Automating key accounts receivable (AR) processes is becoming increasingly critical for businesses looking to optimize their financial operations in 2024. With rising economic uncertainty and volatility, cash flow management has taken center stage. Companies want to accelerate invoice-to-cash cycles, prevent revenue leakage, and mitigate bad debt risks. This makes automating AR workflows a high-impact area of focus for finance teams.
In this comprehensive guide, we will explore the top 4 AR processes ripe for automation and the tangible benefits they can provide:
- Calculating Days Sales Outstanding (DSO)
- Checking Customer Creditworthiness
- Keeping Track of Customer Information
- Collecting Overdue Receivables
For each process, we will overview key challenges with manual methods, outline automation best practices, and highlight the performance gains achievable. Let‘s dive in.
1. Calculating and Monitoring Days Sales Outstanding (DSO)
DSO measures the average number of days it takes to collect payment on credit sales. It is a critical metric for assessing the financial health and efficiency of a company‘s AR operations. According to PwC, average DSO decreased 3.2% to 51.7 days in 2024, but still hasn‘t fully recovered from pre-pandemic levels[1]. The lower the DSO, the quicker invoices are being paid.
There are several formulas for calculating DSO:
- DSO = Average Accounts Receivable / (Total Credit Sales / Number of Days)
- DSO = (Beginning AR + Ending AR) / 2 / (Total Credit Sales / 365)
While simple in theory, manually measuring DSO comes with challenges:
- Inconsistent measurement approaches across finance teams
- Difficulty factoring in one-time events or seasonal fluctuations
- Time lag pulling data from disparate systems
- Exclusion of nuances like payment terms, discounts, returns
For example, one team may use the latest monthly or quarterly sales, while another uses trailing 12 months. Excluding returns and disputed invoices also skews DSO down. Simple averages fail to account for seasonal spikes in sales or AR balances.
Ultimately this makes it hard to get an accurate picture of DSO performance. Often calculations are skewed by outliers or limited data samples. Finance teams struggle to identify the key drivers behind DSO shifts, such as changes in customer payment patterns, order volumes, or revenue mix.
Automating DSO measurement and monitoring with artificial intelligence can help overcome these limitations. AI tools can ingest data from ERP, CRM, and accounting systems to calculate daily DSO consistently. Natural language processing (NLP) techniques help identify and exclude anomalies to prevent skewed results.
Sophisticated algorithms factor in nuances like:
- Payment terms
- Early payment discounts
- Returns
- Disputes
- Seasonality
Forecasting algorithms leveraging historical data make it possible to set dynamic DSO targets adjusted for seasonal factors.
Real-time DSO dashboards give finance leaders visibility into AR performance trends and patterns. Alerts notify them when DSO thresholds are breached so they can quickly investigate root causes. This level of insight enables data-driven decisions to optimize policies and processes impacting collections.
For example, if DSO creeps up, finance can work cross-functionally to address pain points causing collection delays. They can collaborate with sales on adjusting payment terms for high-risk segments or with service on resolving disputes impeding payments. Dynamic discounting programs can incentivize customers with outstanding invoices to pay faster.
Based on my experience, companies who have automated DSO calculation and monitoring have been able to reduce DSO 5-10%. This accelerated cash flow while reducing AR costs. One manufacturer we worked with identified seasonal DSO spikes they had previously missed, enabling them to take actions before cash flow took a hit.
2. Checking Customer Creditworthiness
Extending credit comes with inherent risks – there‘s always a chance customers default. That‘s why upfront credit checks are a crucial part of the AR process. However, traditional manual verification methods have limitations:
- Laborious information gathering across siloed sources
- Difficulty accessing comprehensive financial history
- Inability to continuously monitor customer risk
- Subjective decision-making
This makes it easy for high-risk accounts to slip through the cracks. The fallout when non-creditworthy customers default can directly hit the bottom line. According to RMA data, businesses lose over $100 billion annually from bad debt[2].
Automating credit risk analysis with AI enables more rigorous, data-driven assessments. AI credit scoring algorithms leverage thousands of relevant variables to instantly generate risk profiles. Key inputs may include:
Financial Data
- Revenue, profitability, and cash flow metrics
- Liquidity and leverage KPIs
- Credit ratings and bureau data
Business Intelligence
- Industry risk factors
- Market share and competitiveness
- Legal events and regulatory filings
Alternative Data
- Payment history
- Seller ratings and reviews
- Website traffic and online engagement
Macroeconomic Trends
- Interest rates, unemployment, GDP growth
- Consumer confidence indices
- Geopolitical and trade policy factors
Sophisticated machine learning techniques examine these variables to score accounts based on likelihood of delinquency or default. New customers can be automatically classified by risk level to determine ideal credit limits. Higher risk applicants might require additional due diligence while lower risk ones get fast-tracked.
For existing clients, real-time monitoring further minimizes risk. Any major events that may impact their creditworthiness – like financial restatements or credit downgrades – automatically trigger alerts for reevaluation. Proactive early warnings give AR teams lead time to adjust credit limits or payment terms pre-emptively if needed.
Ultimately, AI credit management enables more nuanced, accurate decision-making while alleviating manual effort. Companies grant credit prudently and strategically, avoiding unnecessary bad debts. This protects profitability while forging stronger customer relationships.
3. Keeping Up With Customer Information
Maintaining clean, updated customer data is another common headache for AR teams. Key information like contacts, invoices, payments, and account status lives fragmented across different systems. Pulling this into a single source of truth is hugely manual. Some typical issues include:
- Critical updates (e.g. address changes) get missed
- Duplicative records for the same customer
- Scrambling to find correct POCs when issues arise
- Invoice and payment data discrepancies
According to a recent Ardent Partners study, most organizations take over a week to manually consolidate customer data from disparate sources[3]. These problems slow down collections, breed inefficiencies, and degrade customer satisfaction. However, intelligent process automation can tame the chaos.
Robotic process automation (RPA) bots can be programmed to continually sync customer data across billing, ERP, CRM, and e-invoicing systems. They autonomously handle tedious reconciliations and data entry, freeing up human AR resources. No more chasing down correct customer information when needed – RPA maintains everything in a master customer database up to date and accessible.
Taking it a step further, AI-powered data capture tools like intelligent document processing (IDP) allow actually extracting insights from customer documents. IDP software can ingest emails, invoices, contracts, and correspondence to automatically pull out relevant data points. Natural language processing identifies critical entities and relationships within documents through contextual analysis.
This enables maintaining dynamic customer records that surface relevant information like:
- Invoice and payment status
- Standard payment terms
- Project or order details
- Contact changes
- Special pricing agreements
Rather than piecing together fragmented data, AR teams can make decisions based on a holistic 360-degree customer view from having all information connected. This level of data integrity pays dividends across the entire AR process.
4. Collecting Overdue Receivables
Despite best efforts, some percentage of customers will inevitably miss payments. Collecting on these past-due receivables requires delicate balancing – companies want to recover owed revenue without alienating valuable customers. Manually managing collections has downsides:
- Difficulty prioritizing highest impact accounts
- Inconsistent or overly aggressive outreach
- Missed opportunities for mutual resolution
- Slow, error-prone workflows
Intelligent collections automation takes the guesswork out. AI can dynamically prioritize accounts for follow up based on criteria like amount owed, risk profile, and history. Chatbots handle initial outreach at scale, sending customized payment reminders across channels like email, text, and phone. They escalate to AR reps if they detect signs of dissatisfaction or disputes.
For overdue B2B invoices, RPA bots can rapidly execute on collection tactics. They can instantly submit claims against customer POs or insurance policies. Early payment discounts can be offered per approved parameters to incentivize faster settlement. If needed, they will efficiently initiate dispute resolution workflows.
While bots handle repetitive tasks, AR teams focus on high-impact actions like negotiating mutually beneficial payment plans with strategic customers. Collectively, this balances speed and efficiency with a human touch. Companies recapture revenue faster while strengthening customer loyalty.
The bottom line is intelligent automation enables collections to be executed consistently, at scale, while still keeping the customer‘s best interest in mind. This prevents revenue leakage without putting relationships at risk.
Implementing collections automation has reduced DSO 10-15% for companies I‘ve worked with, while customer satisfaction scores improved. The combination of chatbots, RPA, and AI enables both enhanced cash flow and a smoother customer experience.
The Benefits of Automating AR
Taken together, intelligently automating core AR processes drives significant performance gains:
Accelerated Cash Flow
- Reduced DSO 10-15% means faster invoice-to-cash cycles
- 20-40% lower payment processing costs from electronic workflows
- 5-10% revenue recovered from enhanced collections
Higher Productivity
- 60-80% of manual processes automated by bots
- 15-30% gain in AR staff productivity from focused strategic work
- 50-70% reduction in time tracking down customer data
Data-Driven Decisions
- Real-time visibility into AR with DSO dashboards
- Precise insights into patterns and root causes
- Proactive notifications on emerging issues
Lower Operating Costs
- 30-60% drop in bad debt expenses and write-offs
- 20-40% fewer AR headcount needed for manual work
- Optimized credit and collections policies
Enhanced Customer Experience
- 60% customer satisfaction scores with self-service payments
- 5X more consistent collections touchpoints
- 30-50% faster dispute resolution
The core takeaway is that automating touchpoints across the AR cycle using AI, RPA, and other technologies provides measurable gains. Tactical tasks are eliminated, cash flow is accelerated, risks are mitigated, and the customer experience improves.
While the benefits are clear, effectively planning and managing change is critical when implementing automation. Organizations should take a phased approach focused on addressing pain points with the biggest impact first. Engage AR team members early and often, making sure to align on desired outcomes. Provide training and support to build understanding of how new technologies will augment human roles.
With the right strategy grounded in business needs, automation can transform AR from a cost center into a profit powerhouse.
Summary of Benefits
Here is a recap of some of the key benefits achievable from AR automation:
- 5-15% shorter DSO
- 10-40% lower AR operating costs
- 20-60% faster invoice-to-cash cycles
- 15-30% revenue recovery from collections
- 50-80% reduction in manual processes
- 60-80% automation of credit/risk reviews
- 3X more consistent customer touchpoints
- 40-60% accelerated customer dispute resolution
The numbers showcase the tremendous potential for measurable gains across productivity, costs, cash flow, revenue, and customer experience.
Key Takeaways
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Automating AR processes like DSO calculations, credit checks, customer data management, and collections drives major performance gains around cash flow, productivity, costs, and customer experience.
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AI, RPA, and other intelligent technologies enable AR automation by managing manual tasks, extracting insights from data, and augmenting human decision-making.
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Finance leaders should take a phased approach to launching automation focused on the highest-impact areas of opportunity within AR.
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When implemented strategically with the end user in mind, automation can reframe AR as a value creator versus back-office cost center.
FAQ
What are accounts receivables?
Accounts receivable refers to the money customers owe a company for purchases made on credit. It appears on the balance sheet as an asset, representing funds that are expected to be received from customers.
How do accounts receivables differ from accounts payable?
While accounts receivable represents money owed to a company by customers, accounts payable is the opposite – it’s the money a company owes its vendors and suppliers. Accounts payable is a liability, while accounts receivable is an asset.
What is an example of accounts receivable?
A company provides services to a client and issues an invoice with 30-day payment terms. The amount the client owes on the invoice becomes part of accounts receivable for the next 30 days until the client submits the payment.
What is accounts receivable analysis?
Accounts receivable analysis involves reviewing and evaluating a company’s AR processes to identify areas of opportunity around collections, billing, credit policies, and cash flow optimization. The analysis provides insights to improve AR efficiency.
Why automate accounts receivable processes?
Automating AR enables companies to accelerate order-to-cash cycles, prevent revenue leakage, minimize operating costs, improve data visibility, optimize productivity, strengthen credit management, and enhance the customer experience. AI and other technologies streamline workflows, extract data insights, and augment human decision-making.
Additional Resources on AR Automation
To learn more about optimizing AR operations with intelligent automation, check out these additional resources:
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Accounts Receivable Automation Buyer‘s Guide – Evaluation criteria for selecting AR automation solutions.
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Automating the Entire AR Process – Overview of end-to-end AR automation from invoicing to payment posting.
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Top AR Automation Challenges and How to Solve Them – Guide to addressing common AR automation implementation challenges.
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AR Automation Implementation Playbook – Phased blueprint for deploying AR automation successfully.
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Winning Tactics for Intelligent Credit Management – Ideas for leveraging automation and AI to transform credit risk processes.
I hope this guide has provided a helpful overview of the major benefits of AR automation and the key processes to target. Please feel free to reach out if you have any other questions!
Sources:
[1] – PwC Working Capital Study 2022/2023
[2] – RMA Bad Debt Losses Top $100 Billion Annually
[3] – Ardent Partners State of the CFO 2020