Credit management has undergone a significant transformation in recent years as a result of changing economies and traditional business practices. Initially, it was just a back-office function, far from the core business with limited strategy involvement. The efforts were narrowly concentrated on the reduction of DSO and risk mitigation. Typically, the role of credit managers was quite finite, limited to evaluating customer creditworthiness. In the recent business era, companies have taken up credit management as an integral part of their business – the Order to Cash cycle starts with placing an order, setting up credit terms, order fulfillment, and all the way up to collecting payments.
In the last 10 years, there has been a tremendous shift from the traditional credit management approach to one that is more strategic. It has evolved into a data-driven operation by implementing Artificial Intelligence (AI), Robotic Processes (RP), Advanced Scoring Analytics (ASA), and automated workflows that eliminate most of the manual tasks and duties previously performed.
Today, credit managers are playing an influential role in the profit-loss segment of a business. With traditional credit management practices getting obsolete, the role of credit managers is getting more crucial and prominent. Gone are the days when credit managers were only responsible for collecting customer documents.
One critical element in credit management is onboarding new customers, so as to help sales grow profitably. Earlier models for managing credit were designed to fit the organizations and industries irrespective of their geopolitical factors. Now with the introduction of different models designed for different businesses, we possess the ability to respond to macro-economic variables, such as inflation.
A considerable amount of time and effort was invested in the whole credit management process previously. Automation brought along a seamless process which not only decreased the amount of manual labor required but also allowed organizations to reallocate resources to other high-value tasks.
Credit management has moved leaps and bounds after the implementation of technologies such as Robotic Process Automation(RPA) and Machine Learning(ML).
Modern technologies such as RPA and ML are becoming household names. The next step in this meteoric rise of credit management is the adoption of automated solutions into businesses. These solutions have to be modified and expanded in such a way that they can reach multiple functions such as collections and cash application, enabling them to create a one-stop-shop for order to cash.
Automating credit management can seem daunting and tedious at first but continued usage of redundant processes is slowly chipping away an organization’s efficiency. Credit management as we know is evolving at a great pace.
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