Technologies such as AI, blockchain and tokenisation are rapidly redefining the front-to-back office value chain in the corporate and investment banking (CIB) sector, from algorithmic credit assessments and ESG-linked financing to decentralised settlement platforms.
AI is affecting the industry on a huge scale, with numerous banks moving beyond experimentation to full operational deployments that are reshaping critical business areas.
“AI is rapidly transforming corporate banking across strategic, operational and customer-facing dimensions,” says Mike Harvey, Absa CIB investment banking managing executive.
On the front end, AI is enhancing client engagement, with applications such as AI-powered chatbots and digital advisory tools offering real-time support, tailored intelligence and seamless transaction updates.
AI is also streamlining operations at the back end through process automation, boosting productivity and freeing capacity by simplifying routine and time-intensive tasks and taking over repetitive tasks, allowing staff to focus on higher-value initiatives.
Harvey references a recent McKinsey report, which paints an optimistic picture of how AI could transform the average asset manager function, estimating that agentic AI could equate to 25%-40% of its cost base.
“Improvements in distribution flows, automated compliance and accelerated software development are already under way, but this is likely just the first phase in the reimagination of asset management,” says Harvey.
“The rise of predictive analytics, where informed decisions are made by analysing vast swathes of data, is particularly relevant for those seeking to allocate scarce capital with precision.”
In digital banking, a notable innovation from Absa CIB is a gold-backed stablecoin, designed for cross-border currency flows.
“Traditionally marked by friction, these transactions are now near instantaneous in a blockchain environment, provided compliance checks are completed.”
Nic Andrew, managing executive: asset management at Nedgroup Investments, sums up the state of AI adoption in the sector, highlighting that asset managers are engaging with AI at differing levels of maturity.
“Some are exploring targeted and isolated use cases to boost productivity, while others are embedding AI more deeply across research, portfolio construction, compliance and client engagement,” says Andrew.
“The most successful managers typically have senior executive buy-in, encourage widespread use and upskilling of the whole business, and have defined clear processes to identify workflows that can benefit from AI.”
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