Practical Use of AI in Finance
Artificial intelligence is no longer an emerging technology in finance — it is an operational reality that is reshaping how financial functions operate, how decisions are made, and how costs are managed. For finance leaders, the question has shifted from whether to engage with AI to how to do so effectively and at what pace.
Current Applications in Finance Functions
The most mature AI applications in finance are in areas where data volume is high, processes are structured, and the cost of error is manageable. Accounts payable automation, bank reconciliation, expense management, and financial reporting are all areas where AI-driven tools are delivering measurable efficiency gains in practice, not in theory.
Fraud detection has seen some of the most significant AI investment in financial services, where real-time pattern recognition across transaction data has demonstrably improved detection rates. For corporate finance functions, the equivalent applications are in anomaly detection across procurement spend and accounts payable — identifying duplicate invoices, off-contract spend, and supplier pricing inconsistencies.
Cash flow forecasting is another area of genuine current value. Machine learning models that incorporate historical payment patterns, seasonal factors, and external economic signals are producing more accurate short-term forecasts than traditional methods. For businesses managing working capital tightly, this improved accuracy has direct financial value.
Where AI Is Not Yet Delivering
There is significant hype around AI capabilities that exceeds what is reliably delivered in practice today. Strategic financial planning, complex scenario modelling, and qualitative risk assessment all require human judgement that current AI systems cannot replicate with the reliability that finance functions need. Organisations that adopt AI in these areas without adequate oversight are taking on risks that are not always visible until they materialise.
The other consistent failure mode is data quality. AI systems are only as good as the data they process. Organisations with fragmented ERP systems, inconsistent data governance, or poor master data quality will find that AI tools amplify their existing data problems rather than solving them.
A Practical Approach
The finance functions that are extracting genuine value from AI share common characteristics. They have identified specific, high-volume processes where AI can reduce manual work. They have ensured data quality in those processes before deploying AI. They have maintained human review at critical decision points. And they have started small, measured outcomes carefully, and scaled gradually.
ERA Group’s technology procurement specialists work with finance leaders to evaluate AI tools for finance applications, ensuring that procurement decisions are based on demonstrated capability rather than vendor claims, and that commercial structures incentivise the delivery of actual outcomes.
