Business Collections and machine learning
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How to increase business collections with Artificial Intelligence?

The analogous problem of business collections management in the private sector, such as banks and credit card companies, is  increasingly gaining attention. Machine Learning, being the hottest trend today, is incredibly powerful for predictions or calculated suggestions based on large amounts of data . As businesses from many industries   have begun to rely more heavily on machines to do the heavy lifting of A/R processes, executives are able to deliver more value to their customers by reflecting on their roles and identifying the opportunities machine learning could offer them.

Traditionally, business executives across the financial spectrum believed that Machine Learning and Artificial Intelligence were just millennial buzz words. This assumption could not be more
flawed. Today, Machine Learning and Artificial Intelligence are the top trending elite technologies available to ensure that collections activities are more effective, starting at root
operational levels so that collections efforts are optimized for maximum productivity with minimum cost.

What is a typical business collections Process?

A typical collections process is largely reactive and relies heavily on due dates as the pivot for all dunning activity. It starts only when an invoice is due or shifts to a larger aging bucket. The
majority of collections operations, including account prioritization, correspondence strategies,and customer collaboration, are based on static parameters such as aging bucket and invoice value. This results in a cluttered collections worklist, inefficient identification of delinquent accounts, and wasted collections efforts.

Due to the absence of a scalable collections process,which takes dynamic parameters into account, the collections team ends up chasing only past due A/R while the overall team productivity is lost in labor-intensive, time-consuming, low-value tasks such as ERP data extraction, manual worklist creation and correspondence with non-critical customers. The key fallouts include a slower cash conversion cycle, increasing DSO,inefficient processes and higher operational costs.The dynamic shift from a reactive to a proactive collections process is one of the beginning perks of an AI-powered collections management process. With machine learning under the hood, the collections team could leverage high-impact predictions to enhance collections output and key KPIs such as DSO (Days Sales Outstanding) and CEI (Collection Effectiveness Index).

So, what,s the future like?

Predicting payment date and delay could be the foundation of a modern-day collections process,which takes the dynamic changes in customer behavior into account when formulating dunning rules and strategies. Further, customer collaboration could be tailored and personalized by analyzing customer preferences in terms of time, day of the week, and mode preferred for communication and with insights on identifying which dunning letters work best for each customer.

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