

Whether it’s retail and commercial banking, securities, trading and investment banking, wealth and asset management, or insurance sectors, a shift in infrastructure strategy that focuses on optimising data exchange is critical in:

Defying Data Gravity barriers
Securing sensitive data
Enforcing data compliance
Utilizing AI-based capabilities
Reducing risks
Lowering costs
Growing revenue

Optimizing data exchange helps protect and serve customers in all branches of financial services.

With Data Gravity creating a virtuous cycle of more data creation by attracting more applications and services, it inhibits enterprise workflow performance, increases costs, and raises new security concerns. This is often further complicated by regulatory requirements, data sovereignty, and other artificial constraints.

However, IT executives and decision-makers at banks, credit unions, and other financial institutions need the ability to lead an effective data-centric strategy that captures, processes, and connects data to all relevant lines of business. Especially when trying to implement new AI and machine learning (ML) initiatives.

AI can’t exist without data — plenty of it — and new business value won’t be unlocked without AI. In our 2024 Global Data Insights Survey, 67% of the respondents stated that having a well-defined data strategy is essential to effectively managing, governing, and leveraging data assets and executing AI.

Retail and commercial banking

If you work in this sector, you must determine:

How do I provide an enriched banking experience while offering customer privacy and AI-based credit and fraud protections?

It’s tough to achieve with traditional infrastructure because of barriers that impact data creation, ingress/egress controls, and AI/ML capability. Not to mention, Data Gravity can severely limit the means to operate banking on a global scale.

By creating a data-centric architecture that optimises data exchange, retail and commercial banking firms can:

Provide a competitive banking experience
Secure the data near the customer
Localise AI-augmented risk and fraud
Enforce local data compliance

Securities, trading, and investment banking

In today’s securities and trading space, you use AI to do everything from optimising strategies to beat the market to taking advantage of investment banking opportunities. Finally, you also have your hands in environmental, social, and governance investing and alternative data.

However, in today’s digital economy, several barriers can get in your way, including the limitations on AI/ML readiness, global trading capability, and ingress/egress controls.

By optimising data exchange in securities, trading, and investment banking, you can:

Create competitive trading strategies
Secure third-party data integration
Localise AI-based investment banking interactions
Enforce local data compliance

Wealth and asset management

If you’re a money manager or work at an investment firm, you try to create a differentiated advisory experience with clients, as well as incorporate alternative data sources and insights to maintain a competitive edge. This is reinforced by combining strategies with AI-based monitoring and reporting.

Like other parts of the financial services industry, wealth and asset management firms face challenges with AI/ML capabilities and ingress/egress controls. Still, there are also challenges with data creation and data usage. Plus, Data Gravity can impact these firms on a global scale.

By optimising a data exchange strategy infused into your wealth and asset management architecture, you can:

Gain all the advantages of the sector
Provide an advisory experience different from competitors

Insurance and reinsurance

This sector relies heavily on enriched customer and client experiences while protecting their data. If you work in insurance or reinsurance, you likely use AI as part of your risk management and underwriting processes.

The challenges of using legacy architecture will impact omnichannel data creation and usage, ingress/egress, AI/ML capabilities, and the means to conduct business on a global scale. In fact, through 2024, it’s estimated that G2000 Enterprises in the insurance industry will face an acceleration of Data Gravity intensity, which is expected to grow by a compound annual growth rate of 143% globally.

By optimising data exchange in the insurance and reinsurance sectors, you can:

Provide a competitive insurance experience
Localise your AI-based products and services
Enforce local data compliance while securing third-party data integration

Achieving optimised data exchange requires a pervasive business platform that operates ubiquitously and on demand, augmented by real-time intelligence to best serve customers, partners, and employees through digitally-enabled interactions across all channels, business functions, and points of presence.

Payment processors

Those who offer payment services rely heavily on real-time data reporting and analytics to scale the required methods for processing customer transactions. Traditionally a manual process, payment services have started to turn to AI for better solutions. These solutions include improved transaction times, increased security, rapid approval processing, and increased availability models (24/7/365).

By optimising data exchange in payment processing, you can:

Differentiate the payment processing experience
Secure third-party data integration
Localise AI-based insights and support
Enforce local data compliance

Consider implementing a high-density colocation strategy to handle the power, advanced cooling, security, compliance control requirements, and interconnectivity required to support this AI solution to deliver low latency globally.

How to overcome the digital transformation challenges and Data Gravity barriers in financial services

If you’re in the financial services industry, I encourage you to check out the latest solution brief in the Digital Realty Pervasive Datacenter Architecture (PDx™) library: Optimising Financial Services Data Exchange.

In it, we talk about the “data-driven digital transformation.”That’s intentional as you must turn your data into a strategic asset.

This solution brief provides financial services business and technology leaders with a codified strategy and solution approach to implement data-driven digital transformation, obtain a competitive advantage, and unlock new growth opportunities.

Download your copy of the Optimising Financial Services Data Exchange solution brief from Digital Realty to learn more.

Unleashing data-driven strategies to optimise financial services data exchange