The Data Scientist Explains: AR Automation
Robots aren’t just characters in science fiction movies, they are an integral component of what keeps businesses moving fast enough to keep up with and surpass the competition. Better than simple machines, robotics can handle tasks in more sophisticated ways and use artificial intelligence to learn and increase efficiency over time.
At Billtrust, we develop software solutions using robotics and machine-learning to automate AR processes and help businesses accelerate their invoice-to-cash cycles. Read Part 2 of our 4-part series on accounts receivable (AR) process automation. Farhad Khalafi, Research and Development Scientist at Billtrust, explains how automation helps move AR process forwards.
QUESTION: How are robotics/AI being used today in Credit and AR functions?
Farhad Khalafi: Software robots are widely used in all areas of credit and AR processing. But you might ask, how does this work?
When specific data arrives within the system, such as a bank payment, a robot will wake up, look at the data, perform the correct action, and then pass the transformed data to the next process in the workflow. From there, another robot wakes up, assesses the data, takes the correct action, and continues the process.
Software robots used in these types of functions can be either simple (e.g. file transfer) or more complex (OCR of images and data validation). The days of manual ledger entries, double entry books, shoe-boxes and color-coded folders are largely behind us.
Artificial intelligence is just beginning to affect various aspects of business processes, including credit and accounts receivable (AR) functions. Learning programs can be used to adapt and learn from the unique operating environment of a business and take appropriate actions as these conditions change.
In our cash application process, we use intelligent programs to automatically correlate cash payment records with unstructured remittance notifications that are sent by the consumers. Our software can also analyze and detect tabular line item data from remittances for correlation with invoice tables generated by a customer’s ERP systems. The input data for these programs is unstructured and can arrive in a variety of formats, such as images, PDF, HTML, email, Excel, Word, comma-delimited, plain text, and more, and via multiple channels including email, EDI, or FTP. Software needs to be smart enough to as to learn the layouts of every single document, interpret the data, and figure out how they relate to consumers or their banks.
Want to learn more?
Read our entire interview series explaining the role of automation in accounts receivable (AR) processes:
Post 1 – The Data Scientist Explains: Automation and Robotics
And don’t forget to check back next week for another fascinating discussion with the Billtrust data scientist.
Farhad Khalafi, Research and Development Scientist at Billtrust. Farhad is responsible for incorporating the state-of-the-art technology in machine learning and robotics into Billtrust’s next generation software in Credit and AR automation, and has over 20 years of experience in software development. He also holds a Ph.D. in Quantum Physics from the University of Cambridge, UK.
Do you have a question for Farhad? Ask him on Twitter @FarhadK