Article

Unleashing Data-Driven Strategies To Optimize Financial Services Data Exchange

Tony Bishop, SVP, Platform, Growth & Marketing
June 30, 2021

The rapidly evolving digital economy is remaking financial services organizations while transforming how they create and deliver value. In the process, firms are creating, processing and storing more information than ever before, and that led to an explosion of data followed by an acceleration of digital transformation in the last year. Despite the disruptive challenges it all creates, it’s imperative to realize it can all be leveraged to:

Enrich customer experiences
Increase margins and competitive advantages
Expand growth opportunities
These should be essential functions of any industry, but they’re mission-critical in the financial services industry.

So, if you’re in the financial services industry, what do you need to do to achieve those goals in an era of explosive data growth and accelerated digital transformation?

You must rethink and reinvent your IT infrastructure to focus on optimizing 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

Think about the massive data sets generated within the industry, especially after the pandemic caused an enormous jump (nearly twice as large as those reported by consumer packaged goods companies) in digital acceleration, according to a 2020 McKinsey survey.

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 within the financial services industry.

Now take that megatrend and combine it with the acceleration of digital transformation, and financial services firms are grappling with a variety of significant challenges, including;

Growth and competition – continued margin pressures, data monetization 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 – scale by consolidation, diversification in lines of business and acquisition of disruptors

Whether it’s Retail and Commercial Banking, Securities, Trading and Investment Banking (IB), Wealth and Asset Management or Insurance sectors, a shift in infrastructure strategy that focuses on optimizing data exchange is key to:

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

Data gravity not only inhibits enterprise workflow performance and increases costs, but it also raises new security concerns further complicated by regulatory requirements and other artificial constraints.

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.

Retail and commercial banking

If you work in this sector, you have to 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 they create barriers that impact data creation, ingress/egress controls, and AI/machine learning 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 optimizes data exchange, retail and commercial banking firms can:

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

Securities, trading and investment Banking

In today’s securities and trading space, you’re using AI to do everything from optimizing strategies to beat the market (find Alpha) to taking advantage of investment banking opportunities. Finally, you also have your hands in ESG investing and alternative data.

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

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

Create competitive trading strategies
Secure third-party data integration
Localize 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’re trying 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/machine learning capabilities and ingress/egress controls. Still, there are also challenges with data creation and data usage. Plus, data gravity can impact those firms on a global scale.

So, if an optimized data exchange strategy is infused in your architecture, you can:

Gain all of the advantages of the securities, trading and investment banking sectors while providing an advisory experience that differentiates you from your competitors.

Insurance and re-insurance

This is obviously another sector that relies heavily on enriched customer and client experiences while protecting their data. If you’re working in the areas of insurance or re-insurance, you’re likely using AI as part of your risk management and underwriting processes.

The challenges of a traditional architecture will impact omni-channel data creation and usage, ingress/egress, AI/machine learning capabilities and the means to conduct business on a global scale. In fact, through 2024, it is estimated G2000 Enterprises in the Insurance industry will face an acceleration of Data Gravity intensity expected to have grown by a compound annual growth rate of 143% globally.

Optimizing data exchange in the insurance and re-insurance sectors means you can:

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

Achieving optimized 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.

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 look at the latest entry into into our “Pervasive Datacenter Architecture (PDx™) library: The Optimizing Financial Services Data Exchange Solution Brief.

You’ll notice it says, “Data driven digital transformation.” That’s intentional because you have to turn your data into a strategic asset.

“In an increasingly digital world, being client-centric means being data-centric,” Santhosh Pillai, chief architect and data management at ABN AMRO Bank, explained in this article by The Wall Street Journal. “Particularly in the post-COVID-19 era, companies can’t meet face-to-face with clients, so they rely more heavily on data and analytic insights.”

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

Download your copy of the new Optimizing Financial Services Data Exchange Solution Brief to learn more.