AI in Corporate Treasury

 

What This Article Is (And Isn’t)

This is not another article telling you that “AI will revolutionize treasury” or that “machine learning is the future.” You already know that. This is a practical guide showing you exactly what AI can do in your treasury department today, with real tools you can access, without requiring a computer science degree or a large IT team.

I will not speculate about future capabilities of any technology. Everything here is based on currently available technology and real-world treasury applications.

Understanding AI

What AI Actually Means for Treasury

When vendors talk about “AI-powered treasury,” they typically mean one of three things:

1. Pattern Recognition from Historical Data

• The system analyzes your past cash flows, payment patterns, or transaction data
• It identifies recurring patterns you might miss manually
• It uses these patterns to make predictions or flag anomalies

2. Automation of Repetitive Tasks

• Software that can read invoices, match transactions, or populate reports
• This is technically called “Robotic Process Automation” (RPA) combined with Optical Character Recognition (OCR)
• It mimics what a human would do, but faster and without errors

3. Natural Language Processing

• Systems that can read contracts, emails, or regulatory documents
• They extract relevant information automatically
• They can flag important clauses or changes

The Core Concepts You Need to Understand

Machine Learning (ML): The system learns from your data without being explicitly programmed for every scenario. Think of it as creating a “recipe” from your historical data that the system then uses to process new data.

Training: This is the process where the algorithm learns from your historical data. For cash flow forecasting, you “train” the model by showing it a long period of your actual cash flows plus relevant context (seasonality, business cycles, etc.). The model identifies patterns and relationships in this data.

Prediction vs. Certainty: AI provides probability-based predictions, not guarantees. A good system will tell you: “Cash flow tomorrow is likely €75,000 ±€15,000 with 85% confidence” rather than claiming exact amounts.

Real Treasury Applications That Work Today

Application 1: Cash Flow Forecasting

What it actually does:

• Analyzes 2-5 years of your historical cash flows
• Identifies patterns by day of week, month, season, business cycle
• Predicts future cash positions with confidence intervals
• Updates predictions as new data comes in

Technology involved:

• Time series analysis (identifies patterns over time)
• ARIMA models (analyzes trends and seasonality)
• LSTM networks (for more complex, non-linear patterns)

Tools you can use without IT department:

1. Microsoft Excel with add-ins:
   • Prophet (Facebook’s open-source forecasting tool)
   • Can be installed as an Excel add-in
   • Cost: Free
   • Learning curve: 2-3 days with online tutorials
2. Cloud-based treasury platforms with built-in ML:
   • Kyriba Cash Forecasting module
   • SAP Cash Management Forecasting
   • Cost: Part of existing TMS subscriptions
   • Learning curve: 1-2 weeks, maybe months for non technical users.
3. Google Sheets with simple formulas:
   • Built-in FORECAST.ETS function (uses exponential smoothing)
   • Cost: Free with Google Workspace
   • Learning curve: 1-2 hours

Implementation steps (no IT required):

Step 1: Export your historical data

• Extract daily/weekly cash flows from your bank statements or ERP
• Include: date, amount, transaction type, day of week, month
• Minimum: 2 years of data (more is better)

Step 2: Clean and structure the data

• Remove obvious errors or one-time exceptional items
• Mark business days vs. non-business days
• Note any major business events (acquisitions, large contracts, etc.)

Step 3: Choose your tool

• Start with Excel/Google Sheets if budget is tight
• Move to TMS modules if you already have the platform
• Avoid building custom solutions from scratch

Step 4: Train your model

• Most tools have wizards that guide you through this
• You typically upload your data and select relevant factors
• The system automatically identifies patterns

Step 5: Test accuracy

• Compare predictions to actual results for 1-2 months
• Calculate the error rate (how far off were the predictions?)
• Refine by adding or removing factors

Expected accuracy: You should aim in the beginning for at least 85-90% accuracy for 1-week forecasts, 75-85% for 1-month forecasts.

Application 2: Fraud Detection and Anomaly Identification

What it actually does:

• Learns your normal payment patterns and amounts
• Flags transactions that deviate significantly
• Identifies unusual vendor payments, duplicate invoices, or suspicious timing

Technology involved:

• Anomaly detection algorithms
• Statistical analysis of transaction patterns
• Rule-based systems combined with machine learning

Tools you can use:

1. Bank-provided solutions:
   • Most major banks offer fraud detection as part of payment services
   • Examples: JPMorgan ACCESS, BofA CashPro, Citi Treasury Vision (Disclaimer: I cannot confirm with certainty that Treasury Vision as marketed includes full anomaly-identification/fraud-detection features)
   • Cost: not disclosed.
   • Learning curve: Minimal (bank trains you)
2. Payment platform add-ons:
   • FICO Falcon Fraud Manager (interfaces with your existing systems)
   • Cost: TBD
   • Learning curve: 1-2 weeks
3. Excel-based anomaly detection:
   • Using statistical formulas (STDEV, AVERAGE) to flag outliers
   • Cost: Free
   • Learning curve: 2-3 hours

Practical implementation:

For small-medium treasuries (without dedicated fraud detection software):

Simple Excel approach:
1. Calculate average payment amount per vendor (last 12 months)
2. Calculate standard deviation for each vendor
3. Flag any payment that exceeds: Average + (3 × Standard Deviation)
4. Review flagged transactions manually

For larger operations (with dedicated tools):

Step 1: Connect your payment system to the fraud detection tool

• Usually done via API or file upload
• Bank or vendor provides connection guide

Step 2: Define your baseline

• System analyzes 6-12 months of transactions
• Learns normal patterns for amounts, frequencies, recipients, timing

Step 3: Set alert thresholds

• Start conservative (high sensitivity = more false positives)
• Adjust based on your team’s capacity to review alerts
• Typical setting: flag top 2-5% of unusual transactions

Step 4: Monitor and refine

• Review flagged transactions daily
• Mark false positives (helps system learn)
• Adjust rules monthly based on results

Expected results: You should aim to generate maximum 5-15% false positive rates.

Application 3: Invoice Processing and Account Payable Automation

What it actually does:

• Reads invoice PDFs or images using OCR (Optical Character Recognition)
• Extracts key data: vendor name, amount, due date, invoice number
• Matches invoices to purchase orders automatically
• Routes for approval based on rules you define

Technology involved:

• Computer Vision (to “see” the invoice layout)
• OCR (to read text from images)
• Natural Language Processing (to understand context)
• Rule-based workflows (to route for approval)

Tools you can access:

1. Entry-level solutions:
   • Stampli (interfaces with QuickBooks, NetSuite, SAP)
   • Tipalti (good for vendor payments)
   • Bill.com (for small-medium businesses)
   • Learning curve: 1 week
2. Mid-market solutions:
   • Basware
   • Esker
   • Kofax (now Tungsten Automation)
   • Learning curve: 2-4 weeks

Implementation without IT:

For companies processing 100-1,000 invoices/month:

Step 1: Choose a cloud-based solution

• Stampli or Bill.com are easiest to implement
• No infrastructure needed (works in browser)
• Connect to your accounting system via provided integrations

Step 2: Train the system on your invoices

• Upload 20-50 sample invoices from your top vendors
• System learns your invoice formats
• Takes 2-3 hours of your time

Step 3: Define approval workflows

• Set rules: invoices under €500 → auto-approve
• €500-5,000 → manager approval
• €5,000+ → treasury approval
• Takes 1-2 hours to configure

Step 4: Test with small batch

• Process 20-30 invoices manually alongside the system
• Compare results
• Correct any errors and system “learns” from corrections
• Takes 1 week

Step 5: Roll out gradually

• Start with your most frequent, standardized vendors
• Add more vendors weekly
• Full implementation: 4-8 weeks

Application 4: Bank Reconciliation

What it actually does:

• Matches bank transactions to your accounting records automatically
• Identifies unmatched items
• Suggests matches based on amounts, dates, descriptions
• Learns from your corrections

Technology involved:

• Fuzzy matching algorithms (matches similar but not identical data)
• String matching (compares transaction descriptions)
• Machine learning (improves matching over time)

Tools you can use:

1. Built into many accounting systems:
   • QuickBooks Online has basic auto-matching
   • Xero has smart matching
   • NetSuite has reconciliation workflows
   • Learning curve: 1-2 days
2. Specialized reconciliation tools:
   • BlackLine (market leader)
   • ReconArt
   • SmartStream TLM Reconciliations
   • AutoRek
   • Learning curve: 2-3 weeks
3. Excel with formulas (for small volumes):
   • VLOOKUP, INDEX/MATCH for basic matching

Practical implementation:

For manual Excel users upgrading:

Step 1: Structure your data consistently

• Bank statement: Date | Description | Amount | Reference
• Accounting: Date | Description | Amount | Invoice Number
• Save both as CSV files

Step 2: Use matching formulas like IF(VLOOKUP(A2, BankData!A:C, 3, FALSE)=C2, "MATCH", "NO MATCH")

• Use fuzzy matching for descriptions that differ slightly
• Excel add-in “Fuzzy Lookup” (free from Microsoft)

For mid-sized operations (moving to dedicated software):

Step 1: Choose your platform

• BlackLine if budget allows (most features)
• ReconArt for better value
• AutoRek for banking-specific needs

Step 2: Connect data sources

• Upload bank statements (MT940, camt053, CSV)
• Connect to your ERP via API or file export
• Takes 1-2 days with vendor support

Step 3: Configure matching rules

• Exact match on amount + date (within ±1 day)
• Fuzzy match on description (80%+ similarity)
• Reference number matching where available
• Takes 2-3 hours

Step 4: Review and teach

• System suggests matches, you confirm or reject
• Each correction improves future matching
• After 2-3 months, matching accuracy stabilizes

Business Case – example

Company Profile

• Mid-sized manufacturing company
• Annual revenue: €150 million
• Treasury team: 2 FTEs
• Monthly payments: ~800 invoices
• Bank accounts: 12 accounts across 4 banks
• Manual processes for everything

Current Pain Points

1. Cash flow forecasting: Excel spreadsheet, updated weekly, often inaccurate
2. Invoice processing: 2-3 days per batch, frequent data entry errors
3. Bank reconciliation: 4 days per month-end, many unmatched items
4. Fraud risk: No systematic checking, relying on manual review of large payments

Implementation Plan (12 months)

Phase 1 (Months 1-3): Cash Flow Forecasting

Investment:

• Microsoft 365 subscription (already have): €0 additional
• Prophet tool from Meta (R and Python)
• Training time: 1 week

Actions:

• Export 3 years of daily cash flow data
• Install and configure Prophet
• Train model on historical data
• Test predictions for 1 month
• Roll out for weekly forecasting

Expected results:

• Forecast accuracy improvement from 65% to 85%
• Time savings: 4 hours/week (was manual analysis)
• Better liquidity management: reduced idle cash

Phase 2 (Months 4-6): Invoice Processing Automation

Investment:

• Stampli subscription: €3,600/year (€300/month)
• Per-invoice fees: €800/month (800 invoices × €1)
• Implementation and training: €5,000
• Total first-year cost: €17,000

Actions:

• Connect Stampli to NetSuite (treasury uses)
• Train system on top 20 vendors (represents 60% of volume)
• Define approval workflows
• Pilot with 100 invoices
• Roll out gradually over 2 months

Expected results:

• 70% of invoices processed automatically
• Time savings: 15 hours/week
• Error reduction: 80% fewer data entry mistakes
• Faster payment: early payment discounts captured

Phase 3 (Months 7-9): Bank Reconciliation

Investment:

• ReconArt license
• Implementation
• Training: 2 weeks staff time

Actions:

• Connect 4 primary banks (via MT940/camt053 files)
• Connect NetSuite (via API)
• Configure matching rules
• Train system with 3 months of historical data
• Go live with daily reconciliation

Expected results:

• approx. 80% automatic matching rate
• Month-end close: reduced from 4 days to 1 day
• Time savings: 12 hours/month
• Improved cash visibility (daily vs. monthly)

Phase 4 (Months 10-12): Fraud Detection

Investment:

• Bank fraud detection service
• Configuration and training

Actions:

• Activate fraud detection module with primary bank
• Configure thresholds based on payment patterns
• Set up alert workflows
• Train treasury team on alert handling
• Monitor and refine for 3 months

Expected results:

• Systematic monitoring of all payments
• Detection of suspicious patterns before payment

Quantifiable Benefits (Annual) could be:

• Staff time savings
• Early payment discounts
• Improved cash utilization (reduced idle cash)
• Error reduction savings

Non-quantifiable Benefits:

• Better cash visibility and decision-making
• Reduced risk of fraud and errors
• Faster month-end close
• More strategic treasury team (less time on manual tasks)
• Improved audit trail and compliance

How to Get Started (Step-by-Step)

Step 1: Assess Your Current State

Create a simple inventory:

1. Data availability:
   • Do you have 2+ years of historical cash flow data?
   • Are your bank statements in digital format?
   • Can you export data from your ERP/accounting system?
2. Process documentation:
   • Map your current processes (how long each takes)
   • Identify pain points (what causes delays or errors)
   • Calculate time spent on manual tasks per week
3. Budget reality check:
   • What can you spend on software this year?
   • Can you allocate staff time for implementation?
   • Do you need approval for cloud-based tools?

Step 2: Pick ONE Problem to Solve

Don’t try to implement everything at once. Choose based on:

• Highest pain: What causes the most frustration?
• Easiest implementation: What requires least IT involvement?
• Fastest ROI: What saves the most time or money?

Recommendation for beginners: Start with invoice processing OR cash flow forecasting.

Step 3: Test with Free/Low-Cost Tools

Before committing to expensive software:

For cash flow forecasting:

• Try Excel’s FORECAST.ETS function first
• Test with 6 months of your data
• See if results are useful

For invoice processing:

• Try free trials: Stampli, Bill.com offer 30-day trials
• Process 50 invoices
• Measure time savings

For reconciliation:

• Use Excel fuzzy matching add-in
• Test with one month of transactions
• Check matching accuracy

Step 4: Implement Your Chosen Solution

Follow the specific implementation steps outlined in Part 2 for your chosen application.

Key success factors:

• Start small (pilot with subset of data)
• Document what works and doesn’t
• Train the system (corrections improve AI)
• Measure results (time saved, accuracy gained)
• Expand gradually

Step 5: Measure and Refine (Ongoing)

Create a simple tracking dashboard:

• Time spent on process (before vs. after)
• Accuracy metrics (forecasts, matching rates)
• Error rates
• User satisfaction
• Cost savings

Review monthly for first 6 months, then quarterly.

Common Pitfalls and How to Avoid Them

Pitfall 1: Expecting Perfect Accuracy Immediately

Reality: AI improves over time as it learns from your data and corrections.

Solution:

• Set realistic expectations: 70-80% accuracy initially is good
• Plan for a 3-6 month learning period
• Accept that some manual review will always be needed

Pitfall 2: Insufficient or Poor-Quality Data

Reality: AI is only as good as the data you feed it.

Solution:

• Clean your historical data before implementation
• Remove obvious errors and outliers
• Ensure consistent formatting
• If data is messy, start with a smaller, cleaner subset

Pitfall 3: Not Training Your Team

Reality: Technology doesn’t replace people; it changes what they do.

Solution:

• Involve your team from day one
• Explain what’s changing and why
• Provide hands-on training (not just vendor presentations)
• Address concerns about job security openly

Pitfall 4: Choosing Tools Too Complex for Your Needs

Reality: Enterprise software often has features you’ll never use.

Solution:

• Be honest about your actual requirements
• Start simple and upgrade later if needed
• Value ease of use over feature lists
• Consider total cost of ownership (including learning time)

Pitfall 5: Implementing Without IT Buy-In

Reality: Even “no IT required” tools sometimes need technical support.

Solution:

• Inform IT about your plans (even if not asking permission)
• Check security and data policies first
• Ask IT to review data connections
• Build a collaborative relationship

Pitfall 6: Underestimating Implementation Time

Reality: Vendors say “quick implementation” but reality varies.

Solution:

• Add 50% buffer to vendor timelines
• Plan for unexpected issues
• Don’t schedule go-live during busy periods (month-end, year-end)
• Have backup plans if things take longer

Technical Concepts Explained Simply

What Is “Machine Learning” Really?

Think of it like teaching a child to recognize animals:

Traditional programming (rules-based):

• You write explicit rules: “If it has 4 legs AND barks, it’s a dog”
• Every scenario needs a specific rule
• Can’t handle variations well

Machine learning (pattern-based):

• You show 1,000 pictures of dogs
• The system learns what “dog-like” features are (4 legs, fur, certain ear shapes, etc.)
• Can recognize new dogs it’s never seen before
• Adapts to variations

In treasury context:

• Rules-based: “If payment amount > €10,000, flag for review”
• ML-based: “Based on 2 years of payments, this transaction is unusual for this vendor and should be reviewed”

What Is “Training a Model”?

Using cash flow forecasting as an example:

1. Data collection:

• You provide historical cash flows (e.g., 3 years of daily data)
• Plus context: day of week, month, season, business events

2. Pattern identification:

• Algorithm analyzes your data
• Finds relationships: “Fridays have higher inflows” or “January has lower outflows”
• Quantifies these relationships mathematically

3. Model creation:

• Creates a mathematical formula (model) that captures these patterns
• This formula can now predict future cash flows

4. Validation:

• Tests predictions on data it hasn’t seen
• Measures accuracy
• Adjusts if needed

Analogy: Training is like teaching someone your company’s payment patterns by showing them months of examples until they can predict what typically happens on any given day.

What Are “Confidence Intervals”?

Good AI doesn’t claim certainty—it provides ranges.

Example:

• Bad output: “Tomorrow’s cash flow will be €100,000”
• Good output: “Tomorrow’s cash flow will likely be €100,000 ±€20,000 with 85% confidence”

What this means:

• There’s an 85% probability the actual cash flow will be between €80,000 and €120,000
• The wider the range, the less certain the prediction
• Narrower ranges indicate more stable, predictable patterns

Why this matters for treasury:

• You can plan for the range, not just the point estimate
• You know when predictions are less reliable (take extra precautions)
• You can set alerts for outcomes outside the confidence interval (true anomalies)

What Is “Anomaly Detection”?

The system learns what “normal” looks like, then flags deviations.

How it works:

1. System analyzes 6-12 months of transactions
2. Calculates normal ranges for:
   • Payment amounts per vendor
   • Payment frequency
   • Typical payment timing
3. Flags transactions that fall outside normal patterns

Example:

• Vendor X: Normal payments are €5,000-10,000, weekly, on Tuesdays
• New payment: €50,000 on Saturday
• System flags as anomaly (unusual amount + unusual day)

This is NOT fraud detection per se:

• It just flags unusual transactions
• Could be legitimate (annual payment, bulk order)
• Could be error or fraud
• Human reviews flagged items

What Is “Natural Language Processing” (NLP)?

Teaching computers to understand human language.

In treasury, NLP is used for:

1. Document extraction:
   • Reading contracts to find payment terms
   • Extracting invoice data from PDFs
   • Identifying key clauses in agreements
2. Email processing:
   • Categorizing treasury-related emails
   • Extracting payment instructions from emails
   • Flagging urgent requests
3. Regulatory monitoring:
   • Scanning regulatory updates for treasury-relevant changes
   • Summarizing compliance requirements
   • Alerting to deadline changes

How it works (simplified):

• System is trained on thousands of documents
• Learns that certain words/phrases appear in certain contexts
• Can identify relevant information in new documents
• Extracts structured data from unstructured text

Example:

• Unstructured: “Invoice for services rendered in March, payment due within 30 days, total €15,000”
• Structured extraction: Invoice Date: March 2024, Due Date: April 2024, Amount: €15,000

What You Should NOT Expect from AI

Let me be clear about limitations:

AI Cannot:

1. Make strategic decisions for you
   • AI provides data and predictions
   • You still need judgment and context
   • Strategic risk decisions remain with humans
2. Replace understanding your business
   • AI finds patterns in YOUR data
   • It doesn’t know your industry dynamics
   • Business knowledge is still critical
3. Work without good data
   • “Garbage in, garbage out” still applies
   • AI cannot fix bad data quality
   • Minimum data requirements must be met
4. Prevent all fraud
   • AI reduces fraud risk, doesn’t eliminate it
   • Sophisticated fraud can evade pattern-based detection
   • Human oversight remains necessary
5. Work perfectly from day one
   • Initial accuracy may be 60-70%
   • Requires learning period
   • Needs ongoing refinement

What AI Does Well:

1. Handle high-volume repetitive tasks
   • Processing hundreds of invoices
   • Matching thousands of transactions
   • Analyzing years of historical data
2. Identify patterns humans might miss
   • Subtle correlations in cash flow data
   • Unusual transaction patterns
   • Seasonal trends across multiple variables
3. Provide consistent analysis
   • Same logic applied every time
   • No fatigue or lapses in attention
   • Documented decision criteria
4. Scale without proportional cost increase
   • Processing 1,000 or 10,000 invoices costs nearly the same
   • Doesn’t need breaks or time off
   • Can work 24/7

Your Next Steps

If you’ve read this far, you’re ready to start. Here’s your action plan:

This Week:

1. Assess your top 3 pain points in treasury operations
2. Check what data you have available (quantity and quality)
3. Identify your budget constraints (€2,000? €20,000? €200,000?)

Next Week:

1. Pick ONE problem to solve (my recommendation: invoice processing OR cash flow forecasting)
2. Sign up for free trials of 2-3 relevant tools
3. Test with a small sample of your real data

Next Month:

1. Choose the tool that worked best in trials
2. Implement with a subset of your data/processes
3. Measure results honestly
4. Document what works and what doesn’t

Next Quarter:

1. Expand successful implementation
2. Start planning second application
3. Calculate actual ROI
4. Share results with leadership

Key Principles:

✓ Start small and prove value before expanding

✓ Focus on practical benefits, not technology for technology’s sake

✓ Involve your team early and often

✓ Measure results honestly

✓ Be patient—AI improves over time

✓ Don’t be afraid to admit if something isn’t working and try a different approach

Final Thoughts

The technology is accessible today. You don’t need a computer science degree or a large IT department. You need:

• Willingness to try something new
• Basic data in usable format
• A few thousand euros for software
• Time to implement and learn

The companies gaining advantage now are not the largest or most technically sophisticated. They’re the ones willing to start small, learn from experience, and adapt.

Your treasury department in 2026 can look very different from today—if you take the first step.

DISCLAIMER: Article prepared for treasury professionals in October 2025 based on currently available technology and verified applications. Specific pricing reflects general market ranges based on publicly available vendor information and may vary based on company size, transaction volumes, and negotiation.