RPA in Collections/Accounts Receivable (AR)
For all the hype that Robotic Process Automation (RPA) has created in the IT industry, its penetration leaves a lot to be desired. It has been studied that, the collections process (also known as Accounts Receivable or AR), despite being an excellent candidate for automation, has an RPA penetration of less than 15%!
What affects RPA adoption?
- Non-standardized processes
- Heterogeneous approaches used for different accounts/regions
- Lack of awareness on RPA and its advantages
- Limited emphasis on RPA evaluation
The first step in improving RPA adoption would be educating organizations on the applicability of RPA in AR and the advantages that RPA brings to the table.
RPA in AR workflow
- Employ multiple parameters such as invoice age and credit risk to prioritize collections
- Automated periodic reminders to delinquent customers
- Automated smart allocation of invoices to collection agents
- Automated sort of invoices
For example, a health insurance claim might need a swap between primary and secondary insurances in order for a claim to be paid. RPA can be used to make this decision and also automatically perform the swap.
- Automated “Scrub” of insurance claims for data adequacy and re-submit if necessary
- Incorporate credit risk analysis for better decision making
- Optical Character Recognition (OCR) technology improves reading accuracy of scanned documents
- Automated tracking and notifications for AR
Advantages of RPA in AR
Advantages of adopting RPA are multifold.
Reduce headcount (FTEs), perform regular Account Receivable tasks with improved Turnaround Time (TAT) and in turn ensure faster payments.
RPA helps organizations tackle heterogeneity by moving their decentralized AR processes into a central shared services setup, where RPA “bots” (software programs used for automation) take care of the bulk of AR processing, leaving just small bits of region/account-specific tasks to be carried out by the collection teams.
RPA, if implemented correctly with complementing Artificial Intelligence (AI) capabilities, can also be used to proactively identify payment/default trends and take preventive measures to reduce the burden on collection teams.
If you would like to continue this train of thought and understand more about RPA in AR, then drop us a line at email@example.com
Rajeev is a Business Analyst at iNatrix with an ever increasing curiosity for applicability of RPA in IT. He has previously worked on US Healthcare applications such as EHR and Pharmacy Benefits Management systems. He loves a good book and unplanned tours and is crazy about cricket.All stories by: Rajeev Karnam