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Why Implementing AI and ML is a Must for Credit Managers


Despite the widespread adoption of artificial intelligence (AI), robotic process automation (RPA) and machine learning (ML) within the finance sector, one area yet to take advantage of these technologies is credit management. While these technologies have been shown to offer a range of benefits including boosting productivity and allowing for valuable insights to be gained from data, there are several reasons why many credit managers have yet to embrace them.

Firstly, it can be difficult to quantify in monetary terms investment in new technologies such as AI and ML, for instance, what is the return on investment (ROI)? Additionally, finance teams may have been reluctant to invest due to the recent uncertainty surrounding Brexit or an apathy towards change. Secondly, implementing technology can sometimes incorporate a number of required solutions within a business, and unfortunately, the credit department is rarely at the top of the order of priorities. Another reason why businesses may be reluctant to implement these new technologies is cost. Many businesses operating in difficult trading conditions either don't have the budget or it is simply allocated elsewhere within the organisation. While this factor is something that businesses will find difficult to overcome, for those in the first two camps, failing to adopt new technologies means they are missing out on significant benefits.


AI and RPA can be used to streamline and enhance credit management processes by enabling finance teams to automate many repetitive, often tedious tasks, such as invoicing. This would see the hundreds of invoices usually dealt with manually by credit management teams automatically inputted and processed within the system. This would save hours of time usually spent by individuals on the task. AI can also be introduced to automate the process of segmenting customers into groups based on established rules. By segmenting customers in this way, finance teams can determine what form of communication certain groups of customers are most likely to respond to, for instance. This will result in more successful customer interactions with the aim of ensuring they make payments on time. This approach will also help to enhance customer relations and the customer experience as the individual's preferences are taken into account.

Risk assessment

Another area where AI is invaluable is in assessing a customer's credit worthiness. Previously, this assessment involved rules that were black and white, with credit managers assessing any grey areas. However, AI can now be introduced to make new connections to assess these grey areas – making it easier for informed decisions to be made on credit risks. With AI and RPA proven to have greater accuracy than people, its use could lead to increased quality and lower costs. Thanks to this accuracy and ability to carry out automated tasks, financial professionals will have more time to spend on bigger accounts or more impactful tasks. In fact, 72% of business decision-makers think AI enables humans to concentrate on meaningful work, which for finance teams means being able to focus on making a difference to their organisation and customers.

Gaining insights from data

AI and ML technologies will also enable finance teams to make better use of the customer data that is being collected by the business and combine this with external data sources. Research has found that 61% of business professionals think machine learning and AI are their organisation's most significant data initiative. Using the technology in this way would enable them to perform reliable predictions based on the past. For example, AI is capable of analysing data in software solutions to determine if there are any patterns. This will help the finance team to predict events, such as which customers will fall into payment arrears. They can then take the necessary actions immediately and decide whether to approve credit. This is likely to increase cash flow as finance teams have an increased awareness as to which customers should or shouldn't have their credit approved. Predictions made by AI can also be applied to other processes, such as the invoicing method, because the technology can predict which payment method will result in the invoice being paid soonest.

Ultimately, while implementing AI and ML requires an initial investment, credit management teams have a substantial amount to gain by doing so. By adopting these technologies, finance teams will benefit from reduced inefficiencies and the ability to spend more time on value-adding tasks, which, in turn, will boost morale among employees and improve customer service.