Global Tech Report 2025: Financial Services Insights

Financial Services Technology Landscape in 2025

The financial services industry stands at a pivotal inflection point in 2025. According to the KPMG Global Tech Report 2025, a comprehensive survey of 612 technology executives from organizations with annual revenues exceeding US$1 billion, the sector is navigating unprecedented complexity. Persistently high inflation, geopolitical pressures disrupting supply chains, slow global economic growth, and interest rate volatility are all converging to create an environment where technology investment decisions carry outsized consequences.

What makes this moment unique is the paradox at the heart of financial services technology: the sector is simultaneously the most profitable adopter of artificial intelligence and the most constrained by legacy infrastructure. Financial services companies are generating profits from AI at rates exceeding every other industry, yet the vast majority remain trapped in pilot programs and proofs of concept that never reach enterprise scale. This tension — between ambition and execution — defines the technology strategy conversation for every bank, insurer, and asset manager in 2025.

The KPMG survey reveals that 74% of respondents reported revenues in excess of US$10 billion, and 45% were board members or C-suite executives. This is not a study of aspirational start-ups; it captures the technology priorities and pain points of the world’s largest financial institutions. Understanding these dynamics is essential for anyone seeking to navigate the digital transformation of financial services in the years ahead.

AI Profitability in Financial Services: The 92% Reality

Perhaps the most striking finding from the KPMG Global Tech Report 2025 is that 92% of financial services companies are now generating profits from artificial intelligence — four percentage points above the 88% cross-sector average. This makes financial services the leading sector for AI monetization globally. The subsector breakdown reveals even more granular insights: insurance leads with 94% AI profitability, followed by asset management at 92%, banking and capital markets at 89%, private equity at 88%, and real estate at 87%.

However, beneath this headline number lies a more nuanced story. Only 32% of financial services companies are generating AI returns at scale. This means that while the overwhelming majority have found ways to extract some value from AI — whether through automated claims processing, fraud detection, or personalized product recommendations — fully 67% have not yet achieved the kind of enterprise-wide AI deployment that transforms operational economics.

The gap between AI experimentation and AI at scale represents what many technology leaders are calling the “AI execution chasm.” Organizations are finding it relatively straightforward to build proofs of concept that demonstrate value in controlled environments. The challenge comes in scaling those solutions across complex, interconnected systems that span multiple business lines, regulatory jurisdictions, and data environments. As financial institutions grapple with this reality, the pressure to move beyond pilots intensifies, especially as competitors begin to capture scale advantages that compound over time.

For deeper analysis on how AI is reshaping investment strategies, explore our interactive guide to AI investment trends.

Generative AI Investment Priorities for Banking and Insurance

Generative AI has rapidly ascended to the top of the technology investment agenda across financial services. The KPMG report finds that 81% of banking and insurance CEOs now identify GenAI as a top investment priority, with 75% of asset management CEOs confirming the same. This near-universal executive commitment signals that GenAI is no longer viewed as an experimental technology — it is a strategic imperative.

The use cases driving this investment are diverse and expanding rapidly. In banking, GenAI is being deployed for automated credit assessment, code generation, and compliance management — particularly the ability to transform complex regulations into clear obligations, detect connections between regulatory requirements and operational risks, and evaluate the effectiveness of existing controls. In insurance, the focus has shifted toward automated claims processing, risk assessment, and the personalization of coverage products based on individual behavioral data.

What distinguishes the current wave of GenAI adoption from earlier AI deployments is the shift from a tool-specific lens to an integrated ecosystem approach. Financial services organizations are moving beyond viewing GenAI as a standalone capability (like robotic process automation or machine learning models) toward what KPMG describes as “ecosystem-level AI blueprints.” These blueprints consider the entire end-to-end value chain, drawing on multiple AI capability sets to transform not just individual process steps but entire operational workflows.

This architectural shift has profound implications. As David DiCristofaro, a KPMG technology leader, noted in the report: “Soon I expect we will see entirely AI-based service delivery models, which require a shift toward component-based architectures.” For financial institutions, this means that GenAI investment is not simply a technology decision — it is a fundamental business model decision that will determine competitive positioning for the next decade.

Legacy Systems and Technical Debt: The Weekly Disruption Crisis

One of the most alarming findings in the KPMG Global Tech Report 2025 is that 58% of financial services executives admit that flaws in their foundational enterprise IT systems disrupt business-as-usual on a weekly basis. This is not an occasional inconvenience — it is a systemic operational risk that drains productivity, erodes customer trust, and diverts engineering resources from innovation to firefighting.

The roots of this crisis run deep. Decades of incremental technology investment, layered integrations, and deferred maintenance have created what the industry increasingly recognizes as “digital debt.” Financial institutions have accumulated vast portfolios of legacy systems — core banking platforms, claims management systems, trading infrastructure — that were built for a different era. These systems often cannot communicate effectively with modern cloud-based tools, creating data silos that impede the very AI and analytics initiatives that leadership has prioritized.

The response to this challenge is clear in the data: 74% of financial services organizations plan to focus on investing in new technology rather than maintaining legacy systems over the next 12 months. This represents a decisive strategic shift. Rather than continuing to patch aging infrastructure, the industry is moving toward replacement and modernization, with cloud-based composable architectures emerging as the preferred target state.

Yet modernization at this scale is fraught with risk. Organizations frequently get stuck halfway through transformation programs, with results not justifying the costs invested. The KPMG report warns of “overcrowded technology transformation portfolios” that become difficult to manage, leading to delivery backlogs and growing digital debt even as institutions try to reduce it. The key to success, according to KPMG’s framework, lies in assessing existing systems for inefficiencies, creating clear roadmaps, leveraging partner assets to de-risk change, and building toward resilient, composable infrastructure that supports future growth.

Cloud Adoption and XaaS Transformation in Finance

Cloud computing and anything-as-a-service (XaaS) adoption have reached a critical mass in financial services. The KPMG Global Tech Report 2025 reveals that 82% of financial services organizations prioritized investment in XaaS technologies in 2024, making financial services the sector most likely to generate profit from its XaaS investments.

The benefits being realized are tangible and multidimensional. When asked about the impact of public cloud platforms and XaaS technologies, financial services executives identified better data management and integration as the top benefit (40%), followed by improved security and compliance (39%), improved efficiency (37%), and accelerated adoption of advanced technology (33%). Sustainability benefits are also emerging, with 31% reporting reduced carbon footprints through cloud migration.

Nearly one-third of respondents report that cloud and XaaS technologies have helped reduce both technology debt and total cost of ownership — addressing two of the sector’s most persistent pain points simultaneously. The shift toward cloud-based composable architectures is not merely a cost optimization exercise; it is an enabler of the agility and flexibility that financial services organizations need to respond to rapidly evolving market conditions, regulatory requirements, and customer expectations.

The regulatory landscape is actually accelerating cloud adoption in some cases. The Digital Operational Resilience Act (DORA) in Europe, for instance, requires financial institutions to implement auto-scaling, self-healing, and load-balancing architectures — capabilities that are native to cloud platforms but extremely difficult to achieve with on-premises legacy infrastructure. Similarly, the US Consumer Financial Protection Bureau’s Section 1033 requirements for consumer access to financial data are driving cloud-based data management solutions that prioritize accessibility and quality.

Data Maturity and Governance: Financial Services Leads the Way

Financial services has established a clear lead in data maturity over other sectors. The KPMG Global Tech Report 2025 measures data maturity across nine categories, and financial services exceeds the cross-sector average in every single one. The most significant advantages appear in data investment (58% vs. 53% cross-sector), data monetization (58% vs. 52%), and data accessibility (57% vs. 53%).

Perhaps the most encouraging trend is the pace of improvement. The report documents a remarkable 28 percentage point year-over-year uplift in the number of financial services companies reaching the top two maturity levels (Influential and Embedded) for data accessibility. This acceleration suggests that the sector’s investment in data infrastructure is producing compounding returns, with each improvement in data quality and accessibility enabling further advances in AI deployment and analytics capability.

Financial services organizations are also 5 percentage points more likely than their cross-sector peers to use data-centric decision-making to adapt digital transformation strategies in response to evolving market trends and risks (50% vs. 45%). Among global leaders across all sectors, this figure rises even higher — leaders are 18 percentage points more likely than non-leaders to embrace data-driven transformation planning.

The shift in how leading institutions conceptualize data governance is equally significant. Rather than treating data as a static asset requiring perfection before use, progressive banks and insurers are adopting a “data products” approach — treating datasets as products with specific business goals, maintenance standards, and quality benchmarks oriented around ongoing operational needs. This philosophy delivers data through what KPMG describes as “a marketplace of accessible data products” that can be discovered and served up to any part of the organization. To see how leading organizations are leveraging data insights, explore our interactive library on data-driven strategies.

Cybersecurity Challenges in an AI-Driven Financial World

Financial services occupies a unique position in the cybersecurity landscape: it is simultaneously the sector most likely to generate profit from its cybersecurity investments and the sector most targeted by sophisticated adversaries. The KPMG Global Tech Report 2025 paints a picture of an industry that recognizes the threat but is not fully confident in its ability to respond.

Only 43% of banking respondents in KPMG’s 2024 Banking CEO Outlook expressed confidence that their organizations’ cybersecurity defenses can keep up with challenges posed by AI advancements. This is a sobering statistic. As AI tools become more accessible, bad actors are weaponizing these technologies to manipulate individuals, businesses, and governments with unprecedented sophistication. Deepfake-enabled social engineering, AI-powered phishing campaigns, and automated vulnerability scanning are all raising the bar for defensive capabilities.

The regulatory response has been swift and comprehensive. The Cyber Resilience Act, DORA, and the EU AI Act are all introducing new requirements that financial institutions must meet. While these regulations add compliance complexity, they also serve as a forcing function for cybersecurity modernization, requiring institutions to upgrade aging systems that may harbor vulnerabilities.

Compounding the challenge is the fact that 58% of executives report weekly disruptions from foundational IT system flaws. Every such disruption represents a potential security vulnerability — an unpatched system, an inconsistent configuration, or a data flow that bypasses security controls. The connection between technical debt and cybersecurity risk is becoming impossible to ignore, adding urgency to the modernization imperative discussed earlier.

Regulatory Pressures Reshaping Financial Technology Strategy

Regulation has always been a defining feature of financial services, but the current wave of technology-focused regulation is qualitatively different. The KPMG Global Tech Report 2025 finds that 75% of financial services executives identify complex regulatory developments as the factor most heavily denting their investment confidence — 7 percentage points above the 68% cross-sector average. This makes regulation the single most important external constraint on technology strategy in financial services.

The concern extends beyond existing regulations to the gaps in the regulatory framework. Among insurance CEOs, 70% believe that the lack of current AI regulation within the sector could become a barrier to organizational success. This finding is counterintuitive — one might expect that less regulation would encourage investment. Instead, the absence of clear rules creates uncertainty about future compliance requirements, making organizations reluctant to commit significant resources to AI initiatives that might need to be restructured or abandoned when regulations eventually arrive.

KPMG’s recommended framework for navigating this environment involves four stages: baseline (understanding current and future risks and regulatory obligations), implement (preparing strategy considering the evolving risk and technology environment), manage (building robust risk management frameworks incorporating standards like ISO/IEC 42001), and prepare (designing program teams and governance structures). The ISO/IEC 42001 standard, published in December 2023, is particularly notable as the first international AI management standard, addressing ethical considerations, transparency, and continuous learning.

For financial services organizations, the message is clear: regulation is not merely a compliance exercise to be delegated to legal departments. It is a strategic input that must be integrated into technology planning from the earliest stages. Organizations that build regulatory adaptability into their technology architecture — through composable, modular systems that can be reconfigured as requirements evolve — will gain a significant competitive advantage over those that treat compliance as an afterthought.

Bridging the GenAI Talent Gap in Financial Services

The talent dimension of the financial services technology transformation is perhaps the most underappreciated challenge. The KPMG Global Tech Report 2025 reveals that only 16% of organizations have a workforce well-equipped to implement GenAI. This figure is striking given that 81% of banking and insurance CEOs identify GenAI as a top investment priority — there is a vast gap between strategic ambition and organizational capability.

The industry’s response to this talent deficit is multifaceted. A significant 61% of financial services firms are looking to hire new GenAI talent, creating intense competition for a limited pool of specialists. But hiring alone will not solve the problem. Leading organizations are pursuing what KPMG describes as a “multifaceted talent approach” that includes partnering with third-party providers who bring global experience, implementing internal learning and upskilling programs, leveraging global delivery centers, purchasing off-the-shelf AI assets that reduce the need for custom development, and co-investing with start-ups and fintech companies.

Beyond pure technical skills, the report emphasizes that digital leaders need to excel in “multimodal thinking” — the ability to rapidly respond to shifting circumstances and manage growing portfolios of change simultaneously. This is not a skill that can be learned from a GenAI certification course; it requires organizational culture change, new leadership competencies, and management frameworks that balance risk mitigation with innovation speed.

The integration challenge is equally significant. Nearly 30% of financial executives agree that difficulty integrating existing tools is one of the biggest barriers to AI adoption. Even organizations with talented AI teams struggle when those teams cannot connect their models to the legacy data systems, compliance frameworks, and operational processes that define financial services. This points to the need for talent strategies that emphasize not just AI expertise but also deep domain knowledge and systems integration skills. Discover how leading firms are approaching workforce transformation in our interactive workforce insights collection.

Strategic Roadmap for Financial Services Digital Transformation

Drawing together the threads of the KPMG Global Tech Report 2025, a clear strategic roadmap emerges for financial services organizations seeking to navigate the complex technology landscape ahead. The data points to four critical priorities that must be pursued simultaneously.

First, upgrade legacy systems with urgency and pragmatism. The weekly disruptions experienced by 58% of organizations cannot be tolerated indefinitely. The 74% planning to prioritize new technology investment over legacy maintenance signals the right strategic direction. The key is to avoid getting stuck in transformation programs that consume resources without delivering results. This means creating focused roadmaps, leveraging partner assets to de-risk change, and building toward composable, cloud-based architectures that can adapt to future requirements.

Second, scale AI beyond pilots. With 92% already profiting from AI but only 32% at scale, the execution gap represents an enormous competitive opportunity. Organizations should adopt ecosystem-level AI blueprints that consider the entire value chain, rather than optimizing individual process steps in isolation. This requires breaking down data silos, establishing clear governance frameworks, and accepting that the journey from proof of concept to enterprise deployment demands fundamentally different skills and organizational structures.

Third, invest in data as a strategic asset. Financial services already leads other sectors in data maturity, but the 28 percentage point year-over-year improvement in data accessibility demonstrates what is possible when investment is sustained. Treating data as products with specific business goals — rather than as a static resource requiring perfection — enables faster time to value and creates the foundation for advanced AI applications.

Fourth, build regulatory adaptability into the technology stack. With 75% of executives citing regulatory complexity as the top constraint on investment, organizations that can adapt quickly to new requirements will outperform those that cannot. Cloud-based, modular architectures, combined with AI-powered compliance tools and robust governance frameworks like ISO 42001, provide the flexibility needed to navigate an evolving regulatory landscape.

The financial services technology landscape in 2025 is defined by paradox: unprecedented opportunity coexists with formidable challenges. The organizations that will thrive are those that can hold this tension — investing boldly in AI, cloud, and modernization while managing risk, compliance, and talent constraints with discipline and strategic clarity. The KPMG Global Tech Report 2025 makes one thing clear: standing still is not an option.