How to implement AI in finance: Where to start and what to avoid

Key takeaways

• Start with one or two high-impact use cases where AI can take real work off your team's plate within weeks, not months.

• Infrastructure decisions matter more than AI features. Disconnected systems limit what AI can see and do.

• Airwallex combines global financial infrastructure and software in one platform, giving AI complete visibility across banking, payment acceptance, and spend management.

All of a sudden, AI is everywhere. If you believe everything you read, it’ll soon be closing your books while you sleep, predicting cash shortfalls weeks in advance, running finance operations end to end, and basically doing your job for you. The sheer volume of these promises, and the expectations they create is starting to feel a little overwhelming. When AI appears to offer answers to everything at once, the harder part becomes trying to cut through the noise and decide where to begin.

The teams that see results tend to start small, stay grounded in how work actually gets done, and expand only once the basics are working reliably. That approach turns big promises into steady progress, and ambition into something teams can act on with confidence.

Where AI delivers value fastest in finance teams

A sensible place to begin is the pockets of finance where AI is already proving valuable. These tend to be high-volume workflows with clear outcomes, where small improvements add up quickly and results are easy to measure, such as:

• Expenses and employee spend processing. This workflow eats up time because every receipt needs checking, coding, and policy review. AI speeds this up by extracting data automatically, categorising spend based on prior decisions, and escalating only real exceptions. 

• Failed payments, which disrupt cash flow and trigger manual follow-ups. AI improves payment success by learning which routes and retry logic work best for different transactions. At Airwallex, we use AI to lift payment acceptance and reduce false declines by reading live issuer signals and dynamically choosing the best path for each transaction.

• Fraud checks, especially traditional rules-based systems, that often block legitimate payments. AI models assess risk using full transaction histories, which improves accuracy while reducing false positives. Mastercard reports its Decision Intelligence Pro improved fraud detection by around 20% on average, with some deployments seeing gains of up to 300%.

• Reconciliation, which slows teams down when mismatches surface late. AI matches transactions continuously and flags issues early. A joint Stanford University and MIT study found accountants using generative AI closed month-end books 7.5 days sooner and freed up around 3.5 hours a week for higher-value work.

How to implement AI in financial services: a practical path

Step 1: Choose two focused use cases

Most teams get the best results when they begin with something contained and measurable. Try picking one workflow that slows your team down, and one where the cost of inaccuracy or delay is high. For most businesses, that’s usually business expenses, fraud checks, or payment retries. The aim should be to try and prove the hypothesis that AI can take real work off your plate within a reasonable window.

Step 2: Evaluate your infrastructure

AI performs best when it can see your banking, payments, cards, and finance data in one place. If these live across different systems, you may need to connect them via integrations or consider moving to a platform that’s already unified. Either way, what’s important here is to get an understanding of the cost and effort involved, to avoid any surprises halfway through a project. Companies that start with disconnected tools often discover that integration costs exceed migration costs to a unified platform, so run the numbers early before committing to a path.

Step 3: Define policies and guardrails

Before you turn anything on, set the basics: spending limits, approval rules, escalation paths, and what counts as an exception. The more clearly you define your rules, the easier it'll be for AI to help. They’ll also give your team confidence that everything happening behind the scenes is controlled, traceable, and easy to unwind if needed.

Step 4: Pilot with human oversight

During the first few weeks, let AI suggest actions while your team reviews and approves them. This practice gives you a feel for how it behaves, where it adds value, and what needs tightening. Keep an eye on both the wins – time saved, fewer declines, cleaner reconciliations – and any mistakes. A weekly review is usually enough to keep things moving.

Step 5: Expand to adjacent workflows

Once you’ve seen results in one area, move to the next workflow. If expenses went well, look at invoices. If fraud detection improved, add payment optimisation. The trick is to keep everything connected so you don’t create new silos. Growth here should feel like building out one system, not stitching together lots of small experiments.

Measuring the success of your AI implementation

Most teams either overthink measurement and track dozens of metrics that don't matter, or avoid it entirely because defining success feels too subjective. The practical middle ground is to pick three to five concrete metrics tied to your chosen use cases, establish a baseline before you switch anything on, and watch for both quantitative shifts and the qualitative signals your team gives you as they work with the system.

1. Select metrics that match your use cases

The metrics worth tracking depend entirely on where you started. Either way, you should document your baseline numbers before you turn anything on, otherwise you're guessing whether things actually improved or just feel different because the workflow changed.

For example, if you decide to apply AI to any of the areas we suggested above, we'd suggest metrics such as the ones below for each type of workflow:

• Expenses and employee spend: Average processing time per expense report; policy exception rates that require manual review; and time from submission to reimbursement.

• Payments and retries: Acceptance rates broken down by geography or card type; the specific reasons transactions decline; and how often retry logic recovers failed payments.

• Fraud checks: False positive rates alongside fraud detection rates; and the average time your team spends reviewing each flagged transaction.

• Reconciliation and close: Match rates; number of exceptions that need manual investigation; and days it takes to close the books compared to your current process.

2. Watch for friction points

AI that's working properly shows up when your team stops building workarounds or double-checking every output manually before they trust it enough to move forward. AI that's struggling creates new kinds of work instead of eliminating old work, which means people spend their time correcting miscategorised expenses, overriding routing decisions that don't make sense, or explaining to stakeholders why the system flagged a legitimate transaction as suspicious. Weekly check-ins during your pilot should surface both patterns early enough to make adjustments before they calcify into permanent habits that undermine the whole implementation.

3. Trust qualitative signals

Numbers tell you what's changed, but the way your team talks about AI can indicate how it’ll be adopted in the long term. The strongest indicator that an implementation has succeeded is when people start proposing new use cases without being prompted, which means they've moved past scepticism and see AI as a tool that makes their work easier. Once confidence builds, the questions will shift from, "can we trust this?" during the early pilot phase to "can we use this for invoice processing, too?”. Teams that trust the system stop running parallel manual processes as a safety net, and high-pressure periods like month-end closes become more relaxed, even if that improvement never shows up on a dashboard.

Common mistakes to avoid in AI finance implementation

Mistake 1: Jumping in without clear goals 

Pick a small set of metrics that matter to your team, take a baseline, and track how things shift. This gives you a simple way to understand whether the system is helping and where it needs tuning.

Mistake 2: Relying on disconnected systems 

When your finance data sits across different tools, AI has to work harder to make sense of everything. Some teams choose to integrate what they have. Others move to a unified setup. Either way, you should be aware of the pros and cons of each approach before you commit.

Mistake 3: Setting guardrails too late

Try to define approval limits, escalation paths, and exceptions early. Build audit trails and rollback options into your setup so every action is easy to follow and adjust. These basics give everyone confidence in the system as it learns.

Mistake 4: Starting with rare edge cases

You’ll probably find that most of the value sits in the everyday workflows your team repeats. These are the best focus points for your early experiments. Once they run smoothly, you can widen the scope. This keeps the project manageable and shows progress sooner.

Mistake 5: Not assigning a project owner

Assign someone to look at how the system behaves, update rules, and flag anything unusual. A light weekly check-in is often enough to keep things moving and prevent small issues from growing.

Build on infrastructure that supports AI from day one

The five-step path above works when your infrastructure can actually support it, and Step 2: Evaluate your infrastructure is usually where problems arise. 

Teams that start with fragmented banking, payment systems, and accounting tools often discover that even a narrow pilot depends on months of integration work before anything meaningful reaches production. What looks like a contained experiment stretches out, and each new workflow added later brings another set of connections to build, maintain, and explain. Over time, measurement becomes harder, because the data needed to judge progress sits in systems that were never designed to work together.

This is where the difference emerges between AI projects that stall and those that scale. When financial infrastructure is unified from the start, AI operates with full context rather than partial signals. At Airwallex, global business accounts, payment acceptance, FX and transfers, and spend management live on a single platform, giving AI a consistent view across transactions, entities, and currencies without relying on stitched-together integrations. Pilots surface issues faster, metrics remain consistent as scope expands, and moving from one use case to the next doesn’t create whole new layers of technical debt.

The finance teams that see the most value from AI over the next few years will be the ones that make clear infrastructure decisions early. The five steps in this article create momentum, but only once you’ve connected your infrastructure across all your financial operations. The good news is, it’s never too late to get started. 

FAQs

How long does it take to see results from AI in finance?

Most teams see meaningful improvements within weeks when they start with a focused use case like expenses, payments, or fraud checks. The biggest delays usually come from infrastructure or integration work, not the AI itself.

Do you need perfect data before using AI?

No. AI works best with clean, connected data, but waiting for perfection slows progress. Establish a baseline, start with what you have, and improve data quality as part of the rollout.

Is AI safe to use in regulated finance workflows?

Yes, when guardrails are defined upfront. Clear approval rules, audit trails, and human oversight during pilots ensure AI operates within controlled boundaries.

What’s the biggest reason AI projects fail in finance teams?

Disconnected systems. When banking, payments, and accounting data live in separate tools, AI lacks context and projects stall under integration complexity.