According to Experian’s global Connected Intelligence: Scaling AI with Trusted Data and Decisioning report, there is a massive gap between the number of institutions that are using AI and those prepared for it.
The survey found that 72 per cent of financial institutions are already using agentic AI, largely for assisting with decisions and supporting underwriters, with 75 per cent of respondents saying the technology is leading to faster decision cycles.
However, only 3 per cent of organisations said their data was completely AI-ready, while 67 per cent said their data was either only partially ready or not ready at all for agentic AI and its role in the decision-making process.
Poor data quality is a key concern for a large number of respondents, with 45 per cent saying fragmented data fails to provide a unified customer view.
Other major barriers to scaling the technology were poor data quality (42 per cent) and a lack of trust in the outputs that AI generates (31 per cent).
The survey also found that trust and governance issues were a major issue, according to 69 per cent of respondents. Transparency of insights and analytics was also a priority for 84 per cent of respondents.
“What we’re seeing with Australian lenders is that AI is already in underwriting workflows, with the research showing 72 per cent are using agentic AI for decision support, but the harder question being asked is how to trust and govern it at scale, especially as regulators sharpen their focus on how data is used. The foundations underneath AI still need to catch up,” said Experian Australia and New Zealand managing director of software solutions Mathew Demetriou.
Trust remains low in terms of AI autonomy, with only just over half (51 per cent) happy to trust AI to make decisions without oversight for low-risk cases. When discussing most use cases, that number drops to 2 per cent.
Still, the findings and known unpreparedness are not slowing the excitement for AI, with 92 per cent of respondents saying they would take on an AI vendor that can meet their data, AI and software needs for underwriting credit risk and fraud.
“Without trusted and well-governed data, there is a risk that organisations may struggle to operationalise AI at scale,” said Demetriou.
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