AI Models in Finance: Current Limitations and Future Potential

What's Happening?

AI models, such as ChatGPT-5, are advancing rapidly but are not yet suitable for finance-grade applications. These models lack the necessary context and integrity to handle financial tasks accurately. For instance, they do not inherently understand accounting principles like the necessity for debits to equal credits or the connection between cash flow and net income. Current AI lacks finance-native guardrails and verifiable reasoning graphs, making it unreliable for closing financial books. While AI can draft reports and entries, it cannot ensure they adhere to accounting logic, posing risks of inaccuracies. The finance industry requires explainable, auditable, and defensible actions, which current AI models cannot guarantee.

Why It's Important?

The integration of AI into finance holds significant potential for efficiency and innovation. However, the current limitations highlight the need for caution. Finance teams could benefit from AI handling mundane tasks like receipt tracking and approvals, freeing up human resources for more complex decision-making. Yet, the lack of trust in AI's ability to perform critical financial functions without human oversight remains a barrier. As AI capabilities grow, the demand for trust and accountability in financial operations will increase. This necessitates investment in systems that ensure data integrity, human oversight, and clear accountability, paving the way for AI to become a reliable tool in finance.

What's Next?

To prepare for AI's eventual readiness in finance, companies should focus on building trust and infrastructure. This includes implementing traceable outputs, enforceable constraints, and systems that allow for human intervention. Finance leaders are encouraged to start integrating AI in areas that cause operational pain, such as procurement and routine approvals, to demonstrate AI's value and build trust among employees. As AI technology evolves, finance teams must be ready to adapt quickly, ensuring that their systems and controls are robust enough to handle increased AI capabilities.