How AI Powers Embedded Finance in AR

Key Benefits of AI in AR:

• Faster Payments: Payment processing is 63% quicker, saving up to 29% on costs.

• Automation: Tasks like invoice processing and remittance matching are 95% automated.

• Improved Cash Flow: Predictive tools forecast payment patterns and liquidity needs.

• Fraud Detection: AI monitors transactions in real-time to flag unusual activity.

• Customer Support: AI-powered chatbots and tailored payment plans improve satisfaction by 40%.

Quick Stats:

| <strong>Feature</strong> | <strong>Impact</strong> |
| --- | --- |
| <strong>Manual Task Reduction</strong> | 30-50% lower costs |
| <strong>Data Accuracy</strong> | 90% improvement |
| <strong>Outstanding Balances</strong> | 30% decrease |
| <strong>Customer Satisfaction</strong> | 40% increase

AI-powered tools like Tennis Finance and J.P. Morgan's COIN system showcase how automation saves time, reduces errors, and improves financial operations. With the AR automation market projected to grow to $6.4 billion by 2033, integrating AI into AR is becoming essential for businesses. This article dives into how AI is reshaping AR processes, from payment automation to predictive cash flow management.

Simplifying Accounts Receivable with AI

Payment Automation with AI

AI-powered payment automation is changing how accounts receivable (AR) functions by simplifying transaction processing and cutting down on manual tasks. This technology plays a key role in reshaping embedded finance within AR workflows.

AI Payment Gateway Functions

AI payment gateways bring a smarter approach to AR processes by using machine learning to make payment processing faster and more efficient. Some cloud-based systems even achieve straight-through processing rates of up to 90% . Here's a breakdown of their key features:

| Function | Impact |
| --- | --- |
| Invoice Processing | Over 95% of invoices are finalized without human involvement |
| Remittance Matching | Cuts processing time from 12 hours to just 15 minutes |
| Payment Routing | Optimizes in real time based on transaction patterns |
| Fraud Detection | Monitors continuously to flag unusual activity

One example is Tennis Finance, which uses an AI-powered AR automation platform to help businesses recover payments faster while reducing manual work. These advanced capabilities provide a structured approach to automating AR with AI.

Setting Up AR Automation

Follow these three steps to integrate AI payment automation effectively:

1. Assessment Phase
   

   Evaluate your current AR processes to find areas where automation can make the biggest impact. For instance, a healthcare provider reduced remittance time by 75% using this approach .

2. Implementation Strategy
   Start with high-impact areas. Taulia's AI system, for example, analyzes spending patterns and supplier relationships to improve payment terms and identify early payment discount opportunities .

3. System Integration
   Ensure AI tools connect smoothly with your existing workflows. LoveBrands, for instance, saved over 15 hours per week by automating manual processes .

Each of these steps helps speed up payment recovery and streamline AR tasks.

Integration Guidelines

Seamless integration requires careful planning around data, technology, and compliance. Here's what to focus on:

• Data Governance: Maintain high standards for data quality and security .

• MLOps Practices: Regularly update AI models to keep them effective .

• Performance Monitoring: Watch for data drift or system anomalies .

• Regulatory Compliance: Align with financial regulations to avoid risks .

Real-world examples show just how effective these practices can be. SAS Accountants helped a medical technology client cut days sales outstanding (DSO) by 75% during the COVID-19 pandemic using AI-driven AR solutions . Similarly, Huttie Group recovered $15,000 in previously written-off debt thanks to automation . These strategies ensure a smooth transition to AI-powered AR processes, highlighting its role in enhancing financial operations.

AI Prediction Tools for AR

AI prediction tools are transforming accounts receivable (AR) by analyzing data to forecast payment behaviors and improve cash flow management. These tools use historical information to provide insights that support smarter financial decisions.

Payment Pattern Analysis

AI systems analyze past payment data to find trends and predict customer behavior. They focus on several factors that influence payment timing:

| Factor | Role in Predictions |
| --- | --- |
| AP Run Dates | Helps determine the best invoice timing |
| Payment Approval Process | Highlights common delays in approval |
| Fiscal Calendars | Tracks seasonal payment trends |
| Order Patterns | Identifies shifts in purchasing habits

For instance, if AI identifies that a customer usually pays invoices five days late, it can adjust reminder schedules to send notifications earlier - ensuring payments are made on time . These insights also help businesses manage cash flow more effectively by predicting liquidity needs in real-time.

Cash Flow Management with AI

AI tools improve cash flow management by offering real-time analysis and predictive models. Forecasting remains a challenge for many AR teams, with 39% of professionals identifying it as their biggest hurdle .

Some key advantages include:

• Automated dashboards for better forecasting and performance tracking

• Risk assessment capabilities to flag high-risk accounts and develop focused collection strategies

"AI has proven to be a game-changer in treasury management, particularly in cash forecasting and payment prediction. By leveraging advanced data analysis and predictive modelling, AI enables businesses to anticipate liquidity needs with greater precision, optimize financial planning, and manage resources more efficiently." – Embat

Tennis Finance AI Tools Example

Tennis Finance (https://tennisfinance.com) showcases how AI can streamline AR processes. Their AI-powered tools have delivered measurable improvements:

• Collection planning is 8x faster with automation

• Collector productivity increases by 2–4x with AI support

• Response rates jump up to 10x through smart borrower interactions

These tools have helped businesses cut loan delinquencies by over 25% and reduce bad debt by up to 20% .

"AI solutions effectively automate both regular borrower follow-ups like payment reminders and complex debt recovery activities that involve regulatory reporting and restructuring poorly performing loans. Thus, on the one hand, AI-driven automation frees the lender's loan servicing team from low-value routines, driving better productivity. On the other hand, it aids the team in efficiently handling complex debt cases, ensuring minimized defaults." – Alex Bekker, Head of Data Analytics Department at ScienceSoft

Customer Service Improvements with AI

AI is reshaping accounts receivable (AR) customer service by providing faster, more tailored support while improving operational efficiency. Beyond technical advancements, it’s changing how businesses interact with customers in AR processes.

AI Support Tools

AI-driven tools like chatbots and virtual assistants deliver instant responses and simplify payment-related tasks. Here’s how they help:

| Feature | Benefit |
| --- | --- |
| 24/7 Automated Response | Quick answers to common payment questions anytime |
| Smart Invoice Access | Easy self-service for viewing and paying bills |
| Payment Status Tracking | Live updates on transaction progress |
| Multi-channel Support | Consistent responses via email, chat, and SMS

For instance, White House Utility District enhanced customer satisfaction with an always-available chatbot for billing and account management, earning a four-star rating . Similarly, Tidio's Lyro AI manages up to 70% of customer inquiries, streamlining support efforts .

Custom Payment Options

AI leverages customer data to suggest tailored payment solutions, improving both collection rates and customer satisfaction. These personalized strategies make AR processes more effective.

"All the data you have already collected about customers and their payments can be used to assist you with your workflow, make recommendations and suggestions, and even help you compile personalized payment reminders to these customers…that's exactly what we are doing right now. We offer an AI-powered, complete view of customers' activity across your entire AR process, enabling you to make intelligent AR decisions."
– Ahsan Shah, Senior VP Data Analytics, Billtrust

Some measurable benefits include:

• Online payment tools improve collection efficiency by 30% .

• Chaser users report getting paid three days faster on average .

• SMS reminders and automated calls help reduce payment delays by 38 days .

AI and Human Support Balance

While automation is crucial, combining it with human oversight creates a more balanced customer service experience. Younger customers often prefer automated solutions, but those over 40 lean toward human interaction for complex issues .

One example comes from an Asian bank that blended AI with traditional support options. This approach led to a 2–3× increase in self-service use, a 40–50% drop in service interactions, and over a 20% reduction in service costs . Similarly, Mercury, a fintech company, uses automation for routine tasks while reserving human support for more complex concerns. This strategy allows them to serve over 100,000 customers while enabling agents to focus on priority issues .

Security and Rules for AI Finance

Bringing AI into embedded finance means prioritizing strong security and sticking to strict rules to safeguard sensitive financial data. With cybercrime costs projected to hit $23.84 trillion by 2027 , it's clear that protecting AI-powered systems is non-negotiable.

Finance Rules for AI Use

When financial institutions adopt AI in their payment systems, they must comply with various regulations. Here's a breakdown of key areas and their requirements:

| <strong>Compliance Area</strong> | <strong>Requirements</strong> | <strong>Impact</strong> |
| --- | --- | --- |
| Know Your Customer (KYC) | Identity checks and due diligence | Prevents fraud and money laundering |
| Transaction Monitoring | Analyzing payment patterns in real time | Detects suspicious activity |
| Data Protection | Adherence to GDPR and CCPA | Protects customer privacy |
| AI Governance | Transparent decision-making processes | Ensures accountability

For example, Bank of America uses an AI fraud detection system that compares transactions against historical data, flagging unusual activities. This approach has significantly reduced fraud losses .

"Banks need to be transparent about the monetization of personal data and establish effective governance of inflows where they are joint data controllers with third-party providers and operate a shared risk model. This includes being confident in the operational resilience of embedded finance partners." - Elena Matova, SoftServe

Beyond compliance, technical security measures are just as important.

AI System Security Steps

J.P. Morgan's COIN system is a great example of how advanced security can be integrated into AI systems. It uses encryption, access controls, and continuous monitoring to safeguard financial operations .

Key security measures include:

• Multi-factor authentication to verify user identities.

• Real-time monitoring to catch threats as they happen.

• Data encryption for secure storage and transmission.

• Fraud detection algorithms to flag unusual activities.

• Regular security audits to uncover and fix vulnerabilities.

These steps help ensure AI systems remain both efficient and secure.

Data Protection Guidelines

Protecting customer data requires a layered approach that combines technology and strong processes. Here's how:

| <strong>Protection Layer</strong> | <strong>Implementation</strong> | <strong>Purpose</strong> |
| --- | --- | --- |
| Data Governance | Access controls and monitoring | Prevents unauthorized access |
| Encryption | End-to-end encryption | Secures data in transit/storage |
| Audit Trails | Detailed logging of activities | Enhances security monitoring |
| MLOps Integration | Version control for AI models | Preserves model integrity

"Ensure AI models are transparent, explainable, and aligned with ethical standards. Avoid biases and promote fairness to build trust with stakeholders and meet regulatory demands for accountability." - Prove AI

Regularly updating security protocols is essential to address new threats . These layers of protection create a strong foundation for secure and reliable financial operations.

Conclusion

AI is reshaping how embedded finance handles accounts receivable (AR) operations. With the global AR automation market expected to hit $6.4 billion by 2033 , more companies are turning to AI to simplify and improve financial processes.

AI has the potential to automate 60-70% of manual accounting tasks . Tools like Tennis Finance help businesses recover payments faster, cut down on manual labor, and improve cash flow visibility.

"When purposefully deployed, AI can significantly improve AR processes by automating routine tasks, increasing accuracy and providing valuable predictive insights. These improvements enhance cash flow management and create a more efficient AR operation." - Rebeca Bichachi, Product Marketing Specialist, NetSuite

Here’s a quick look at how AI is making a difference in AR:

| <strong>Area</strong> | <strong>AI-Driven Improvement</strong> | <strong>Business Impact</strong> |
| --- | --- | --- |
| Payment Processing | Automated remittance matching | 40% reduction in manual effort |
| Deduction Management | Auto-coding and claim resolution | 30% improvement in recovery rates |
| Risk Assessment | Dynamic credit evaluation | Real-time risk monitoring and adjustment |
| Cash Flow | Predictive analytics | More accurate forecasting and planning

These advancements highlight the power of AI in transforming AR operations.

As the financial landscape continues to evolve, combining AI with embedded finance offers a clear way forward. To succeed, businesses must prioritize security, compliance, ethical practices, and a thoughtful balance between automation and human oversight. By leveraging these tools, companies can enhance efficiency and better meet customer needs.

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