Cyber Clock
Ashely Wolfgram
As financial-services companies around the world race to keep pace with a rapidly evolving technology landscape, they should consider not only what benefits new emerging technologies offer but also what risks they introduce.
The insights in this report were derived from a 2023 survey of 37 financial-services companies around the world. The institutions surveyed included asset managers and private equity companies; retail, corporate, and investment banks; payment companies and clearing houses; capital markets; insurers; and a major data provider. Twenty-six of the institutions reported less than $30 billion in annual revenues, and five reported at least $60 billion (exhibit).
Cloud and edge computing. In cloud and edge computing, workloads are distributed across locations, such as hyperscale remote data centers, regional centers, and local nodes, to improve latency, data-transfer costs, adherence to data sovereignty regulations, autonomy over data, and security.
Applied AI (inclusive to generative AI). Models trained in machine learning can be used to solve classification, prediction, and control problems to automate activities, add or augment capabilities and offerings, and make better decisions. Note that at the time of the development and issuing of the survey, generative AI (the next generation of applied AI, which can automate, augment, and accelerate work by tapping into unstructured mixed-modality data sets to enable the creation of new content in various forms, such as text, video, code, and even protein sequence) was included as subset of the applied AI technology category.
Next-generation software development. New software tools, including those that enable modern code deployment pipelines and automated code generation, testing, refactoring, and translation, can improve application quality and development processes.
Trust architectures and digital identity. Digital-trust technologies enable organizations to build, scale, and maintain the trust of stakeholders in the use of their data and digital-enabled products and services.
Industrialized machine learning. A rapidly evolving ecosystem of software and hardware solutions is enabling practices that accelerateand derisk the development, deployment, and maintenance of machine learning solutions.
Advanced connectivity. Wireless low-power networks, 5G/6G cellular, Wi-Fi 6 and 7, low-Earth-orbit satellites, and other technologies support a host of digital solutions that can drive growth and productivity across industries today and tomorrow.
Quantum technologies. Quantum-based technologies could provide an exponential increase in computational performance for certain problems and transform communications networks by making them more secure.
Future of mobility. Mobility technologies aim to improve the efficiency and sustainability of land and air transportation of people and goods using autonomous, connected, electric, and shared solutions.
While these technologies can provide exponential benefits, they can also bring cyber risks that companies must mitigate using their existing cybersecurity capabilities. The research shows that current capabilities are falling short of addressing these risks. Most survey respondents also recognize the need to strengthen critical cybersecurity capabilities, including third-party or supply chain management and privileged access management (PAM). As companies continue to increase their reliance on newer technologies, they must ensure they have thought through and implemented the necessary risk management capabilities. Otherwise, they may find the risks outweigh the benefits.
As the technology landscape in the financial-services industry continues to evolve rapidly over the next three to five years and as the associated risks mount, now is the time to future-proof the environment. Financial institutions can lay the foundations for action by asking themselves four questions about their pursuit of emerging technologies:
To better understand how institutions are approaching and prioritizing new technologies, we surveyed companies around the world about the applicability of ten emerging technologies to their businesses.
The survey results reveal that financial-services companies are not exploring all the emerging technologies equally. Instead, they are concentrating on those they perceive as most applicable to their organizations and likely to bring the most value, all while factoring in their current technological capabilities, their long-term business and tech strategies, and the potential regulatory impacts.
In recent years, financial-services companies have evolved into technology-driven companies. This tech-centric approach is visible in the ways they are prioritizing their investments; in addition to embracing software technologies, they are prioritizing investments in scaling technology development, such as DevOps (software development and IT operations), and industrializing machine learning and AI.
Institutions are also weighing the current level of maturity of each technology in their plans, considering the proven (and unproven) use cases that could add value to their businesses. The most applicable technologies were further along in their maturity journeys than some of those that were deemed less relevant.
Maturity and proven use cases undoubtedly help propel widespread adoption, and indeed survey respondents confirmed that cloud computing is already the most mature emerging technology used across financial-services companies. Over 70 percent of companies see their cloud adoption in the post-pilot stage, and 42 percent consider their capabilities fully adopted and in the maintenance stage.
Applied AI gets nearly as much attention, with almost 80 percent of respondents calling it relevant to their businesses. AI and machine learning have a long history in financial services. Corporate and investment banks, as well as insurers, were early adopters of AI and machine learning, decades before other financial institutions. The rest of the financial-services industry has caught up in recent years, and adoption has only continued to grow.
Unlike with cloud adoption, however, the maturity level of applied AI is still evolving. While many financial-services companies recognize the relevance of applied AI, most of their use cases remain in the early stages of development. Seventy percent of the survey respondents reported being in the pilot stage or earlier. Some use cases such as financial-crime, financial-risk, and asset modeling are quite mature. Those that are in the early stages include gen AI and large language models. Many institutions are still exploring their use in customer interaction support, personalized marketing, and fraud. These efforts offer companies the opportunity to gain a competitive advantage in the applied AI space before the technology is ready to be deployed. They can implement, for instance, proper oversight and responsible guardrails and controls for AI technology, thereby hastening its adoption for when it has sufficiently matured.
Almost 75 percent recognize the applicability of next-gen software development to their businesses, enticed by the ability to transform their software development life cycle and simplify previously complicated custom development tasks. AI-enabled development and testing, low-code and no-code tools, and other advances can improve processes and software quality in each stage of the development life cycle.
Next-gen software development is largely in the pilot stage across many companies. They stand to transform their software development life cycle, reaping the rewards of simplifying complicated tasks in custom application development. While only 11 percent of the survey respondents have fully adopted this technology, more than 50 percent are in the pilot or post-pilot expansion stage, indicating they have had time to consider the benefits and use cases of the technology.
Trust architecture and digital identity are also advanced across many companies. Almost 50 percent of the survey respondents put themselves in the post-pilot or maintenance stage of digital identity, and 70 percent call trust architecture applicable to their businesses, with use cases regarding digital banking, omnichannel customer experience, a 360-degree view of customers, and digital-wallet offerings. These efforts have demonstrated such benefits as faster innovation, stronger asset protection, and better customer experience, further persuading institutions to invest in underlying technologies, including zero-trust architecture, digital-identity systems, and privacy engineering.
At the other end of the spectrum, less than one-third of the survey respondents are considering the following emerging technologies that stand to benefit financial-services companies applicable to their companies today: quantum, future of mobility, and immersive reality. Many institutions may not see adoption of these technologies happening soon and therefore are not prioritizing them today, because of the longer runway for adoption. It could well be that advances in quantum computing over the next few years may result in quantum quickly rising to a top concern, given its potential for materially affecting areas like password breaches and encryption breaking.
While this perspective is appropriate when considering the current maturity of these technologies, especially compared with more advanced and widely adopted technologies such as cloud and edge computing, financial-services companies should not be so quick to dismiss them. Quantum computing, for example, is estimated to bring over $600 billion in value to finance, with potential benefits such as real-time automated decision making and support activities such as holistic stimulations of liquidity or risk stimulations as part of large-scale, high-margin deals.
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