AR - Accelerate Receivables using Machine Learning technology
5 Reasons why technology can accelerate your receivables process
ERP systems of today are becoming more function rich and are seen to be robust around core financials, however ask most Credit professionals and the majority would say that the AR functionality is still some way behind, making their job a challenge to try and identify a clear collection strategy using their ERP. Most Credit Teams have to rely on excel spreadsheets to manage their portfolio or rely on their experience and knowledge of knowing their client behaviour
Most edge technologies move this slightly further forward by automating the spreadsheet and providing segmentation, workflow, event management etc. However, these have not been widely adopted as seen as expensive, difficulty to justify ROI and still don’t really fulfil the need of a the Credit Professional.
All that is now changing with the introduction of Machine Learning technology that can learn behaviours and predict the future, as well as provide a real time insight into the sales ledger. This allows dynamic strategies to be applied, and makes the collection team more efficient by allowing more cash to be collected and spending more time having the right conversations with the right information to hand.
1) Predicting the Future
Like driving a car, most collection systems are based on what is in the rear view mirror and use data that is potentially not up to date or not accurate. From this, collections teams try to pull a customer view to allow a list of calls or actions to be taken. AR software that learns what clients’ payment behaviour is, and can predict on day one what the expected revenue would be like right up to month end, with real time information, can allow the system to automatically assign the right collection activity at the right time every time. This means collection teams are always talking to the right customers, all the time, every time.
2) Dynamic Strategies
Whilst machine learning goes a long way to improve efficiency and reduce intensive manual work arounds, even machines can’t always predict every action from every customer. So having the ability to change a strategy in real time, or assign a client set to a collections agent when someone unexpectedly calls in ill, or when holiday time arrives is a must. Most existing solutions require a change request which can take 7-14 days to implement, with AI solutions this change can be seen instantly and reacted to instantly, meaning you are in control all of the time.
3) Prescriptive or Predictive
Understanding customer behaviour and what makes them pay is crucial to an efficient collections team. Machine Learning software can do this by working out for example which customers always pay on a certain date and therefore if the invoice is submitted accurately and on time then the customer AP system will always send a payment on a certain timed schedule. The software will learn this and predict when the next payment will arrive, therefore no action needs to be taken until the solution highlights that the target date has been missed. If a client doesn’t use an AP solution or is not on a predictive payment schedule, the solution will record and learn what prompts a payment. Therefore bringing more efficiency to process and allowing credit staff to focus on the right things, at the right time.
4) Real Time information all the time
Most ERP systems or bolt on collection systems still don’t have a real time view of all activity in the sales ledger which can hamper collection activities and can sometime lead to embarrassing phone calls.
For example, activity that is driven from an ERP or a legacy collections system is usually driven from the cash posted to the ledger, therefore not seeing cash that may have arrived into the business in the last 24 hours or even longer and certainly not seeing a remittance that has arrived but where the payment may not arrive for another couple of days. Machine Learning solutions see all the activities and all the data that is needed to support the collection team, so if a remittance arrives today this can be flagged and taken out of a collectors call queue dynamically saving them their time and the embarrassing phone call. If a cheque arrives, it will be scanned in and will appear immediately to alert a collector. Similarly, if an electronic payment arrives it will automatically be allocated to the account and invoices, giving a real time view.
By using Machine Learning solutions organisations can make better decisions, improve collections, and operate much more efficiently. Take this even further by providing a real insight into a sales ledger to see information previously unavailable. What if you could, in an instant, see what a customer attractiveness rating was, that is, seeing the profit margin or the profit erosion of clients. Using external risk scores to the machine may provide the opportunity to review credit risks, increase limits and offset risks. Look at customers who are constantly late or constantly cost more to manage than the revenue they generate. Simply create a strategy to move them to a more predictive payment method or even decide not to trade with them! All this and more at the touch of a button and the right intelligence.
Rimilia’s credit management solution, Alloc8 Collect, brings a new way of working to credit management challenges. Book a demo today to see Alloc8 Collect in action.