In recent years AI (Artificial Intelligence) and Machine Learning have transformed countless digital processes, making business more efficient and having a direct impact on profit margins. But arguably their greatest impact can be felt in the world of credit control. Here we offer up a guide of how to harness the immense power of AI and Machine Learning to enact effective credit control processes.
What Exactly is Artificial Intelligence and Machine Learning?
Many people still remain confused about what Artificial Intelligence and Machine Learning really are. You don’t need to understand how precisely they work, but rather what they do. Machine learning (ML) is an application of AI. It works to provide systems with the ability to automatically learn and improve from experience, instead of being explicit programmed by a human. It’s the idea that a computer program can access data, use that data and ‘learn’ things itself. Ultimately, the aim of ML is to allow software to learn automatically, removing the need for human intervention or assistance, which for business is both time-consuming and costly. Artificial Intelligence (AI) is more broader in scope. But understood simply, it’s the science and engineering processes that make computers behave in ways that we previously believed required human intelligence. Both ML and AI have a considerable impact to make on the process of credit control.
Machine Learning and Credit Control
Credit control software is a tool which applies Machine Learning processes. It can identify credit risks more promptly and accurately, and it can improve cash flow. The machine learning models, which are automated within the credit control software, empower businesses to analyse vast amounts of data. These smart tools enhance a company’s decision-making capabilities, and they mean users can more accurately predict defaults, for example. The reality is that machine learning models can yield much better insights than a human analyst could ever present.
Interestingly, studies suggest that 50% of all debt collection correspondence is directed at customers who would have paid even without correspondence reminding them to pay. ML improves the debt collection process because it can automatically identify customers that don’t require texts, emails, letters etc. Instead it can priorities accounts that will benefit from chasing. This saves times and money.
A ‘Single View of Debt’ (‘Single Citizen View’ or ‘Single View of Customer’) has been another recent advancement in this regard. It is a means to manage and present all data relating to an individual customer, business or citizen in one single record and location, making it easier to read and easier to access. A single view of this debt leads to more accurate reporting, forecasting, communication and collection.
Artificial Intelligence and Credit Control
Artificial intelligence debt collection software can be used to create human-like ‘voices’, bringing a more personal touch to debt collection and credit control correspondence via the call centre. As consumer preferences shift more towards digital channels, AI tools can help to optimize omnichannel communications too, such as emails, texts, and voice calls — which can be used to extend the outreach of the collections process. AI can also provide credit control managers with information relating to the optimum time of day for any digital communications to be sent, and also the best channel by which to reach the debtor.
Today AI and Machine Learning enables companies to use advanced analytics to their advantage, driving smoothly automated credit control strategies. Try a demo of Lateral today, and experience our industry-leading credit control software for yourself.