Transforming Banking with GenAI

The banking sector is set for significant transformation, driven by the rapid evolution of artificial intelligence. While it has long played a role in banking - from enhancing algorithmic trading to credit scoring, risk assessment, and more - generative AI (GenAI) now offers exciting new opportunities. GenAI models differ as they excel in understanding natural language, contextual analysis and pattern recognition in large and often unstructured datasets. It’s a technology that could add $200-340 billion annually to global banking, with leading banks already seeing lower cost-to-income ratios, streamlined operations and staff more able to focus on complex, value-add tasks. 

With AI spending in the sector set to hit $400bn next year, effective adoption hinges on careful integration. Moving from experimentation to full-scale deployment is no small feat and navigating regulatory requirements, mitigating biases and breaking down data silos are all crucial steps that need to be taken. Implementation requires careful navigation, particularly when advanced applications like fraud detection and predictive analytics may bring additional security and governance risks. However, organisations can no longer afford the slow pace of past implementations. With mounting pressure to accelerate migration and data transformation, leveraging AI solely in its final state is insufficient; it must be embedded throughout the entire process.  

This Financial Times webinar, in partnership with PwC, was the banking edition of the FT’s AI Transformation Series. The discussion explored the latest GenAI applications in the banking industry, discuss emerging techniques such as synthetic data and self-supervised learning, and provide strategies for navigating the rapidly evolving AI landscape.  

Revolutionising Core Banking

From automating routine processes, enabling real-time analytics, and offering hyper-personalised customer experiences, how is GenAI transforming core banking operations? Where are the untapped opportunities for innovation and key limitations?  

Building the foundation

Every AI project must start with a robust data strategy - how can banks ensure the accuracy and integrity of their data, break down existing silos, and establish secure and scalable governance frameworks?  

Reskilling for the future 

How can investment in reskilling initiatives prepare workforces for a blend of technical proficiency and critical, adaptable thinking? Can strategic partnerships with tech firms further support this transformation?  

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