Artificial intelligence (AI) adoption in the financial services industry has risen to 81 percent, indicating it has effectively entered a stage of widespread use. Profitability and improvements in operational efficiency were driven more by an organisation's AI maturity, the level of technological advancement and the size of investment than by adoption itself.

Fintech News Switzerland reported on May 6 that the Cambridge Centre for Alternative Finance, under the University of Cambridge Judge Business School, released research findings on the status of AI adoption in the financial sector.

The survey was conducted from October 2025 to January 2026 across 628 financial institutions, AI vendors and regulators in 151 regions. Overall, 81 percent of respondents said they had adopted AI in some form, and 40 percent said their level of AI use had reached the 'scaling' or 'transforming' stage. This shows the financial sector is accepting AI as a core technology in efficiency, risk management and customer personalisation.

Gaps by industry segment were also confirmed. Fintech companies moved faster on AI transformation than traditional financial institutions. The share that reached the 'transforming' stage was 19 percent for fintech, more than triple the 6 percent for incumbent financial firms. Incumbent firms, meanwhile, were more concentrated in early adoption stages, with 21 percent in the 'exploring' stage and 44 percent in the 'piloting' stage. Unlike fintech with a digital-centric structure, legacy systems, complex integration and security requirements at traditional financial firms acted as hurdles to wider rollout.

By technology, traditional machine learning was used most widely. Some 75 percent of respondents said they had adopted machine learning and were using it for fraud detection, credit screening and anti-money laundering (AML) suspicious transaction detection. Adoption of generative AI also rose quickly to 71 percent. More recently, adoption of agentic AI, which autonomously performs multi-step tasks with set goals, reached 52 percent. Applications are expanding into areas such as autonomous trading, dynamic portfolio rebalancing and real-time risk mitigation.

Within financial firms, AI use was focused first on operations and back-office functions. The most mature and widely used application was process automation, with a 79 percent adoption rate, while data visualisation and software development were each at 75 percent. In the front office, AI-based customer support was highest at 73 percent. Sales, customer relationship management and strengthening customer touchpoints followed at 67 percent, with marketing and personalisation at 64 percent.

Results diverged clearly depending on the level of AI adoption. Of companies with high AI maturity, 64 percent said profitability improved, compared with 33 percent among companies with low maturity. Some 56 percent of fintech companies said they experienced productivity gains, but financial institutions were at 34 percent.

Investment size also emerged as a factor separating outcomes. Among companies that invested at least $100,000 in the most recent fiscal year, 61 percent said they confirmed an increase in profitability. That compared with 40 percent among companies that invested less than $100,000. The share reporting improved profitability was 54 percent among companies using AI models they built in-house or fine-tuned, higher than 39 percent among those relying on general-purpose products or external vendor solutions. The analysis said AI's financial impact is more sensitive to control and investment intensity than to adoption itself.

Employment impact still appeared limited. Some 74 percent of respondents said there had been no clear decrease or increase in headcount due to AI adoption over the past 3 years. In the outlook for 2030, more weight was placed on retraining and role transitions than on layoffs. Some 25 percent expected workforce retraining and transitions, and adding the 10 percent that expected a net increase brings the total to 35 percent that saw AI changing jobs or having a positive impact. By contrast, one-quarter of respondents expected a net decline in jobs by 2030. The payments segment was seen as the most pessimistic area, with 21 percent forecasting a sharp decrease.

Separate from the pace of rollout, bottlenecks were also clear. The biggest issue was data availability and quality. Some 66 percent of AI vendors, 46 percent of regulators and 40 percent of industry respondents cited data issues as a key obstacle. Vendors viewed data quality and completeness as the biggest difficulty when collaborating with clients. Legacy systems, fragmented work environments and limits on data sharing were also cited as major constraints. Regulators pointed to a lack of AI education and capability-building at 48 percent, talent shortages at 47 percent and technology and infrastructure limits at 45 percent as key constraints.

Against this backdrop, the next competition in finance is shifting from whether AI has been adopted to how deeply it has been embedded. The performance gap between financial firms and fintech is also widening at the same point. As generative AI and agentic AI spread rapidly, how much data quality and internal capabilities are strengthened remains a variable that will determine actual improvements in profitability and productivity.