Artificial intelligence is now evolving and the picture in every sector is transforming and becoming more efficient. Traditionally accountant used to keep track of the in(s) and out(s) of the income requiring careful attention. But with AI has made money management not only easier but also more efficient.
AR automation can reduce DSOs by as much as 32% according to their latest study of 100 U.S. based CFOs. Traditional methods are slow and full of errors. They need too many people and cost too much money. AI solves these problems by making processes smarter and faster. Let us understand this better.
What is AI in accounts receivable?
AI in accounts receivable uses a smart computer program to handle money owed by customers. These systems learn from past data to make better decisions about collections. It uses machine learning, NLP, robotic process automation and predictive analyses in AR.
- NLP: NLP basically stands for Natural Language Processing; it helps an AI model to gain deeper understanding and respond empathetically. When we talk about AR, NLP helps AI to interact with the customers.
- ML: Machine learning (ML) helps AI in learning from data and adapt to variety of prompts. It allows AI to observe past payment patterns and helps generate a strategic and personalized follow up.
- Robotic process automation: You can take AI helps in automating repetitive tasks like data entry, invoice matching, reconciliation work, and more.
- Predictive Analysis: Predictive Analysis comes into picture when you require AI to make predictions using insights from the historical data. In AR, it can predict delayed payments, flow of cash, risk of credit, and more.
Key benefits of AI in accounts receivable
There are multiple benefits of using AI in accounts receivable, one is of course that it makes the whole process efficient and error free. But there is more to it which is stated below:
Faster payment collection
You can collect payments a lot faster with the help of AI. This improvement can save you millions of pounds. As per the report by FIS Webinar AI helps businesses collect money 5-15 days faster than manual methods. This improvement can mean millions of pounds in extra cash flow for large companies.
Reduced manual work
Automated systems can process invoices more quickly than manual methods, leading to timely payments and better cash flow management. Staff can focus on more important tasks instead of data entry.
Better risk management
AI looks at customer data to predict payment behaviour. It identifies risky customers early, helping to prevent bad debts and write-offs. This helps prevent bad debts and write-offs.
Companies achieve faster collections, reduced manual workloads, and better credit management through AI automation.
How AI works in accounts receivable?
Customer risk scoring
AI examines past payment patterns to score each customer’s risk level. It looks at both internal data and credit bureau information.
The system creates categories like:
- Very low risk (2% chance of late payment)
- Low risk (5-10% chance)
- Medium risk (20-30% chance)
- High risk (40-60% chance)
- Very high risk (70%+ chance)
Automated collections strategy
Based on risk scores, AI assigns different collection approaches:
High-Risk Strategy Example:
- Day -10: Email reminder before due date
- Day +1: Late payment notice via email
- Day +4: Phone call from collector
- Day +6: Alert to sales team about credit hold
Low-Risk Strategy Example:
- Day -5: Automated email reminder
- Day +3: Second automated reminder
- Day +7: Automated phone message
Smart payment application
AI learns how payments should be applied to invoices. When it cannot match a payment automatically, it watches how staff handle the exception. Next time, it uses this knowledge to apply similar payments correctly.
The system can also handle small differences automatically. If an invoice is short-paid by 50p, AI can write off the difference and apply the rest of the payment.
What is payment automation software?
There are many tools that can help your business handle money coming in automatically. Here are some popular ones:
Software Options
| Software | Monthly Cost | What It Does |
|---|---|---|
| Oracle NetSuite | Ask for price | Sends bills automatically, accepts different payment types, calculates taxes correctly |
| Sage Accounting | £15 to £39 | Uses AI to help with tasks, creates and sends bills, connects with bank accounts |
| Invoiced | Ask for price | Makes complex bills, chases late payments, lets customers pay online, connects with accounting software |
| Serrala | Ask for price | Works with SAP systems, tracks money in real-time, manages disputes, creates reports |
| Zoho Books | £20 to £165 | Sets up repeat tasks, sends payment reminders automatically, sorts payments by type |
Why use payment automation?
Get Paid Faster
Send bills instantly instead of manually creating each one. Customers get their bills sooner, and you typically get paid 15-30 days faster than with manual processes.
Better Money Management
Predictable payment timing makes it easier to plan your business finances. Real-time reports show exactly what money is coming in and when.
Save Time
Automation handles repetitive tasks like creating invoices, sending reminders, and tracking payments. This frees up staff time for more valuable work like customer service or business development.
Make Fewer Mistakes
Automated systems eliminate human errors like wrong amounts or forgotten reminders. Every invoice follows the same accurate template, reducing payment delays.
Keep Customers Happy
Consistent, professional invoices and timely reminders create a better customer experience. You can still add personal touches while maintaining efficiency.
Stay Legal
Automated systems keep proper records and follow tax rules, including Making Tax Digital requirements. This makes tax filing and compliance much easier.
How to start using automation?
Step 1: Look at Your Current Process
Map out how you currently handle payments from start to finish. Identify which tasks take the most time or have frequent errors. These are your priority areas for automation.
Step 2: Pick the Right Software
Choose software based on your business size, budget, and needs. Consider how many invoices you send monthly and whether you need features like recurring billing. Take advantage of free trials before deciding.
Step 3: Set Everything Up
Connect the software to your accounting system and payment gateways. Create professional invoice templates and set up automated reminder emails (typically at 7, 14, and 30 days overdue).
Step 4: Test Before You Start
Run complete tests with fake customers to ensure everything works correctly. Check that invoices send properly, payment links work, and money reaches the right accounts.
Step 5: Check How Well It Works
Monitor reports to track improvements in payment speed, time savings, and accuracy. Use this data to fine-tune your settings and maximize the benefits
The evolution shows how businesses moved from reactive to proactive approaches. AI represents the latest stage, offering predictive insights rather than just historical reporting.
Practical Example: Risk-Based Collections
Consider two customers:
Customer A:
- Amount owed: £50,000
- Days past due: 30+
- Risk score: Low (2% chance of default)
- Cash at risk: £1,000
Customer B:
- Amount owed: £30,000
- Days past due: 10+
- Risk score: High (50% chance of default)
- Cash at risk: £15,000
Traditional methods would focus on Customer A due to higher amount and longer delay. AI correctly identifies Customer B as the priority due to higher risk of loss.
Implementation areas
Credit Management
- Real-time order approval
- Online credit applications
- Automated limit reviews
- Risk alerts and monitoring
Collections Process
- Risk-based prioritisation
- Automated reminder sequences
- Collector workload management
- Customer communication tracking
Cash Application
- Automatic payment matching
- Exception handling
- Small balance write-offs
- Multi-currency processing
Dispute Management
- Automatic dispute identification
- Routing to correct departments
- Resolution tracking
- Root cause analysis
Challenges and Considerations
Data Quality Requirements
AI needs clean, consistent data to work properly. Poor data quality can lead to wrong predictions and bad decisions.
Companies must ensure their customer data is accurate and up-to-date before implementing AI solutions.
Integration Complexity
Most businesses use multiple ERP systems. AI solutions must integrate with all systems to provide complete visibility.
Source: FIS ERP Integration Experience (2024) – Over 90 different ERP interfaces have been successfully implemented, showing the technology can handle complex environments.
Staff Training Needs
Teams need training to understand AI recommendations and use new tools effectively. Change management is crucial for successful implementation.
Cost Considerations
Source: FIS Implementation Data (2024) – Initial setup costs vary based on company size and complexity. Simple implementations might take 1-2 months, whilst complex multi-ERP setups could take longer.
Source: Industry Analysis (2024) – Return on investment typically justifies costs within 6-12 months through improved cash flow and reduced labour costs.
Future of AI in Accounts Receivable
Emerging Technologies
Machine learning algorithms continue improving prediction accuracy. Natural language processing helps understand customer communications better.
AI can simplify reporting and internal support by consolidating accounts receivable data into dashboards, generating forecasts, and identifying trends.
Industry Trends
More businesses are adopting AI as costs decrease and benefits become clearer. Cloud-based solutions make advanced technology accessible to smaller companies.
Companies can achieve significant improvements in efficiency, accuracy, and cost savings in their AR processes through proper AI adoption.
Best Practices for Success
Start Small and Scale
Begin with one area like payment application or customer scoring. Expand to other areas once the team is comfortable with the technology.
Monitor and Adjust
Regular review of AI performance ensures optimal results. Adjust parameters based on actual outcomes versus predictions.
Maintain Human Oversight
AI should support, not replace, human judgement. Keep experienced staff involved in complex decisions and customer relationships.
Measure Results
Track key metrics like DSO, collection rates, and staff productivity. Regular measurement shows ROI and identifies improvement opportunities.
Case Studies
Global Manufacturing Company
Source: FIS Client Case Study (2024)
- Size: £86 billion annual revenue
- Results: 10+ day DSO reduction, 25% fewer overdue accounts
- Challenge: 15% increase in transaction volume handled simultaneously
- Success Factor: Risk-based collection strategies
Mid-Size Service Company
Source: FIS Webinar Example (2024)
- Challenge: Seven different ERP systems
- Solution: Centralised AI platform with multi-system integration
- Result: 90% reduction in manual payment application time
- Benefit: Staff redeployed to customer service roles
ROI Calculator
Source: Industry Benchmarks & FIS Data (2024)
| Metric | Before AI | After AI | Improvement |
|---|---|---|---|
| DSO (days) | 45 | 35 | 22% faster |
| Collection rate | 85% | 95% | 12% better |
| Staff hours | 40/week | 15/week | 63% reduction |
| Processing errors | 5% | 1% | 80% fewer |
Conclusion
AI in accounts receivable offers numerous benefits. It helps in faster payments to reduced manual work. It is transforming the traditional way of handling basic AR tasks like customer payment history, invoice and credit data, communication records and more. It is bringing accuracy, efficiency within a limited time frame now. Companies that adopt AI early gain competitive advantages through better cash flow and efficient operations.
FAQs about AI in Accounts Receivable
What size company needs AI in accounts receivable?
How long does AI implementation take?
What data does AI need to work effectively?
Can AI work with existing ERP systems?
How accurate are AI predictions?
Is AI secure for financial data?
Can AI handle multiple currencies?
What training do staff need?
How does AI improve customer relationships?
Parul is a dedicated writer and expert in the accounting industry, known for her insightful and well researched content. Her writing covers a wide range of topics, including tax regulations, financial reporting standards, and best practices for compliance. She is committed to producing content that not only informs but also empowers readers to make informed decisions.