Financial leaders prioritise responsible AI for greater ROI impact
A new study by FICO and Corinium has found that financial institutions worldwide are now prioritising responsible AI standards over generative AI technologies in pursuit of better return on investment (ROI).
The global survey, which collected responses from more than 250 senior executives such as Chief Analytics and AI Officers, Chief Technology Officers, and Chief Information Officers, explored evolving AI investment strategies, operational readiness, and oversight in the financial sector.
Responsible AI emphasis
The report underlines that more than 56% of Chief Analytics and AI Officers see responsible AI standards as a primary driver for improved ROI, putting these standards ahead of technologies like generative AI, which garnered 40%. Leaders surveyed view responsible AI as crucial for competitive differentiation. Dr. Scott Zoldi, Chief Analytics Officer at FICO, said:
"Responsible AI extends beyond risk mitigation - it's a business imperative. Over half of CAOs and CAIOs (56%) believe that implementing Responsible AI standards will significantly impact ROI. Meanwhile, human-AI collaboration is key, with 44% of surveyed leaders identifying it as an exciting area for future development. To ensure accountability and reduce AI hallucinations, organizations must clearly define the boundaries and interactions between human oversight and AI capabilities."
According to the survey, only 40% of executives highlighted generative AI and large language models as substantial avenues for ROI improvement. Respondents cited decision intelligence platforms as a leading investment priority and pivotal for integrating AI into business operations, with features such as explainability and traceability noted as especially valuable.
Implementation and integration gaps
The findings indicate that just 12% of organisations have fully embedded operational standards for AI. The study identifies a gap between ambition and execution regarding the adoption of responsible AI systems; standards such as bias mitigation, performance monitoring, and secure data handling are flagged as fundamental but inconsistently implemented.
The data also show caution regarding generative and agentic AI. Eleven percent of surveyed leaders expressed concerns about the unpredictability and instability of agentic AI, citing issues related to technical immaturity and challenges with establishing measurable business outcomes.
Paola Leites de Moraes, Production Director for Financial Services at Corinium, commented on the shifting landscape, saying:
"By focusing on technologies that enhance strategic decision-making and trust, today's businesses are heralding the arrival of a mature AI landscape that places more importance on accountability and oversight and less on hype-driven investments."
Platform collaboration
The report also highlights the perception that collaboration and unified platforms can significantly enhance AI effectiveness. More than 75% of respondents believe that enhancing cooperation between business and IT leaders through a standard AI platform could lead to ROI gains of 50% or greater. Traditionally, siloed development and model building have resulted in duplicated effort and misaligned priorities. The push towards unified decisioning platforms aims to consolidate these processes for improved performance and oversight.
Barbara Widholm, Vice President of Automation and AI at State Street, outlined practical challenges organisations face:
"From my perspective, the lack of a unified platform and process often leads to duplicated efforts, inconsistent tooling, and misaligned priorities. AI initiatives may be technically sound but fail to scale or integrate due to infrastructure gaps or unclear ownership."
Alignment challenges
Despite growing investment and awareness, the report reveals that nearly 95% of organisations struggle with full alignment across AI initiatives, development, infrastructure, and business strategies, with only 5% of CAOs and CAIOs reporting complete alignment. This widespread issue highlights difficulties in transitioning AI systems from proof of concept to profitable operations.
The success of AI-driven business transformation depends not only on technical capability, but on organisational change and strategic planning. That includes breaking down internal silos, aligning teams, and ensuring responsibility for AI initiatives is clearly defined to deliver measurable outcomes.
The FICO and Corinium report suggests that unlocking the full potential of AI in financial services requires the enforcement of robust standards, the adoption of integrated systems, and greater cross-functional collaboration among business and technology leaders.
