From the C-suite to sales, all strategic business functions depend on the financial function for structured financial data and accurate analysis to make timely and informed business decisions. The finance team is also entrusted to increase efficiency, reduce operations costs, improve cash flow, comply with financial regulations and mitigate risk exposure.
With this increase in strategic objectives, Finance teams are challenged in terms of time, and resources to eliminate the mundane, time-consuming, and repetitive tasks, to provide them more time to focus on strategic high priority initiatives.
The Chief Financial Officer (CFO) office needs to focus on optimizing legacy processes and explore automation opportunities to drive operational efficiencies.
Accounts receivable (AR) automation has become an essential and dominant focused area in the financial management of an organization. Historically, it has been a manual, labour-intensive process intensive. Business challenges, such as high debt and low fund growth are the results of poorly managed AR processes.
AR team manages the outstanding invoices or the money owed by clients for products or services sold on credit. A streamlined and efficient AR process positively impacts finance, sales, customer service, and overall operations.
Automating your AR processes helps the team save time and focus on work that drives strategic outcomes instead of mundane, repetitive tasks , such as a high volume of invoice matching, verifying payments, emailing invoices, determining the outstanding invoices for follow-up, sending payment reminders, listing short payments, and verifying disputes. Intelligent automation also facilitates exhaustive analysis of purchase orders, sales quotes, customer credit scores, invoices, and payments.
RPA (Robotic process automation) is ideal to handle mundane, repetitive tasks without human intervention and has gained a lot of popularity in the automation of financial processes amongst mid-market organizations. Gartner’s research shows that around 80% of finance leaders have already implemented or are planning to implement RPA.
The basis of RPA is its capability to mimic human actions to automate at user interface (UI) level. RPA uses software ‘bots’’’ to handle repetitive, transaction-heavy, time-consuming tasks, such as invoice processing, data entry, and compliance reporting and minimizes errors in processing associated with human involvement.
RPA solutions offer a high level of security to finance functions, and they work without interruption for substantial cost savings. Pure RPA can perform rule-based repetitive tasks, and it is much lower in cost when compared to AI and ML-based automation software. RPA tools can also interact with a wide range of critical applications, such as enterprise resource planning (ERP) and customer relationship management (CRM) platforms.
RPA is cost-effective at processing large volumes of rule-based collections tasks and enables lowering Days Sales Outstanding (DSO) value in the AR function. RPA augments the effectiveness by achieving more with fewer resources in less time. However, using RPA to automate the entire AR process where there is the scope of variations to the defined rules, requires creating an exception path to the RPA workflows. And this is time taking and also necessitates manual intervention for exceptions and decision making.
There are several scenarios in an AR workflow where simple process automation does not provide adequate results. RPA comes with its own set of limitations in AR process automation.
RPA is a purpose-built software and an RPA bot follows a specific set of rules to complete a task. But an RPA bot is not dynamic and any changes such as the creation of new orders template in the AR process will require rescripting of bots. Thus, even though RPA comes with a promise of eliminating tedious manual tasks, it increases the workload on the IT team due to a lack of adoption to new process changes.
In some of the AR processes, such as the Cash application, to capture customers’ remittance information from check stubs or emails, all the steps can’t be automated directly by using rule-based RPA tools. Instead, it would require AI or ML capabilities, and OCR engines. These additional technology components require additional investment and skilled developers. However, the additional investment still does not guarantee the results desired by mid-sized organizations..
It is difficult to handle complex scenarios with RPA-based solutions. Such as while extracting remittance information from emails, an RPA bot can pull out the information and upload it. But the bots don’t have the ability to check the authenticity of the information. If the information is incorrect, the analyst will have to manually check the process and rectify it. This scenario is just one of several examples where even though RPA is able to speed up the AR process, it is unable to eliminate errors. There are other scenarios like creating a prioritized worklist, auto credit scores, solving missing remittance cases, credit approval workflows where RPA has not been very successful in providing desirable output.
RPA integration with third party sites such as payment portals, banks, credit agencies, etc requires bots to be built, which requires about 4 to 6 weeks. Web scraping bots tend to be fragile and fail as and when information elements and their position on the website changes. Furthermore, whenever the process is updated, the RPA navigations and instructions also need to be reconfigured and integrated with ERP, which is complicated and requires IT involvement.
Automation is not limited to RPA, automation can also include RPA alongside APIs, customized code, AI or ML, or off-the-shelf software. Once routine processes are automated using RPA, you can apply AI to orchestrate human-like intelligence to the bot-driven automation process, create the data you need, and then make business decisions at speed.
RPA and Artificial Intelligence(AI) are complementary to one another. While pure RPA can perform rule-based repetitive tasks, it hits a wall when faced with unstructured data. Mid-sized organizations can tackle semi-structured and unstructured data using Machine Learning(ML), deep learning, computer vision, and cognitive technologies.
AI boosts the power of RPA by preventing RPA bots from failing if any underlying rules change in external sites. AI helps in predictive analysis and finding patterns in historical data to identify the most relevant information to support contextual and informed decision-making.
A combination of RPA, integrated workflows, and AI/ML creates a much more holistic and formidable automation process for AR. It aids in decision-making on matters such as delinquent account prioritization and setting customers’ credit limits.
At the same time, the finance teams can also focus on root cause analysis which helps speed up the process. The combination of RPA, OCR, and AI/ML increases the percentage of AR automation to 80-90%in midmarket companies.
To tackle and overcome the limitations of stand-aloneRPA, HighRadius offers its holistic RadiusOne suite for mid-sized businesses.
The HighRadius RadiusOne AR Suite includes a set of AI-powered solutions designed to support AR processing for mid-sized companies across industries with a complete order-to-cash (O2C) solution. Automate and fast-track key functions with RadiusOne AR applications, pre-loaded with industry best practices, and ready-to-use with popular ERPs including NetSuite, Sage Intacct, Microsoft Dynamics, and Infor.
The HighRadius RadiusOne AR Suite includes automation solutions for processes including e-Invoicing & Collections, Cash Reconciliation, and Credit Risk Management.
RadiusOne AR apps are designed to suit the needs of midmarket AR teams and aim at automating labor-intensive processes, maximizing working capital, and enabling faster cash conversion.
With all these features in RadiusOne’s arsenal, it delivers exceptional results for its users consistently. With its adaptive and dynamic nature, RadiusOne enables end-to-end AR automation through a combination of AI and RPA to improve quality control, lower operation costs, and increase efficiency in midmarket companies.
RPA and AI/ML capabilities together meet a wider range of automation requirements and deliver a greater degree of automation for an end-to-end process. The out-of-the-box features in RadiusOne help in automating end-to-end AR processes in mid-market companies by combining RPA and AI/ML capabilities. AI/ML handles complex processes that require cognitive decision-making ability for a certain task to be performed and this is where RadiusOne differentiates itself from stand-alone RPA.
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.