In banking as in other industries, AI is rapidly becoming a core business driver. The biggest gains will come from a foundational rethink of operations, not marginal improvements.
The financial sector is undergoing a profound transformation, powered by AI. Banks’ strategic integration of AI is moving beyond simple efficiency gains to make the technology a core business driver, focused on hyper-personalization, augmentation of human talent, and robust governance.
The real opportunity, says Andy Schmidt, vice president and global industry lead for Banking at CGI, [our AI in Finance judging partner], lies not in simply applying AI to existing workflows, but in fundamentally rebuilding processes with AI at the core.
A key aspect of this transformation is the shift towards an ultra-personalized and predictive customer experience. AI is moving past rudimentary chatbots to become an “agentic, conversational assistant” that can proactively anticipate a customer’s needs: from preventing payment failures by automatically increasing card limits to providing tailored financial guidance and real-time product recommendations.
Going forward, this intensified focus on customer experience will be a significant component of return on investment (ROI), Schmidt predicts.
“The real value comes in improved customer experience,” he stresses. “Being able to onboard customers more quickly, being able to transition from opportunity to revenue more quickly, and optimizing the customer experience so that they remain satisfied and stay with the bank over time.”
Schmidt highlights success stories in wealth and personal finance where GenAI drives personalization recommendations. DBS Bank’s harnessing of AI, for example, has drastically accelerated customer journeys, demonstrating the potential for significant scale and opportunity.
Human-AI Augmentation
The case for AI adoption in banking centers on strategic augmentation, were AI becomes a co-pilot for human experts. The goal is to automate repetitive and low-value tasks, freeing up human capital to focus on such complex, high-value activities as strategic decision-making, advisory sales, and conflict resolution.
Further driving this internal empowerment is the democratization of GenAI tools across the workforce, accelerating research, analysis, and data synthesis. Crucially, banks must commit to the principle of human oversight, ensuring that for complex matters, a human being is always in the loop and remains the final decision-maker.
AI’s role in risk management is evolving from reactive analysis to real-time, predictive analytics. By continuously monitoring vast internal and external data streams, AI can anticipate potential risks and perform complex what-if scenario planning. This capability couples with enhanced fraud detection, where sophisticated AI, including neural networks, provides real-time surveillance and prevention across massive transaction volumes.
AI is also streamlining the traditionally costly and time-consuming realm of regulatory compliance. Schmidt emphasizes the value of AI in bringing “transparency, auditability, and repeatability to key processes, especially when it comes to compliancerelated processes like KYC [know your customer].” Relatedly, AI is automating tasks like credit report preparation and enhancing the rigor of due diligence on complex M&A transactions.
Maximizing ROI Gain
A significant lesson emerging from AI deployment is that the most substantial returns come from a foundational rethink of operations, not marginal improvements. The financial industry is recognizing that “adding AI to existing processes will make them marginally better,” Schmidt notes, but that “optimizing processes to leverage AI will make them dramatically better.” The best way to realize the benefits of AI transformation, he adds, is in “examining these long-standing processes, optimizing them, and fundamentally rebuilding them. The goal is to integrate AI at the core of the process, rather than sprinkling it on top as an afterthought.”
With every aspect of AI adoption, however, the best approach is to proceed in stages. For those beginning their AI journey, Schmidt suggests adopting large language models (LLMs) as a starting point before transitioning to more specialized, purpose-built models. The effective integration of AI requires continuous change management to sustain capabilities and maximize ROI over time.
Methodology
The Global Finance AI In Finance award winners are chosen based on entries provided by financial institutions. Entrants are judged on the impact, adoption, and creativity that AI brings to both systems and services. Winners are chosen from entries submitted by banks and evaluated by a world-class panel of judges at CGI, a leading multinational IT and business consulting-services firm. CGI is a trusted AI expert that combines data science and machine slearning capabilities to generate new insights, experiences, and business models powered by AI. The editors of Global Finance are responsible for the final selection of all winners.
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