AI in account receivables: a source of competitive advantage?
While accounts receivable (AR) is typically the largest asset on most businesses financial statements, opportunities exist to manage it more effectively and generate higher profits.
While accounts receivable (AR) is typically the largest asset on most businesses financial statements, opportunities exist to manage it more effectively and generate higher profits.
While accounts receivable (AR) is typically the largest asset on most businesses financial statements, opportunities exist to manage it more effectively and generate higher profits.
AR processes were initially designed to keep the bill collection process as organised as possible, as B2B supply chains and payment terms have become more sophisticated, it has become increasingly difficult to manage AR effectively.
The consequences of ‘getting it wrong’ are growing – poor management of invoice to cash collection processes leads to overdue invoices piling up, which, in turn, leads to cash flow problems. As control over operating cash reduces, businesses need to rely more and more on expensive bank credit.
When credit is available this challenge can be managed but today the access to bank credit cannot be taken for granted. And let’s not forget that the time taken to turn AR into cash (days sales outstanding or DSO) is directly linked to the financial health of the business.
Organisations can reduce their DSO and optimize their receivables process by executing a number of strategies:
1. Mandating payment terms across their supply chain. This strategy can only be attempted where there is a significant imbalance in power between the company and its suppliers, for example if a large retailer has a significant market share it can dictate payment terms to its suppliers.
2. Turning the AR function into a core competency. This involves tuning the AR function using a combination of top professionals backed by exceptional processes and procedures.
3. Leveraging the latest technologies to ensure that all staff are supported with the combined expertise of the entire company.
While some companies may be in a position to execute strategy 1 above, most companies are part of other companies’ supply chains and hence cannot execute it. The ongoing war for talent means that relying on strategy two may not be a recipe for long-term success.
Technology can help
Clearly the first step to reducing DSO involves sending out invoices on time. Then making it easy to pay by providing many payment options, including cash, cheque and electronic.
While much focus is placed on digital, the 2016 Electronic Payments Survey from AFP revealed that 44% of businesses still receive payments through cheque.
Not only do manual driven payment methods create time delays but they also increase costs and raises security concerns. In a recent survey, 71% of corporate respondents mentioned that they have experienced actual or attempted cheque fraud.
Using an ERP or an E-Invoicing solution could help to streamline transaction processing and help manage DSOs better but it falls short of the organisation’s overall objective of increasing straight-through processing and improving productivity. While these solutions do help automate a large part of the order to cash or procure to pay cycle, they don’t approach working capital management as a strategic discipline.
To ensure overall working capital optimisation, corporate treasury teams need to automate their receivables process with artificial intelligence-driven solutions.
This would include collecting timely dues from buyers, ensuring strong logistics management and building correspondent bank relationships for timely processing of receivables.
The solution used to automate the account receivables processes would ensure efficiency and productivity gains not only in terms of realizing collections and reducing DSOs but also in enhancing supplier and dealer relationships.
Many of these benefits are typically attributed to non-AI powered solutions, so how can artificial intelligence help? For starters, it can enable higher collections by identifying when a corporate client needs to be prodded about an overdue invoice, highlight the most effective way they should be contacted — by phone, email or visit — and the payment method preferable to the client through analysis of their behaviour data. AI-powered solutions can also process
AI-powered solutions can also process large volumes of invoices quickly, thereby improving the swiftness of processing. Robotic process automation can help the organisation automate the cash application process where remittance data is gathered through disparate sources, matching it with the invoice data and reconciling the resulting electronic payment with the corresponding customer account.
If customers submit payment which either combine multiple invoices or don’t match any invoices in the accounting system, AI solutions can analyze the possible invoices and can match the paid amount to the right combination of invoices.
Not only can these solutions do a simple match via an invoice number but they also enable a more complex pattern based matching which involves studying payment patterns (via trend analysis) and constructing reconciliation rules like LIFO, FIFO, normal match, best match etc.
Moreover, AI solutions can help extract key data including historical data (open, closed, disputed invoices; collector logs and customer loyalty) and external data (customer credit ratings and interest rates) and give key insights on all possible credit and AR prediction scenarios including use-cases such as invoice payment dates, validation of deductions and prediction of potential customer default.
This helps in a number of ways including:
As the landscape is changing, agile and innovative banks are tapping into this new opportunity by offering the solutions corporates seek to optimize their working capital. For example, Bank of America has introduced an “Intelligent Receivables” solution designed to streamline the receivables process of its corporate customers. Using this solution, payers are automatically identified, as is remittance data that might have arrived
For example, Bank of America has introduced an “Intelligent Receivables” solution designed to streamline the receivables process of its corporate customers. Using this solution, payers are automatically identified, as is remittance data that might have arrived separate to an invoice. Clients can also set up automatic emails to payers, in order to further streamline the process.
Fintechs are following suit to provide more financially viable solutions to the small and medium-sized enterprises. Startup YayPay uses machine learning to predict the potential day of invoice payment by looking at the customers’ payment habits and behaviours.
Accounting software firm Xero also deploys a machine learning automation system which is able to learn how to categorize invoices over time. New Zealand-based Debtor Daddy uses artificial intelligence to manage overdue invoices sending reminders to the customers when their payments are overdue and following up automatically.
The bottom line
The benefits of reducing DSO are well known, for example an average organization can reduce its trapped cash by nearly 33% simply by reducing DSO by 5 days. However the challenge lies in designing, developing and executing an AR strategy that can deliver benefits today and tomorrow.
The rapid advances in AI can now be used to augment human operations expertise to bring the combined intellectual property of a company to bear on each situation. This combination of intelligent automation will deliver short-term benefits while turning the AR function into a source of competitive advantage which is crucial for corporate treasurers who wish to be game changers in the field.