AI in Banking: GFT's Perspective
Key Takeaways
As a high tech customer-centric industry where service quality, speed and efficiency lead to competitive advantage, AI has the potential to help banking firms significantly leapfrog the competition.
The market appears to be pointing AI at four specific challenges:
- Better customer experiences
- Improved process efficiency
- Increased process quality
- Enhanced technical productivity
All of these imperatives can have significant impact on competitive differentiation. Harnessing AI’s ability to workaround the clock, leverage vast swathes of historical case data, work at speed unmatchable by humans, to undertake mundane repetitive tasks without becoming distracted or bored at very high accuracy thresholds, and to dynamically apply rigour to process from a regulatory and security perspective, make it a highly attractive feature of the future service landscape of banks.
GFT has made significant investments through product development, partnerships and acquisitions, as well as working closely with our customers to realise these potential benefits. Along the way, we have learned many lessons and identified new opportunities to implement, accelerate and scale. The aim of this paper is to explore and share some of those learnings.
Download the paper to discover real productionised examples of work that we have undertaken across the industry.
See Our Full Video Here!
FAQ: AI in Banking - Key Questions Answered
How is AI transforming the banking industry today?
AI is transforming banking by improving customer experience, increasing process efficiency, enhancing risk detection, and boosting technical productivity. Banks are using machine learning and large language models (LLMs) to automate compliance monitoring, reduce false positives in misconduct detection, and deliver personalized digital experiences.
For example, AI-powered misconduct platforms have reduced false positives by up to 40%, while automated investment assessment tools have cut manual processing by more than 75%. These gains directly impact cost, scalability, and regulatory performance.
The full AI in Banking report outlines real-world production use cases and implementation lessons from global banks. Download the report to explore detailed case studies.
What are the most impactful AI use cases in banking?
The most impactful AI use cases in banking include fraud detection, misconduct monitoring, intelligent banking assistants, investment decision support, and developer productivity automation. These applications combine machine learning, behavioral analytics, and generative AI to improve accuracy and reduce operational costs.
For example, AI-driven behavioral analytics can identify weak signals of misconduct while maintaining compliance. Intelligent banking assistants leverage LLMs to provide personalized transaction insights and product recommendations - securely integrated with enterprise systems.
Our AI in Banking report provides detailed architecture patterns, governance insights, and measurable results from production deployments. Download the full perspective to see how banks are scaling these use cases responsibly.
How can banks implement AI securely and remain compliant?
Banks can implement AI securely by combining production-grade data environments, AI assurance frameworks, and strong governance controls. Moving from proof of concept to production requires continuous model monitoring, explainability, bias testing, and regulatory alignment.
Secure AI experimentation environments allow data scientists to work with sensitive production data under strict access controls. AI assurance ensures models remain accurate, fair, and compliant with evolving regulatory expectations. This is critical as AI regulation increases globally.
The AI in Banking report outlines a structured approach to AI governance, model risk management, and MLOps best practices. Download the report for a practical implementation framework.
