The use of and use cases for artificial intelligence (AI), robotic process automation (RPA) and machine learning (ML) are continually growing. Consequently, its adoption across the finance sector is increasing, particularly as those businesses that implement these technologies stand to see significant benefits. For instance, according to research by PwC, 54% of executives say AI tools have boosted productivity, while its use can also help organisations get more value from their data. However, despite the preponderant use of these technologies in most other areas of the financial sector, AI and ML are yet to take hold within credit management, but why not when they have great potential?
There are a number of reasons that new technologies such as AI and ML haven’t been implemented within credit management.
Firstly, it’s 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 that latter point 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?
The benefits of AI and ML for credit managers
AI is already being used in the financial domain today, primarily for stock trading, predicting fraudulent transactions and determining risks. However, its use cases aren’t limited to these areas. 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.
Further to this, AI technologies can allow finance departments to 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 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 free time to spend on bigger accounts or more impactful tasks. In fact, 72% of business decision-makers believing that AI enables humans to concentrate on meaningful work.
For finance teams, this means they would be able to focus more closely on making a difference to their organisation and customers, rather than on the smaller necessary but time-consuming tasks.
Making better use of data
By implementing AI and ML technologies, finance teams will also be able 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.
As AI and ML become more advanced, we are seeing the emergence of more technologies that use natural language processing (NLP). NLP is a branch of AI that helps computers understand, interpret and manipulate human language. In the world of finance and credit management, NLP is beginning to 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.
Ultimately, while implementing new technology requires an initial expenditure, failing to automate manual tasks with the use of AI and ML will lead to inefficiencies which can be costly to a business and lead to poor morale. Organisations and credit management teams that are willing to embrace new technologies are also likely to reap the rewards of more streamlined operations, more effective use of data and a happier workforce that can focus on adding value.
Marieke Saeij is CEO of Onguard