The rapid advancement of AI is placing unprecedented demands on traditional data infrastructures forcing businesses in the banking, financial services and insurance (BFSI) sector to prioritise between security, quality and sustainability.
This is according to a new survey from Hitachi Vantara which is based on a study that covered 231 BFSI specialists, C-level executives and IT decision-makers spanning 15 countries across the globe.
The report found that while 36% recognise the importance of data quality for AI success, financial leaders’ focus remains on data security – leaving gaps in AI performance and long-term ROI.
“Financial institutions worldwide are accelerating AI adoption, but many are realising their data infrastructure isn’t ready to support it,” said Joe Ong, VP and general manager for ASEAN at Hitachi Vantara.
“This global research reflects what we’re also hearing in Southeast Asia — that the real barrier to AI success isn’t the technology itself, but the ability to manage data securely, accurately, and at scale,” said Ong.
He said that. Financial organisations must focus on strengthening their data foundations to ensure AI delivers real, sustainable impact.”
Nearly half (48%) of respondents cite data security as their top concern for AI implementation, reflecting the critical need to guard against internal and external threats.
Hitachi Vantara thinks this is understandable considering that 84% of respondents admit losing data to an attack or mistake would be catastrophic.
However, the study results showed that ignoring data quality comes at a cost for BFSI institutions.
In BFSI companies, data is only available when and where it is needed a quarter of the time (25%), and BFSI AI models are accurate only 21% of the time.
Also, 36% are concerned about the risk of a data breach from internal AI, and 38% are concerned about inability to recover data from ransomware.
Although ransomware attacks are top-of-mind for BFSI IT leaders, 36% say a data breach caused by AI making a mistake is a top three concern for them, and 32% are concerned an AI-enabled attack could cause a data breach.
“The business model in financial services is inherently tied to trust. Reputational harm is a significant risk, and so in our industry, the interaction between security and accuracy is a critical and complex challenge,” said Mark Katz, CTO of financial services at Hitachi Vantara.
“For instance, if a chatbot inadvertently discloses sensitive information that was included in the training data, that will have serious repercussions,” said Katz. “Additionally, the cost of a wrong answer or a hallucination poses a significant risk; if someone were to act on bad data, it raises all sorts of questions about liability.”
Despite accuracy challenges, AI adoption within BFSI is accelerating. However, many are deploying AI without adequate preparation.
Among respondents, 71% admit to testing and iterating on live implementations, while only 4% are using controlled sandbox environments.
The research confirms that financial services leaders are convinced that data quality is the most important consideration for successfully implementing AI, but concerns like security are too urgent to ignore, and ROI is suffering.
