Banks Expect Cost Efficiencies with AI, But They’ll Have to Pay Dearly to Keep It Running

Financial institutions implementing artificial intelligence (AI) for streamlined operations may face a hidden cost: data management.

A recent survey by SaaS firm Alveo suggests that automation’s potential profit gains could be offset by expenses associated with managing the vast amount of high-quality data required for AI models to function effectively.

Data storage fees, security measures and ongoing maintenance can all turn out to be very costly.

“As automation reduces the human role in data workflows, good quality data becomes even more critical,” said Martijn Groot, VP Marketing and Strategy at Alveo, highlighting a key finding from the survey: 63% of decision-makers at financial services firms anticipate increased data spending due to AI.

Automation might allow financial instituions to lay off tellers and other kinds of workers, but they’ll need to hire data scientists, AI model developers and data management experts.

This shift is crucial for building the data management technology infrastructure required for AI success.

Alveo’s survey also found that technological limitations (50%) and lack of skilled personnel (46%) are seen as major roadblocks to implementing AI in financial data management.

Financial services firms are already leveraging AI for various aspects of data management, including risk management (55%), client data management (49%), portfolio management (47%) and master data management (46%).