TODAY, MOST EVERY COMPANY NEEDS TO BE A technology company – meaning they must be tech-savvy and innovation-minded if they wish to stay competitive and remain relevant. And, if oil was the resource that fueled industrialization for many generations, then data is surely the new, must-have asset of our information economy. Sound, scrubbed and standardized data has become the cost of entry for doing business across most sectors, as well as serving as an important means of differentiation and competitive advantage.
Machine learning and generative Artificial Intelligence (AI) is turbo-charging what data can do. While an AI-powered future might feel some distance away, its transformative potential is top-of-mind across industries, with a new analysis from the McKinsey Global Institute estimating that generative AI could add the equivalent of $2.6–$4.4 trillion annually. By enabling large-scale data capture, mining and manipulation at speed, generative AI has introduced the potential to fundamentally reshape knowledge industries.
State Street’s position in the financial services industry places us at the convergence of technology and innovation, products and operations. As a highly regulated institution, State Street is able to leverage its 231-year history along with its size, scale and history of stewardship to bring early tech innovations such as blockchain and tokenization of financial assets to clients and the financial services industry writ large. Our role as a GSIB (global, systemically important bank) provides a unique vantage point from which to witness our current moment and the technology-led transformation of the global investment landscape that is currently underway. So, what are we observing at this critical moment in tech innovation? Given that data is the fuel for all AI activity, the opportunity AI presents today for the financial services industry points to the importance of companies first having in place a rigorous and trusted data transformation program. And research shows that firms that have a holistic data strategy in place are already enjoying meaningful benefits across various performance metrics.
A recently published State Street report, “ Capturing the Data Opportunity: Institutional Investors in the Age of AI,” examines the data opportunity in the age of AI for institutional investors worldwide. The first comprehensive study to quantify the data opportunity in economic terms, the report presents research based on a survey of more than 500 asset owners, asset managers, wealth managers, official institutions and insurers from around the world. It provides deep insights into where firms stand in their data transformation, the challenges they face and the tools they have at their disposal.
The survey results found that more than 80% of financial institutions surveyed rated the opportunity that comes from improved data management and usage as “medium,” “large,” or “transformational.” That said, the survey revealed a very surprising finding: while surveyed firms with a data strategy reported on average a 24% increase in customer satisfaction, a 21% increase in customer retention, a 19% increase in new client acquisition and a 19% increase in revenue growth, fully two-thirds of participating firms reported a lack of an overall data strategy, with asset owners lagging by financial institution categories surveyed. Our survey also pointed to where institutional investors expected AI to provide the most value in the next two to five years, namely:
- ENHANCED CYBERSECURITY
Using AI to analyze network traffic, detect anomalies and proactively identify potential threats. - AUTOMATED INVESTMENT ANALYSIS
Including gathering data and running trend analysis and predictive modeling - CUSTOMER EXPERIENCE AND ENGAGEMENT
Using AI-powered chatbots and virtual assistants to improve customer experience with natural language queries - RISK ANALYTICS
Generative AI-derived risk factors that augment existing macroeconomic, statistical and
fundamental factors - PERSONALIZED INVESTMENT ADVICE
Using AI algorithms to analyze individual investor profiles, preferences and market trends to provide personalized advice.
It’s worth noting here that although it might feel like AI technology was born last year, given all the attention paid to ChatGPT and other popular chatbots, many AI applications have been around since the late 1950s. Commonplace examples familiar to everyone include face ID and image recognition, chatbots that mimic people in customer service text or recorded messages, and expert systems like self-driving vehicles and chess-playing computers.
State Street has been developing AI functionality for over five years and is home to hundreds of AI practitioners. Leveraging the promise of AI has contributed to our transformation efforts and has driven greater service quality, accuracy and speed. We have used machine learning to enhance or automate existing processes to reduce manual work and increase efficiencies in ways that unlock capacity for our employees to advance more strategic and higher impact work.
As a technology-led company, we are looking to AI as a means of accelerating our innovation agenda. We are considering carefully how AI can help enhance efficiency across the business, improve client experience, empower our employees with greater opportunities and drive a more streamlined and productive organization to benefit all our stakeholders. Among potential uses we are currently pursuing are ways to incorporate AI into expenses reporting (to auto-populate scanned submissions and flag outlier submissions), RFP process enhancement (to automate responses and generate tailored, segment-specific marketing material), inquiry management (to reduce manual effort) and document intelligence (to expedite extraction of key information and data).
As AI adoption continues to grow, how to implement this technology in a mindful manner has become as important as what to do with it. At State Street, our approach to responsible AI rests on the following four focus areas:
- ETHICS
Ensuring fairness and avoiding bias through balanced representative samples for each target class and leveraging of diverse data labeling - PRIVACY AND SECURITY
Protecting data against security risks unique to AI use - TRANSPARENCY, EXPLAINABILITY, MONITORING
How AI systems are designed, what data they are trained on and how they are deployed and monitored. Also being transparent regarding the limitations of the technology and for what use-cases it is appropriate - ACCOUNTABILITY
Establishing clear lines of accountability, governance and controls.
An integral part of responsible AI is responsibility to our people. While it’s true that our industry is on the verge of a major shift, it is vital to remember that AI is still just a tool. Like the calculator, the computer or the internet before it, AI is an innovation that will only be as smart and capable as the people and teams that are putting the technology to work.
Large, publicly traded, global financial institutions have an obligation to serve shareholders and other stakeholders, including clients, employees, partners, vendors and the communities in which they live and work. Part of that responsibility takes the form of introducing new technology and innovations to investors in ways that help familiarize them to what these advances can do, and to thoughtfully integrate new technology into the broader financial services ecosystem.
Great interest exists across our industry today to use AI’s revolutionary technology to transform operating models and help investors make better-informed investment decisions. While we use AI to modernize and “future-proof” our own operating models, clients and other stakeholders are looking to us to thoughtfully integrate new technology into the financial services ecosystem and connect it to the broader institutional investor community. We do this work knowing that human expertise and insight, high-touch client engagement and personal relationships, and the kind of decision-making that comes with depth of experience remain irreplaceable.
