Since childhood, I’ve always been in awe of all those people who could solve the Rubik’s cube. Throughout my life, I’ve tried solving it multiple times and was only successful in matching one face color at best (the adjacent colors were not correctly aligned). And then recently, I made yet another attempt. But this time, I spoke to a few people who could solve it, saw a few videos on YouTube and figured something out; something that I probably already knew and used it in other aspects of life but it didn’t strike me to use it to solve the cube. The key in solving a complex problem (like the Rubik’s cube) lies not in trying to solve it in its entirety, but solving one small problem at a time while keeping the larger objective in mind. E.g. when attempting to solve one color of the cube, I had to keep in mind that the adjacent face colors kept matching, i.e. solving it layer by layer.
In the same way, solving an AR problem requires solving one process (color) at a time while keeping in mind the adjacent process (color). Very often we have seen organizations attempting to solve just the Cash Application process while assuming the Deductions team/ process is fairly streamlined only to end up in a soup with their Deductions process, or tried solving the Deductions process only to realize that they have more issues now in their collections process. The AR problem is once again a complex one, much like the Rubik’s cube and needs to be resolved one process (color) at a time, but without forgetting that correlation with the adjacent process (color).
While the challenge has been around since the dawn of time (businesses have been around forever, and accounting has always been a key part of it), multiple attempts have been made to solve it. One of the approaches gaining popularity in the 21st century is the “Integrated Receivables’ (IR)” approach that uses software to automate and streamline mundane AR tasks. The benefit of this approach is many fold. While it helps streamline one process at a time, one also gets to see different parts of the engine within the AR process starting to engage with each other, to reach a common goal i.e. get money in the system faster (increase working capital). It also gives better visibility from a Manager, Director or CXO level to view the whole process as one, identify key areas of improvement and thus help smoothen the operation of the engine. While the approach has many more benefits, the last one I think worth mentioning is that the IR approach is modular, scalable and customizable. It’s not a one size fits all solution and is tailored based on needs and affordability.
Let’s try to further understand this approach with the help of an example. Imagine a company ABC Products who do business with your organization. Based on the information ABC Products initially gave you, you set a risk category and credit limit as a part of your Credit initiation process (Credit management). However, over a period of time, you have learned an order placement and payment behavior pattern of ABC Products (predictive analytics). You realize that ABC Products is about to place their next order, but are already close to their credit utilization. The system would automatically send out an email notice to ABC Products (auto correspondence) that they are close to their Credit Limit Utilization, and provide them with a link to pay the invoices (as a part of the Collections process). ABC Products can then click on that link, which takes them to a self-service portal and gives them an option to select the invoices they would like to make payments for. If they do want to make short payments and start a dispute (Deductions/ dispute management), they can choose the appropriate reason code. Once the payment is made, the system would automatically clear out the open invoices that were paid for and post the cash on account for ABC Products (Cash Application). Along with doing all this, information is relayed back to free up the credit utilization, and the system continues to run smoothly with minimal human intervention. As you may have noticed, the entire process is closely integrated and would require IR approach to solve it.
In this approach, we start with the 5 main pillars of the AR process i.e. Credit, Collections, Deductions, Cash Application and Electronic Invoices Presentment and Payments (EIPP). After understanding the overall processes for each one of the above-mentioned pillars, we identify the most important areas that need improvement and use that as a starting point. While attempting to solve that, we do keep in mind that how changes to this pillar can impact other processes (solving two colors of the cube at a time). Once the process has been resolved, we then move on to the next pillar (next layer in the cube). This iterative process leads to solving the entire Accounts Receivables process in a stepwise approach.
The “Integrated Receivables” approach has been developed by HighRadius, a leading software company that focuses exclusively on automation of the Accounts Receivables process.
After 550+ implementations over 11 years, HighRadius has managed to streamline AR processes across industries for clients such as P&G, Cargill, Schlumberger, Starbucks, GE, HP, Johnson & Johnson, Kellogg’s, Sony, Airgas to name a few. While some have seen tremendous improvement in DSOs, others have reduced write-offs/ bad debt, most have improved their Cash Application process; there is one thing in common: they all have improved their Working Capital.
Please share your feedback/ comments on the above-mentioned article and also let me know how you may have solved the Account Receivables process at your organization.
As far as the Rubik’s cube is concerned, my personal best time is 127 sec. I’m working on improving it to under 2 minutes. Let me know if you need help in learning how to solve that.
For further reading on articles related to AR automation, please click on the below links
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HighRadius Integrated Receivables Software Platform is the world’s only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway – covering the entire gamut of credit-to-cash.