Accounts receivable (AR) is the future influx of revenue for a good or service sold today. But there are complexities inherit to it. For instance, one challenge companies face is they miscalculate the DSO (days-sales-outstanding), the time between a good sold and the payment received.
According to Pwc, DSO showed a 3.2% decrease down to 51.7 yet still hasn’t fully recovered from the rise in 2019. 1 With rising inflation and drive for efficiency, businesses desire to reduce DSO and eliminate miscalculations. AR automation with accounts receivable software provides a way to achieve that and save costs without harming customer relationships.
Especially for enterprises, a systematized inefficiency in the AR process will slowly, but surely, wreak havoc on the business model. In this article, we will discuss the top 4 use cases of accounts receivable automation in the workplace, and the benefits automation will bring with it.
1. Calculating and monitoring DSO
DSO is the time between when you sell a good (on credit) and get paid. A high DSO means there are more days between sale and collection. Businesses should always measure DSO with respect to their budget allocation plan so they do not find themselves with less cash than planned in the short term before receiving payment. Low liquidity, in the extreme, can lead to insolvency because the business simply doesn’t have the cash to cover its day-to-day expenses.
Moreover, the DSO metric should be closely monitored for strategic purposes. For instance, it might be rising because the only way the salespeople can push the products is by offering generous payment terms to customers. That’s not a good reflection on the quality of the product. So real-time monitoring of the DSO is paramount.
2. Checking credit
The financial credibility of your customers has a correlation with their payment timing. Arguably, the cornerstone of the 2008 housing crisis was the fact that home-buyers with bad credit ratings were allowed to get mortgages that were not likely to be paid back. To avoid bad and delinquent debts, businesses should perform accurate Know Your Customer (KYC) processes. That requires implementing a clear and consistent credit policy, rating, and approving. We have an article that discusses credit scoring models in more depth if you’re interested.
3. Keeping up with the customers
Keepin records of their customers, their amounts outstanding, payment structure, debt denomination, and all other pertinent information is a part of healthy accounts receivable management for businesses. This information is usually on invoices. It can be a good idea to store them in a singular location to have them accessible on-demand.
Moreover, if an invoice is not paid, you should be notified promptly so you could communicate with the customer and remind them of their obligation to pay and collect your receivables.
Use automated invoice generators to create digitized invoices from sales done on different commercial channels. This will allow you to have a digitized, transferable version of the data that you can transfer to other ERPs through EDI to take action on.
4. Collecting receivables
As mentioned briefly above, the AR process ultimately boils down to payment collection. Your business will cease to exist if it’s not injected timely with influxes of cash for the goods or services it provides.
We’ve already mentioned how automated solutions automatically keep track of the customers’ invoices, remind them of their obligations on the due date, and collect payments accordingly. But there are some pro tips that you can take advantage of in your automated solution to achieve the best results.
You can incentivize your customers to pay faster by offering them discounts. Although it’s not a profitable, long-term strategy, it’s appropriate for overdue debts and customers who have shown to be unreliable. Just with traffic fines where the government incentivizes violators to pay their fees quicker by offering some markdowns, you can define commands so the solution intermittently sends emails to overdue customers that they will be offered pre-calculated and approved discounts if they settle their debts quicker.
Moreover, automated solutions are integrated with payment processing software applications that are capable of receiving payments from a multitude of different channels (different credit card companies, wire transfers, money orders, cheques, etc.) If you offer your customers more payment gateway options, you will give them more possibilities to pay their debts.
Automating AR Processes
Automating DSO calculations can help companies prevent losses early. Enterprise Resource Planning (ERP) systems, like Oracle or Microsoft Dynamics, have built-in tools for accounts receivable that can automate DSO calculations.
Business Intelligence Tools (BI) tools like Tableau can also connect to different data sources, extract sales and collection data, and monitor real-time DSO screens.
As FSI global puts forward, measuring DSO presents challenges and must be measured consistently to have meaningful information.2 These software may not always be sufficient to solve the problems inherent in DSO calculations. Such as bad-debt write-offs and dolar exposure.
It won’t be possible to detect AR teams’ performance without considering evolving information such as:
- Differences in measurements
- Seasonal fluctuations
AI technologies not only measure and track DSO, but they also use historical data to make predictions about how DSO will change in the future.
In checking credits, Robotic Process Automation (RPA tools) can be set up so that credit notes and outstanding bills are automatically retrieved, checked, and matched. They can interact with multiple systems without having to integrate them in a complicated way. OCR technologies can also digitize paper credit notes so that the data can be used by integrated systems or RPA bots. This is especially helpful when working with paper credit notes.
In automating the process of checking customers Customer Relationship Management (CRM) software like Microsoft Dynamics CRM give a full picture of all customer interactions, from sales to help questions. AR systems can be linked to these platforms to help keep track of invoices, payments, and other billing-related exchanges. IDP software, on the other hand, can do more than capture: it can understand, extract and categorize the data on invoices. This way, when combined with deep learning and NLP features, businesses can track relevant data with customers on invoices.
In collecting receivables, integrations with APIs can help. Many platforms have APIs that let different systems integrate each other. For example, connecting a CRM to an invoicing tool can automatically create invoices based on sales records, and connecting it to a payment gateway can let you monitor payments in real time.
In automating documents, accounts receivable teams still tackle manual tasks such as retrieving, copying and pasting key information and tracking them afterwards. Software solutions such as IDP and OCR can help. Yet, APAI software can take the process a step further with the use of continual learning technology and GPT models. These solutions offer an autonomous character that can help automate AP and AR steps and keep the process going by itself.
For more information on topic, You can check out our comprehensive, data-driven list and research articles on the topic:
- Accounts Payable AI Platforms
- 10 AI Applications in Accounts Payable (AP) Processes for 2023
- 7 Vic.AI Alternatives to Automate Accounting in 2023
What are accounts receivables?
Accounts receivable is the money that customers owe to a business after buying goods or services on credit. They are important for a company’s balance sheet, showing expected cash inflows from clients.
How do accounts receivables differ from accounts payable?
While accounts receivable represents the funds owed to a company by its clients, accounts payable is the exact opposite. Accounts payable involves the money a company owes to its suppliers or vendors, highlighting the outgoing funds that needs to be paid out.
What is an example of accounts receivable?
Imagine a business offers services to a client and issues an invoice with a 60-day payment term. If the client hasn’t paid the invoice immediately, the amount due becomes an accounts receivable until the client realize the payment.
What is accounts receivable analysis?
Accounts receivable analysis is a way for businesses to figure out how well and how efficiently they handle credit and collections. Through this analysis of the accounts receivable process, businesses can measure the pace of their payment processing and the consistency of customer payments.
Why to automate accounts receivable processes?
Automating accounts receivable processes is essential to transform time-consuming and error-prone manual accounts receivable processes into efficient operations. Without an AR automation solution, manual processes there can be challenges that affects cash flow such as delays in customer payments. Companies can reduce the burden on the accounts receivable team by adopting AR automation software, that can:
-speed up collection of customer payments
-eliminate the risk of late payments with payment reminders
-electronically process invoices
-ensure accurate credit management
-optimize cash flow
For more on finance
If you want to learn more about the other technologies used in the financial sector, read:
- Top 8 RPA Use Cases & Examples in Finance
- Real-Time Asset Valuation& Asset Accounting Automation
- Finance Automation: Use Cases, Technologies & Benefits
And if you believe your enterprise would benefit from a fintech solution, we have data-driven lists of vendors prepared.
We will help you choose the best one suited to your business:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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