Southeast Asia finance professionals rank GenAI as top future skill as training lags

Generative AI has become the highest-ranked future skill among finance professionals in Southeast Asia, even as current proficiency levels and access to formal training remain limited.

A survey by ACCA and Chartered Accountants Australia and New Zealand found that 39% of respondents in the region placed GenAI tools among the three most important skills they will need over the next five years. Data visualisation followed at 34%, with process automation at 29% and the ability to explain data clearly to senior leaders at 27%.

A further 25% selected the creation and monitoring of real-time dashboards, while 24% identified predictive analytics. Technical capabilities such as database management and programming ranked lower, selected by 16% and 11% of respondents, respectively.

These findings appear in the Enabling finance insight report, which draws on responses from 1,600 finance professionals globally.

Training has not kept pace with demand

The regional demand for GenAI skills contrasts sharply with low levels of current proficiency. Across the global survey, 72% of respondents reported having basic or no GenAI skills, prompting 41% to pursue training in their own time.

This leaves many finance professionals preparing for the wider use of AI without structured support from their employers. While informal learning may help employees become familiar with individual tools, it provides less preparation for checking the accuracy of generated information, identifying incomplete data, or assessing whether an output can be trusted.

CA ANZ Chief Executive Officer Ainslie van Onselen said, “AI is now a core part of the finance toolkit, but it’s not a shortcut. CFOs and finance teams need to use it to sharpen judgement and generate real value, not just speed up old processes. That means investing in structured learning and working more closely with IT and data teams. Upskilling isn’t optional. It’s how you manage the risk.”

ACCA is also introducing professional certificates in data analytics, cybersecurity, AI, and organisational transformation for finance professionals. The data analytics and cybersecurity programmes are already available.

Concerns over AI reliability remain widespread

Training needs are closely tied to anxieties regarding the quality of AI-generated information. The survey found that 93% of respondents were worried about the integrity and verifiability of insights produced by AI.

These concerns included hallucinations, inaccurate results, incomplete datasets, limited transparency, and bias. Finance teams therefore need to understand how to check generated information, question its assumptions, and determine whether it is suitable for use in business decisions.

Problems with the underlying data add another layer of risk. Data quality and a lack of appropriate skills were each cited by 42% of respondents as barriers to producing better business insights, while 40% reported difficulty combining information from multiple sources.

These limitations can weaken the quality of forecasts, reports, and recommendations before AI is introduced. Automated tools may process information more quickly, but they still depend entirely on the accuracy and completeness of the data they receive.

Finance teams are working more closely with data and IT

Almost 60% of respondents reported close collaboration between finance, data, and IT teams. That cooperation has become increasingly important as finance functions use information from across an organisation to support planning and decision-making. Finance professionals bring knowledge of reporting, controls, and business requirements, while data and IT teams manage the systems and information needed to produce those insights.

Strategic priorities were the most commonly cited reason for increased data analysis, selected by 45% of respondents. Regulatory requirements followed at 43%. The findings indicate that finance roles increasingly require a combination of accounting knowledge, data interpretation, communication, and governance. Technical skills such as programming remain useful for specialised positions, but many finance professionals are expected to assess outputs, explain findings clearly, and work across departmental boundaries.

Formal training will therefore need to cover both the use of GenAI tools and the judgement required to evaluate their results. Without those skills, organisations may produce analysis more quickly while remaining exposed to inaccurate or poorly supported conclusions.