In the financial services industry, artificial intelligence (AI) has evolved into a mainstream standard, with machine learning (ML), and generative AI (genAI) emerging as the most widely adopted technologies in the category, according to a new study by the Cambridge Centre for Alternative Finance (CCAF) at Cambridge Judge Business School, University of Cambridge. However, the realization of profitability gains and operational improvements depend heavily on an organization’s AI maturity, sophistication, and investment levels, the research found.

Released in April 2026, the study was conducted between October 2025 and January 2026, and captured insights from 628 financial institutions, AI vendors, and regulatory authorities operating across 151 jurisdictions.

Results highlight the widespread adoption of AI in finance, with 81% of industry players now adopting AI at some level and 40% reporting advanced AI adoption, including “Scaling” or “Transforming”. This underscores the industry’s recognition of AI’s critical role in enhancing efficiency, risk management, and customer personalization.

Fintech firms lead in adoption

Findings show a clear divide between fintech companies and incumbents, with fintech firms leading in AI adoption and being more than three times more likely as traditional financial institutions to have reached the “Transforming” stage at 19% versus 6% for incumbents.

Conversely, incumbents show higher shares of “Exploring” (21%) and “Piloting” (44%), underscoring slower progression through the AI adoption maturity curve.

This disparity reflects the fact that fintech firms are digital-first, more agile adopters of new technologies, whereas traditional financial institutions often face organizational inertia, legacy complexity and more demanding integration and security requirements that complicate the path to scaling deployment.

ML and genAI as primary frontiers

Looking at specific technologies, the study found that classical ML is the most widely adopted AI technology among the financial services providers, embraced by 75% of respondents. These systems learn statistical patterns from labeled historical data and are commonly applied to fraud scoring, credit underwriting, and anti-money laundering (AML) anomaly detection.

However, several newer technologies are rapidly scaling. GenAI, in particular, is recording an adoption rate of 71%. GenAI involves large ML models trained once on a vast corpus of text, code, or other data, and then adapted to many downstream generation tasks.

Additionally, agentic AI has emerged as a booming frontier technology, with 52% of industry respondents actively adopting it. This demonstrates rapid uptake in a relatively short period of time. Agentic AI refers to systems that pursue objectives through autonomous, multi-step sequences of actions. Common applications include autonomous trading, dynamic portfolio rebalancing, and real-time risk mitigation.

AI deployment in financial services

The research also looked at the deployment of AI within financial institutions, and found that AI is mostly used in operational and back-office functions. The most mature and widely adopted use cases globally are process automation, data visualization, and software development, with adoption rates of 79%, 75%, and 75%, respectively.

Within the front-office, AI-powered customer support leads at 73%, followed by sales, customer relationship management (CRM) and outreach at 67%, and marketing and personalization at 64%. These applications primarily support client relationship management and enhance customer acquisition strategies.

The impact of AI

Findings from the study also show that AI adoption is increasingly generating measurable improvements across financial services, especially regarding productivity. The strongest gains were observed in technology, data, and product functions, where 79% of respondents reported positive outcomes. Back office and operations followed closely at 75% overall.

Crucially, the research found strong correlations between AI maturity, sophistication, and spend. 64% of more mature adopters of AI reported increased profitability compared with 33% of less mature firms. Similarly, 56% of fintech firms recorded productivity gains compared with 34% of financial institutions, a divergence which aligns with the 17% maturity gap in advanced AI adoption between fintech firms and finance incumbents.

Investment levels also play a pivotal role, with 61% of firms that invested over US$100,000 in the most recent financial year observing increases in profitability compared to 40% of firms spending less than US$100,000.

Furthermore, firms with fully in-house or fine-tuned AI models reported higher profitability gains at 54% compared with those relying on off-the-shelf or vendor-built solutions at 39%.

Taken together, these findings indicate that realizing financial value from AI may depend less on adoption alone and more on organizational maturity, technical capability and the level of control over AI development.

Workforce implications of AI and future outlook

Regarding the impact of AI on employment in the financial services sector, the results show that the actual effect on headcount has remained very limited for the last three years, with 74% of respondents reporting that no significant job losses or gains have been observed due to AI implementation.

Looking ahead to 2030, the industry expects structural transformation rather than simple contraction. 25% of firms expect “Reskilling and Transformation” of the workforce. Combined with the 10% of respondents expecting a net increase, a total of 35% of the industry anticipates a future where job roles are transformed through reskilling or positively impacted by the use of AI.

Nevertheless, a quarter of firms predict a net reduction in jobs by 2030, with the payments sector being the most pessimistic, with 21% of respondents projecting a significant decline.

Challenges to AI adoption

Despite growing AI adoption, the CCAF study also highlights persistent challenges, especially around data quality, fragmented systems, technology and infrastructure challenges, and limited institutional capabilities.

Data availability and quality are the leading pain point hindering AI adoption, cited by 66% of AI vendors, 46% of regulators, 40% of industry participants. Vendors also reported specifically acute data-related challenges when working with their clients, with 72% citing data quality and completeness, 46% legacy systems and siloed environments, and 41% reporting data-sharing restrictions.

For surveyed regulators, lack of AI training and capacity building (48%), talent (47%), and technology and infrastructure (45%) are also core constraints for AI adoption in addition to data issues.

Featured image: Edited by Fintech News Switzerland, based on image by tamirt via Magnific