Artificial intelligence is accelerating productivity at Deutsche Bank, enabling tasks that once took years to be completed within months, a senior executive said on Thursday.
The German lender is using AI to speed up technology projects and tackle a backlog of internal work, but it is keeping a wary eye on rising computing costs.
"We're seeing things that were two years that are now getting done in three to six months... we know the productivity is there," said Denis Roux, chief information officer, investment bank at Deutsche Bank, speaking on the sidelines of the bank's Bank on Tech event in Bengaluru, India. He declined to quantify the impact.
Backlogs that once took months are now being cleared in weeks, Roux said, adding, "All I'm hoping to do is use these tools to continue to make things more efficient."
The bank has around 9,000 employees in its technology function in India, accounting for about 45% of its global tech workforce. Global firms are increasingly using their Indian hubs for higher-value functions, including finance, software development and R&D.
Still, managing the cost of AI adoption is a priority as providers move to usage-based pricing models, Roux said, likening it to the discipline companies developed during their move to cloud computing.
AI firms such as Anthropic and OpenAI are increasingly shifting to token-based pricing that charges customers based on usage, instead of a subscription-based service.
Engineers at Deutsche Bank are allocated quotas for tokens and can request additional capacity but have to demonstrate value, with learnings then shared across the organisation, Roux said.
"We monitor the usage patterns... we don't want to slow people down and want them to keep going, but we also want to get a return," he said.
The bank is also developing AI tools to automate tasks like extracting and analysing financial data, as well as applications that link external events such as geopolitical or market developments to its portfolio to understand exposure.
Roux said the bank remains cautious about deploying AI for everything, using simpler models for routine tasks and also assessing where traditional solutions may be more effective.
