Australian financial leaders struggle with outdated data: study

A recent international study conducted by AI company ActiveOps has revealed that a significant majority of financial operations leaders in Australia are struggling to trust their data.

According to the findings, 92% of these leaders experience difficulties in extracting reliable insights from their data, while 33% are often reliant on data that is more than two months old.

The study highlights that all surveyed businesses find it considerably challenging to derive meaningful insights from their datasets, further indicating a pressing issue within the financial sector. Support for AI remains high among these leaders, with many recognising its potential to streamline operations. However, the data environment does not appear to be conducive to effectively leveraging AI technology.

The survey, which targeted Chief Operating Officers, Chief Financial Officers, and Senior Heads of Operations within Australia's financial services sector, underscores several key concerns. Retaining and supporting employees, increasing employee productivity, and improving sustainability were identified as the primary priorities for these leaders. Despite a strong belief (90%) that employee engagement would improve with access to real-time data, many are forced to operate with outdated information. Specifically, one in five have to rely on data that is a week old, while one in three deal with data that is more than two months old.

Anna Itsiopoulos, Managing Director, APAC at ActiveOps, commented on the necessity of having a strong data foundation for successful AI deployment: "Real-time data combined with AI-driven analytics can be a game-changer for the sector, but only if the right data environment is nurtured to support it. Data must be cleansed, classified and made securely accessible across the organisation in order for AI initiatives to perform well, otherwise the complexity of AI could do more harm than good. Businesses need to rewind, step back, and ensure they have a strong data foundation before they leap into AI or inch closer to their AI objectives." This statement underscores the importance of a stable and trustworthy data environment to harness the full capabilities of AI.

The report, titled "Ready Or Not AI Is Here," suggests that financial services institutions in Australia currently lack the appropriate data environment to support advanced AI initiatives. The consensus among respondents is clear: data must be up-to-date, correctly classified, and widely accessible rather than siloed within specific departments. A failure to achieve this leads to data irrelevance, an issue many Australian institutions are currently grappling with.

Although support for AI remains robust, with 45% believing that AI will enable more informed, real-time decisions, the lack of access to real-time trusted data acts as a significant roadblock. This gap fosters mistrust in the technology, leading to what the report terms "decision paralysis" due to diminished confidence in data and analytical outputs.

The survey also highlighted several critical findings: - 92% believe that if they had trusted data, they could make more effective decisions. - 90% think that employee engagement would improve with real-time data. - Half of the respondents believe AI will facilitate more real-time decisions. However: - 1 in 3 are forced to base decisions on data that is more than two months old. - 40% have no access to any form of real-time data. - 100% agree that it takes significant effort to get insights from their data.

This survey is not limited to Australia. The study, which also examined countries such as the US, Canada, New Zealand, and the United Kingdom, found that 98% of global respondents face substantial challenges when adopting AI for data processes.

The research methodology involved a survey conducted between February and March 2024 by Censuswide, targeting over 850 senior figures within the Financial Services sector across seven countries. The aim was to gather insights on their priorities for 2024, the challenges they face with operational data and AI adoption. ActiveOps asserts that these insights are crucial for understanding the current landscape and the necessary steps to optimise the implementation of AI in financial operations.