The adoption of artificial intelligence (AI), robotic process automation (RPA) and machine learning (ML) is increasing across the finance sector. This is being driven not only by a growing number of use cases but also the significant benefits these technologies offer, for instance, PwC has found that 54% of executives say AI tools have boosted productivity. Yet, despite AI and ML being widely used across many areas of the finance sector and the great potential these technologies offer, their adoption within credit management has been much slower, but why?
Firstly, adopting AI and ML is an investment that at times can be difficult to quantify in monetary terms. For instance, what is the ROI? In addition to this, finance teams may be holding back on investment due to the uncertainty surrounding Brexit or apathy towards change. Secondly, implementing technology can sometimes incorporate a number of required solutions within a business, and unfortunately, the credit department is often some way down the order of priorities. The final reason businesses may be reluctant to implement these new technologies is cost. Many companies operating in difficult trading conditions either don’t have the budget or it is simply allocated elsewhere within the organisation, rather than assigned to the finance department. While the final reason may be more difficult to overcome, for those in the first two camps, by failing to adopt new technologies, these organisations are not only losing out on the benefits of automation but are also failing to capitalise on the extensive data at their disposal. So, why can’t finance teams afford to ignore AI and ML?
How can credit managers benefit from AI and ML?
AI could be used by finance departments to streamline and enhance their credit management processes. AI and RPA technologies allow 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 will save hours of time usually spent by individuals on the task. Similarly, there is potential to automate the compilation of reports. With finance professionals no longer required to carry out these repetitive and time-consuming jobs, it’s likely to also improve morale and allow these individuals to focus on adding value to the organisation.
Credit management teams can also implement AI 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. Additionally, this approach will help to improve customer relations and enhance the customer experience as each individual’s preferences are taken into account.
The use of AI and ML technologies will also allow 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 allow 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 allow 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 of 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, as AI can predict which payment method will result in the invoice being paid quickest, and transferring customers to collection agencies.
Additionally, AI can improve the assessment of a customer’s credit worthiness. Previously, this assessment involved rules that were very black and white, with credit managers assessing any grey areas. However, AI can now be used 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 free time to spend on bigger accounts or more impactful tasks. In fact, 72% of business decision-makers think that 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.
The emergence of new technologies that use natural language processing (NLP) – a branch of AI that helps computers understand, interpret and manipulate human language – could also be hugely beneficial to credit managers. In this environment, NLP can be used to identify words within reports that could indicate the future of an account. If this technology is adopted by finance teams, it will enable them to get a greater insight into customers and any risks they pose much faster and more easily than is currently possible. It will mean that individuals aren’t required to trawl through reports and allow them to make decisions about whether to approve credit, for example, quicker.
While some credit managers and finance teams may be reluctant to adopt AI and ML due to the initial expenditure required, failing to embrace these technologies could be a costly mistake, leading to inefficiencies and poor employee morale. By using these technologies, organisations and credit management teams will not only benefit from reducing the amount of time spent on repetitive and time-consuming tasks but also from more effective use of data and a happier workforce that can focus on adding value.
Marieke Saeij, Chief Executive Officer, Onguard
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