AI in finance: How artificial intelligence will reshape finance teams

Key takeaways

• AI in finance has evolved from drafting summaries to executing entire workflows autonomously, cutting close time, preventing fraud, and improving payment success rates.

• AI works best when it can see the complete picture. When your banking, payment systems, and finance software are split across different platforms, AI can't connect the dots between them.

• Airwallex combines global financial infrastructure and software in one system, giving AI complete visibility into every inflow, outflow, and approval flow across all entities and currencies.

Artificial Intelligence (AI) won't replace your finance team but it will take over their repetitive work. Books can close overnight, fraud checks run in real time, failed payments retry automatically and route through the best path, and cash forecasts update continuously across all entities and currencies. But AI can do this only when it sees the whole picture: that is, when your banking infrastructure, payment systems, and finance software are all connected. 

What is AI in finance?

AI in finance refers to using artificial intelligence to automate finance functions. AI is suitable for finance because most of the work is high volume, repetitive, rule-based, and judged by clear outcomes. Data is structured, processes are documented, and success is measurable, which is the kind of environment where it can perform well.

There’s a good chance that you’re already using some form of AI in your financial workflows. And if you’re not, the good news is that AI will probably come to you, especially if you’re relying on a modern vendor. For example, rules-based automations are already matching invoices to purchase orders. Scheduled workflows are triggering month-end close tasks. Risk scores are flagging suspicious transactions.

AI evolves by learning from every transaction and adapting to new patterns within your guardrails. It reads receipts and categorizes them based on how your team has categorized previous, similar expenses. It adjusts payment routing based on which paths have the highest success rates for specific transaction types.

Understanding AI in finance: key concepts and definitions

To understand how AI will reshape finance teams, you need to understand a few key concepts that define how AI works and what it needs to be effective.

• Machine learning is how AI learns. It analyzes patterns in historical data to make predictions or decisions. AI can learn that certain transaction patterns indicate fraud or that payments to specific regions succeed more often through particular routes.

• Agents are AI systems that plan and execute tasks independently within defined boundaries. They assess situations, choose actions, and use tools to complete workflows, from receiving an invoice to executing an payment.

• Agentic AI refers to this autonomous capability. The term distinguishes systems that rely on generative AI (that drafts content) from analytical AI (that produces predictions). 

• Conversational finance UX means interacting with your finance system through natural language. You ask, "What's our cash position in Singapore?" or "Pay this invoice on Friday," and AI interprets the request, gathers the data, and executes the action. 

• AI-native systems are platforms designed from the ground up with AI capabilities built in, rather than AI features added to existing software. 

• Unified infrastructure means your banking accounts, payment systems, cards, and finance software operate on a single platform with shared data. This lets AI see relationships across your entire finance operation rather than isolated snapshots from individual tools.

• APIs (Application Programming Interfaces) enable agents to take actions like initiating payments or updating records. Without APIs, agents can analyze data and recommend actions, but can't execute.

These concepts connect to one another. Agents use machine learning to make decisions, APIs to take actions, and unified infrastructure to see the complete context they need to operate effectively. When any piece is missing, AI’s capabilities drop from autonomous to assistive.

Why AI is critical for modern financial services

Finance operations don't scale the way the rest of your business does. Revenue can double without doubling headcount in sales or engineering, but finance teams grow nearly linearly with transaction volume. Every new market, currency, entity, and payment method requires additional manual work. Month-end closes take longer, reconciliation requires more people, and fraud reviews create bottlenecks. 

The only way to break this pattern is to automate the routine work that consumes 60 to 70% of a finance team's time, and AI is the most effective technology that can learn your business context well enough to do it reliably.

Companies that deploy AI in finance operations now will pull ahead of those that wait. And most companies are waiting: A recent McKinsey report found that nearly two-thirds of organizations have not yet begun scaling AI across the enterprise. 

If you deploy AI early enough, your close cycles will shrink from days to hours while competitors chase receipts across systems. Your payment acceptance rates will improve while others lose revenue to avoidable declines. Your fraud detection will catch sophisticated patterns while competitors waste time clearing false positives. The gap compounds quickly because AI gets better with volume, which means early adopters will gain both operational advantage and better AI performance over time.

Key AI applications in finance

AI's impact is already showing up across three core finance functions where companies are seeing measurable returns today.

• Payment optimization analyzes issuer response patterns in real time and adjusts routing and retry strategies automatically. Prosus subsidiary iFood improved acceptance rates to 97% while dropping chargeback rates from 2.6% to 0.1% after implementing AI-driven improvements.

• Fraud detection analyzes patterns across complete transaction histories rather than evaluating each transaction in isolation. Mastercard's Decision Intelligence Pro improved fraud detection by approximately 20% on average, with some implementations showing up to 300% improvement. Visa's Scam Disruption practice disrupted US$350 million in attempted fraud during 2024.

• Reconciliation and close use AI agents to handle time-consuming tasks. Microsoft deployed Copilot-style assistants that reduced bank reconciliation time from hours to minutes. A 2025 field study referenced by MIT and Stanford found that generative AI pilots reduced monthly close time by 7.5 days across accounting workflows.

At Airwallex, we’re using AI to lift payment acceptance and reduce false declines by reading live issuer signals and dynamically choosing the best path for each transaction. We also automate finance operations: AI captures and categorises expenses and keeps transactions in sync with accounting systems to speed up reconciliation.

The future of AI in finance: emerging trends and technologies

The good news is that if you’re in finance, AI will likely come to you in the form of conversational UI, expense automation, reconciliation, and more. But it's worth understanding where this technology is heading. The AI handling individual tasks today will evolve into coordinated systems where multiple agents work together with less human intervention. Here's what that shift could look like.

Coordinated agents working together

Multiple specialized agents will coordinate across spend, payments, treasury, and compliance with clear handoffs and shared context. One agent processes an invoice and triggers a second to check budget and policy compliance. That agent hands off to a third that executes payment at the optimal time and rate, then notifies a fourth to update cash forecasts. Platforms with unified infrastructure can deploy these agents now because their banking rails, payment systems, and finance workflows already share a foundation that lets agents hand off work without integration gaps.

Autonomous finance operations

Autonomous finance looks like a network of agents running continuously in the background, each handling a different part of financial operations. At Airwallex, we're already building AI agents on top of our unified software and infrastructure. These will manage invoicing, treasury, policy checks, and more. Humans can step in anytime, set approval checkpoints, or inspect decisions, but day-to-day operations will increasingly run themselves.

AI as a strategic partner

AI will become your strategic finance partner that interprets data and proposes actions in real time. You’ll be able to ask, "What is our FX exposure in Singapore next quarter?" and receive projections, hedging recommendations, and scenario modeling. AI will also alert you when cash flow shifts unexpectedly, customer patterns change, or strategic risks emerge.

Because AI will understand the full lineage of your finances, from supplier relationships to payouts, and revenue to reconciliation, it can connect patterns that would take humans days to piece together. This shifts the CFO's role from operator to architect, from managing accounts to shaping strategy.

Read more about how conversational AI is rewriting financial leadership.

Richer payment data, smarter models

Payment data is getting richer, which will make AI smarter. ISO 20022, a global standard for financial messaging, has replaced cryptic payment codes with structured data about invoice numbers, beneficiary details, and remittance information. This will enable more accurate fraud detection, better cash forecasting, and more intelligent payment routing because models will be able to learn from context rather than just amounts and dates.

Choose a platform that gives AI the full picture

Finance teams have spent decades optimizing for control, adding approval layers, and manual checks to catch errors and prevent fraud. For years, that made sense. Mistakes were expensive and volumes were manageable. But as companies expanded, those same controls became the bottleneck. Now you can end up spending more managing the processes than you'd lose to the occasional error.

AI flips this model. It allows finance teams to move faster but still enforce controls automatically, with more complete audit trails. But the bigger shift is how AI unlocks growth. For example, you can enter new markets and currencies without scaling your team at the same rate.

This only works when your infrastructure is built for it. At Airwallex, we’ve built global financial infrastructure and software as a unified system, which includes global business accounts, payment acceptance, FX and transfers, and spend management. Our AI sees the complete picture of your finances as a single source of truth across every country, entity, and currency. Other platforms may offer advanced AI, but if their systems aren't connected, AI never gets the full picture.

The finance teams that reach autonomous operations first will define what's possible for the next decade. You're either building on infrastructure designed for it from the start, or spending years retrofitting fragmented systems while others pull ahead.

Frequently asked questions

What's the difference between analytical, generative, and agentic AI?

Analytical AI analyzes data for insights and predictions, like risk assessment scores. Generative AI, on the other hand, is able to use that data to create new content, such as financial reports. Agentic AI takes that one step further. It’s able to plan and execute workflows, like processing invoices or retrying failed payments.

How do I get started with AI without having to rebuild everything?

Incrementally integrate AI into your workflows. Start with the most repetitive tasks with the largest outcomes, such as automating expense management and optimising payment processing. Consider switching to an AI-ready financial platform where infrastructure and software across global business accounts, payment acceptance, and spend management are already integrated. This allows AI to work the most effectively.

What guardrails do I need for AI systems?

Continuously monitor and improve AI systems through assigning a dedicated topic owner for regular human oversight. Ensure data privacy and security, maintain transparency, and audit the AI system for bias and compliance with regulations.