Investment banking, just as every other segment in financial services, is being fundamentally reshaped by the rise of AI agents, according to Ravi Khokhar, Global Head of Cloud for Financial Services at Capgemini.
“The potential is simply too hard to ignore,” he told Traders Magazine.
Recent data from the Capgemini Research Institute shows that AI agents could deliver up to $450 billion in economic value by 2028, signaling the opportunity that exists for the financial services industry.
To capitalize on this opportunity, 33% of banks say they are developing their own AI agents in-house, while 48% of financial institutions are creating new roles for employees to supervise agents.
“On the trading side, AI agents can process real‑time market data and sentiment in milliseconds, continuously learning and optimizing strategies, 24/7 without any breaks,” Khokhar said.
Across risk management, instead of periodic checks, agents provide continuous monitoring of credit, market, and operational risks, he said.
“They use predictive analytics to anticipate exposures before they materialize. Effectively, embedding governance into every transaction,” Khokhar said.
“And when it comes to deal execution – one of the most inefficient processes that executives highlight across capital markets – AI agents are equipped to do the heavy lifting,” he said.
“It can streamline due diligence by automating document review, compliance checks, and financial modeling, cutting timelines drastically,” he added.
According to the Capgemini Research Institute’s World Cloud Report in Financial Services 2026, the top processes for banks to deploy cloud-native, AI agents at scale include customer service (75%), fraud detection (64%), loan processing (61%) and customer onboarding (59%).
“When you combine all this intelligence with global cloud infrastructure, it is easy to understand why two-in-three C-suite executives firmly believe cloud-based orchestration is critical to their AI strategy,” commented Khokhar.
“It underscores the need for scalable, secure, and intelligent platforms ensuring seamless collaboration across geographies and systems,” he added.
AI agent adoption is poised for rapid growth as 80% of financial services firms are in the ideation or pilot stage of deployment, according to the findings.
However, a sizable opportunity remains to be unlocked as only 10% of firms surveyed have implemented AI agents at scale.
“In my view, I am increasingly hearing that the barriers are less about the technology but rooted in execution. Firms are wrestling with two critical roadblocks: regulatory and compliance challenges alongside a lack of skilled talent from leadership to employee level,” commented Khokhar.
“With evolving governance standards and jurisdiction specific rules, 96% of firms tell us that uncertainty slows adoption,” he said.
The global regulatory landscape is becoming increasingly fragmented, with region-specific nuances varying from North America to Asia-Pacific, he added.
“Clients are also wrestling with talent constraints: nearly all firms are struggling with a lack of AI skills among leaders and employees, which limits internal capability and increases reliance on external vendors,” Khokhar said.
“What gives me hope is that firms are looking to overcome these behavioral challenges by intentionally reskilling employees. This tells me that financial institutions want to keep this intellectual knowledge in-house – using AI agents to augment their human workforce,” he added.
According to Khokhar, early adopters of AI agents are already seeing a first-mover advantage.
“They view agents beyond just efficiency gains, with a focus on real business outcomes and re-imagined business processes,” he said.
In market analytics, cloud-native AI agents deliver multi-dimensional insights in real-time – analyzing trends, sentiment, and liquidity at scale, he said.
“Fraud detection is where agents demonstrate remarkable potential to reduce financial and reputational risk across the sector,” Khokhar said.
“By continuously learning from evolving fraud patterns, the models can simulate emerging typologies and identify fraud across datasets before it occurs,” he said.
“Dynamic pricing may be the most exciting. Agents can intelligently learn and process live market data, competitor actions, customer behavior to put forward hyper-personalized pricing. The result: higher margins and improved rates of client acquisition,” he added.
For banks to scale AI-driven operations, the “path forward is simple”, according to Khokhar.
“Embed governance into the architecture itself,” he said.
“For people to trust AI agents, investment banks must be able to articulate its boundaries and know that it will only operate within them,” he said.
“They must be designed with explainability, bias detection, and audit trails so every decision is transparent and accountable,” he added.
Khokhar stressed that cloud platforms play a significant role moving beyond just an infrastructure provider.
“With region-specific data residency, encryption, and compliance certifications, they allow banks to expand globally while meeting local regulatory requirements,” he said.
“Highly regulated industries, like financial services, must rally all parties – employees, legal, compliance, and regulators – for a successful rollout,” he said.
“Until this is addressed, most firms will remain stuck at the proof-of-concept stage – where the technology is proven to work in controlled environments – but cannot be scaled across the organization,” he concluded.
