Software engineering is the most mature AI application in the financial industry and is a primary cyber risk transmission vector, with 48% of respondents flagging adversarial AI as a top concern. This is reinforced by the recent news that Anthropic’s Mythos model is often more capable than humans in hacking, making manual oversight of AI use in financial services problematic.
“Further complicating this problem space is a notable perception gap: AI vendors place less priority than industry and regulators on both adversarial AI threats (35% versus 50% industry, 57% regulators) and cyber/operational resilience (32% versus 46% industry, 59% regulators),” says the 2026 Global AI in Financial Services Report: Adoption, Impact and Risks.
Intersecting vulnerabilities on data protection and privacy
“These intersecting vulnerabilities can also feed into the top perceived risk across all stakeholders – data privacy and protection (73% of respondents) as sensitive data is typically the primary target for the cyber exploits these vulnerabilities enable,” the report adds. Other top AI risks identified include model hallucinations, unreliable outputs, model opacity and lack of explainability, and market abuse.
Says Bryan Zhang, Executive Director of the Cambridge Centre for Alternative Finance:
“The scale and pace of AI adoption in financial services is genuinely remarkable – 4 in 5 firms are already deploying AI at some level, agentic systems have crossed into the mainstream and real productivity and profitability gains are being felt across the industry, although unevenly.
“But our data also reveals a sector navigating a very fluid and complex landscape, with fragmented views expressed by the industry, regulators and Al vendors on issues such as accountability when things go wrong, and risks such as cyber vulnerabilities are compounding faster than they can be humanly overseen. The opportunity is enormous. So is the responsibility to get the governance right and strengthen trust.”
The scale and pace of AI adoption in financial services is genuinely remarkable – 4 in 5 firms are already deploying AI at some level, agentic systems have crossed into the mainstream and real productivity and profitability gains are being felt across the industry, although unevenly.
Big gap between AI experimentation and firm-wide implementation
The survey results signal a “deep execution gap between early-stage experimentation and institution-wide AI integration”, with most financial-services industry AI use cases being back-office functions such as software engineering and data management rather than AI-powered customer support.
Fintechs lead incumbent financial services providers in using AI in customer support, with 76% of respondents in large financial institutions finding it hard to measure the value of AI deployment.
“Overall, AI is primarily being used currently to improve execution, throughput and service rather than to fundamentally reconfigure business models, though 51% of more mature AI adopters are piloting or deploying new financial products powered by AI,” says the report. This signals “a potentially significant execution and business integration gap” regarding AI use in financial services.
Says Kieran Garvey, Lead in AI at the Cambridge Centre for Alternative Finance:
“What this study shows is a sector in genuine transition. AI is already delivering real efficiency gains – in operations, in software development, in customer-facing services – and more mature adopters are beginning to use it to create entirely new financial products. However, the same capabilities driving those gains are also creating or exacerbating risks from model hallucinations and biases, data protection and privacy, lack of explainability, herding, third-party dependency and adversarial threats. How we collectively manage and mitigate these risks will shape the future trajectory of digital financial services.”
What this study shows is a sector in genuine transition. AI is already delivering real efficiency gains – in operations, in software development, in customer-facing services – and more mature adopters are beginning to use it to create entirely new financial products.
Industry is far ahead of regulators in AI adoption
The report finds that industry respondents are far ahead of regulators in adoption and deep adoption of AI, with 48% of the 130 regulatory authorities surveyed reporting they are “still in the ‘exploring’ stage for AI adoption or not engaged with AI at all”.
Most organisations surveyed said they are building off external AI models rather than training from scratch, with OpenAI the most-used foundation model provider across all groups (76% of industry and 48% of regulators), followed by Google and Anthropic.
Report based on insight from 628 respondents
The report is based on insights from 628 respondent organisations, including 203 fintechs, 149 financial incumbents, 146 AI vendors and 130 central banks and other financial regulators across 151 jurisdictions around the world.
The global research was conducted by the Cambridge Centre for Alternative Finance, in partnership with Financial Innovation for Impact (FII), the Bank for International Settlements (BIS), the International Monetary Fund (IMF), the World Economic Forum (WEF), the Inter-American Development Bank (IDB), CGAP, the Arab Monetary Fund (AMF) and with the support of the UK Foreign, Commonwealth and Development Office (FCDO).
Measuring the value of AI deployment is difficult for many in financial services
Other findings in the report include:
1
AI is boosting productivity, but enterprise value remains hard to prove
Productivity effects are already felt, but enterprise value remains harder to evidence. Positive productivity impacts brought about by AI are perceived to be highest in technology, data, and product functions (79%), followed by back-office and operations roles (75%) and front-office roles (69%). “However, 55% of industry respondents and 63% of surveyed regulators find it difficult to measure the value of AI deployment, rising to 76% among large financial institutions.”
2
Profit gains from AI are emerging, but far from universal
Profitability outcomes are positive but uneven, correlating with AI investment and workforce preparedness. Only 40% of respondents report increased profitability from AI, while 43% report no change.
3
Operational efficiency leads expectations, while regulatory clarity lags
Industry, vendors and regulators all identify greater operational efficiency as the top expected benefit of AI by 2030 (73% of industry, 66% of vendors, and 56% of regulators). There is also broad alignment on the need for clearer regulatory guidance, ranked as a top priority by 69% of industry, 67% of vendors, and 79% of regulators.
4
Data, skills and legacy systems continue to limit AI scale‑up
Data quality, talent, and legacy architecture remain the core constraints to adoption and scaling. Data availability and quality remain the leading pain point hindering AI adoption, cited by 66% of AI vendors, 46% of regulators, 40% of industry and 34% of fintechs.
5
AI adoption is widening the gap between advanced and emerging economies
There is an economic divide in AI adoption in financial services, with advanced economies nearly 7 times more likely to reach the transforming stage in AI use compared to emerging markets and developing economies.
Download the report
‘2026 Global AI in Financial Services Report: Adoption, Impact and Risks’ was produced by the Cambridge Centre for Alternative Finance (CCAF) in collaboration with Financial Innovation for Impact.
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