AI in Finance: Powering Smarter Cross-Border Payments and FX Decisions

Key takeaways

• AI is moving from buzzword to backbone technology in finance. It’s already embedded in fraud controls, credit decisions, customer support, and trading, often without teams realizing it.

• Finance functions are catching up fast: surveys show more than half of finance teams now use AI, with early adopters reporting tangible productivity gains and better customer experiences.² ³

• For cross-border payments and FX, AI and automation can streamline payment routing, reduce errors, improve compliance, and give finance leaders more control over costs and risk, especially when combined with a specialist provider like Xe.

Why AI is now a finance priority

Artificial intelligence in finance uses advanced algorithms, machine learning, and language models to analyze large volumes of data, automate repetitive work, and support better decisions—far beyond what traditional rules-based software can manage.¹

What has changed in the last 2–3 years isn’t just the technology, but its accessibility:

• Cloud infrastructure and modern APIs make it easier to connect AI tools into existing systems.

• Generative AI and copilots turn complex analytics into natural-language queries your team can actually use.³

• Finance teams are under pressure to do more with less—shorter close cycles, tighter cash control, and better risk oversight—making automation and AI far more than a “nice to have”.²

The result: AI has become a practical lever for finance leaders who want to modernize processes without a full systems rebuild.

1. Map where AI already touches your finance stack

Most organizations already use AI somewhere in their financial workflows, even if it’s not labeled that way.

Common “quiet” AI use cases:

Banking portals & payment platforms – fraud checks, sanctions screening, and transaction monitoring often rely on machine learning.⁵

Accounting and ERP systems – duplicate invoice detection, anomaly flags, and automated coding suggestions.

Corporate cards and expense tools – auto-classification of spend, receipt matching, and policy violation alerts.

CRM and collections tools – next-best-action suggestions, propensity-to-pay scores, and automated outreach.

Take an inventory of:

• Where AI is already embedded (bank, ERP, TMS, CRM, payment provider).

• Which processes are still heavily manual (spreadsheets, email, copy-paste).

• Where data quality is strong enough to support better analytics (e.g., structured invoice data vs. unstructured PDFs).

This baseline lets you prioritize AI projects that complement, not duplicate, existing systems.

2. Target quick-win AI use cases in finance

You don’t need a massive “AI transformation” to see meaningful gains. Focus on a few concrete use cases with clear ROI.

Cash flow forecasting and scenario planning

Machine learning models can:

• Ingest historic cash flows, seasonality, and customer behavior.

• Incorporate external signals like rates, FX volatility, or macro indices.

• Produce rolling forecasts and “what if” scenarios on demand.

Impact:

• Fewer surprises in working capital.

• Better timing of major payments and FX conversions.

• Stronger confidence when discussing liquidity with the board.

Invoice processing and reconciliation

AI-powered OCR and classification can:

• Read invoices in multiple formats and languages.

• Extract fields (amount, dates, tax, PO number) with high accuracy.

• Match invoices to POs, contracts, and payments.

Impact:

• Shorter invoice approval cycles.

• Fewer manual errors and duplicate payments.

• Clearer view of actual vs. planned spend.

Customer support and internal “copilots”

Generative AI chatbots and copilots can:³

• Answer routine questions about invoices, payment status, and FX rates using knowledge bases and transaction data.

• Draft explanations for variances, management commentary, or board pack summaries.

• Help team members query ERP or TMS data in natural language instead of complex reports.

Impact:

• Faster response times for internal stakeholders and customers.

• Reduced pressure on finance teams at month-end.

3. Apply AI to cross-border payments and FX

Cross-border payments are prime territory for AI: high volumes, multi-currency complexity, and strict compliance requirements.

Industry analyses show that AI in cross-border payments can boost speed, reduce costs, and strengthen security by optimizing routes, spotting anomalies, and automating checks.⁴

Smart payment routing

AI-driven routing engines can:

• Evaluate multiple payment corridors (SWIFT, local rails, alternative partners).

• Balance speed, cost, and reliability for each transaction.

• Learn over time which routes perform best for each corridor and amount.

Impact: fewer failed payments, lower fees, and more predictable settlement times.

FX risk analytics and decision support

AI tools can:

• Analyze your historic FX flows by currency, region, and counterpart.

• Simulate how rate moves would affect margin or project profitability.

• Suggest hedging levels or timing based on your risk appetite (e.g., cover 60–80% of forecast EUR exposure over the next quarter).

Impact: more disciplined FX decisions, less “gut feel” trading.

Anomaly and fraud detection on cross-border flows

Machine learning models excel at pattern recognition across large transaction sets. They can flag:

• Unusual counterparties or geographies.

• Amounts or payment patterns outside historical norms.

• Sequences that resemble known fraud typologies.⁵

Impact: strengthened defenses without overwhelming teams with false positives—especially when combined with human review and clear escalation paths.

4. Strengthen risk, fraud, and compliance with AI

Regulators recognize that financial institutions have used AI for years in three core areas: customer support chatbots, fraud and AML monitoring, and underwriting. They now emphasize governance, explainability, and model risk management alongside innovation.⁵

For finance leaders, that means:

• Fraud & AML:

  AI can dramatically improve detection rates while reducing noise, but models must be tuned, monitored, and documented.

• Credit risk:

  Alternative data and non-linear models can expand access to credit—but fairness and bias controls are critical.

• Operational risk:

  Anomaly detection can surface unusual ledger entries, suspicious journal patterns, or access irregularities in key systems.

Practical steps:

• Work with Compliance and Legal early when deploying AI in regulated areas.

• Maintain clear model documentation, validation, and performance monitoring.

Keep humans “in the loop” for high-impact decisions (blocking payments, denying credit, filing SARs).

5. Build responsible AI governance for the finance function

Governance is what turns AI from a series of pilots into a durable capability.

Key components:

• Clear use-case selection

• Prioritize projects with measurable value, suitable data, and acceptable risk.

• Defer use cases where explainability or data quality is weak.

• Data foundations

• Clean, consistent master data (customers, suppliers, chart of accounts).

• Robust access controls and encryption for sensitive financial data.

• Model risk management

• Define who owns each model (and its performance).

• Set thresholds for retraining, recalibration, and retirement.

• Implement regular testing for drift, bias, and unexpected behavior.

• Ethics and transparency

• Be clear when decisions are supported by AI vs. made by humans.

• Give customers and employees routes to challenge or appeal important outcomes.

• Skills and change management

• Train finance staff to work with AI tools, not around them.

Re-frame AI as a co-pilot that removes low-value work so humans can focus on judgment, relationships, and strategy.³

6. Prepare your team and data for AI

Successful AI adoption in finance is less about buying tools and more about readiness.

Data readiness checklist

• Are your core systems (ERP, TMS, CRM, HR) integrated or at least reconcilable?

• Can you reliably trace from invoices and contracts to payments and GL entries?

• Do you have clear ownership for metadata like cost centers, project codes, or entity tags?

Team readiness checklist

• Do your finance team members understand the basics of how AI works and its limits?

• Are there champions in FP&A, treasury, and operations who can sponsor pilots?

• Do you have a governance forum where Finance, IT, Risk, and Compliance review AI use cases together?

Starting with a small set of high-leverage use cases—like cross-border payments, cash forecasting, or invoice processing—builds confidence and a repeatable pattern for future AI projects.

AI and cross-border payments: impact snapshot for finance teams

Illustrative example of how AI-enabled automation and smarter FX workflows can affect operating costs and team capacity:

* Includes automation of payment routing, reconciliation, and exception handling.
** Illustrative only, assuming blended finance FTE cost plus a 10–20 bps improvement in FX and fee leakage.

The exact numbers will vary, but the direction is consistent: better automation and smarter decision support free up finance teams to focus on analysis, not admin.

FAQ

Will AI replace finance professionals?

Unlikely. AI is very effective at pattern recognition and repetitive tasks, but it lacks context, judgment, and accountability. The highest-performing finance teams use AI to automate data collection and first-pass analysis, while humans handle interpretation, negotiation, and final decisions.

How accurate are AI-driven forecasts and recommendations?

Accuracy depends on data quality, model design, and stability of the underlying environment. Forecasts and recommendations should be treated as decision support, not guarantees. Strong teams validate AI outputs against business knowledge and adjust where needed.

Is AI only relevant for big banks and enterprises?

No. Cloud platforms and APIs have made advanced capabilities accessible to mid-size and even small organizations. Many AI-powered tools—invoice capture, anomaly detection, chatbots—are now embedded in mainstream SaaS products, allowing smaller finance teams to benefit without specialist data science staff.³

What about regulatory and privacy concerns?

Regulators expect financial institutions and corporates to manage AI like any other model: with clear governance, testing, documentation, and controls. Sensitive financial and personal data must be handled in line with privacy laws and internal policies, with robust security and access controls.⁵

Where should we start if we’re new to AI?

Pick one or two areas where pain is high and data is reliable—such as cross-border payment reconciliation, cash forecasting, or invoice capture. Run a tightly scoped pilot, measure the impact, refine governance, then expand to adjacent use cases.

How Xe helps

Even the most advanced AI strategy needs a solid, reliable payments and FX backbone. Xe provides the infrastructure to move money and manage currencies so your finance team—and any AI tools you adopt—can operate with confidence.

Send and receive international payments at scale
Use Xe’s money transfer platform for secure, fast cross-border payments in 100+ currencies, with transparent rates and no surprise markups.

Centralize global business payments and FX risk management
Manage multi-currency accounts, FX exposure, and payment workflows in one platform designed for businesses.

Hold, convert, and pay from multi-currency balances
Keep funds in the currencies you spend, reduce repeated conversions, and pay suppliers from local-currency balances to simplify reconciliation.

Integrate global payments into your own systems and AI workflows
Plug Xe’s Payments API into your ERP, platform, or AI-enhanced treasury tools so payments can be triggered programmatically and tracked end-to-end.

Citations

¹ IBM – IBM (2024)
² Gartner – Gartner (2024)
³ Bain & Company – Bain & Company (2024)
⁴ Scalefocus – Scalefocus (2025)
⁵ Bank for International Settlements – Bank for International Settlements, FSI Insights No. 63 (2024)

Information from these sources was taken on November 25, 2025.

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Disclaimer:
The content within this blog post is for informational purposes only and is not intended to constitute financial, legal, or tax advice. All figures and data are based on publicly available sources at the time of writing and are subject to change. Actual conditions may vary depending on location, timing, and personal circumstances. We recommend consulting official government resources or a licensed professional for the most up-to-date and personalized guidance.