Published: November 26, 2019
More than two-thirds of credit and collections professionals report that overdue Accounts Receivable (A/R) averages are greater than 10% of their portfolio, according to the FIS 2019 Credit and Collections Market Report: Modernising Credit-to-Cash with Artificial Intelligence that surveyed more than 100 credit and collections professionals. Of the respondents 78% report that over the past 24 months, their days sales outstanding (DSO) has remained flat or increased. Doing business using the same processes and systems is not getting the results that companies are seeking.
The good news is that companies are exploring new technology opportunities such as specialised technology combined with artificial intelligence (AI) and process automation to overcome these challenges. Just over half, 58%, of companies have either already adopted it or are looking to implement some form of AI in the next 18 to 24 months (fig. 1). This article uncovers more of the challenges companies are facing and examines how they are modernising the credit-to-cash process with specialised solutions combined with AI and process automation.
Fig 1: how likely are you to implement ai in your credit-to-cash operations?  |
The increasing collection volumes is the number one challenge with which credit and collections departments are struggling to keep up. According to the report, 31% indicate collections volume has gone up with same or reduced staffing (fig. 2).
Every company is being forced to do more with less and many are also still using enterprise resource planning (ERP) systems or manual processes to manage their credit and collections. Rather than struggle to squeeze more out of their limited staffing and technological resources, companies are making the move towards modernisation. While the adoption has been slow, 27% of organisations now use specialised systems to automate their credit-to-cash operations (fig. 3). These specialised solutions create capacity within their teams and enable them to address the secondary and tertiary challenges that plague businesses.
Fig 2: Looking back over the past 24 months, which has been your top challenge? |
Fig 3: What systems do you use to automate your credit-to-cash operations?  |
Another challenge facing credit and collections departments is the assimilation of new finance systems and/or acquisitions, according to 12% of respondents. Given that credit-and-collection functions have major cash flow implications for every organisation, the expectation to continue to improve the collection rates of outstanding A/R puts a major burden on businesses to look for solutions that provide strategic interoperability. This includes the ability to consolidate disparate ERP systems into a single view of risk and cash. Especially in a good economy, companies acquire other businesses that bring the complication of assimilating the legacy system and data into current processes and systems.
Even the best of teams that have solid policies and processes in place struggle to bring in new systems and effectively spread the additional workload across their team. This is driving companies to work with specialised solutions that can provide knowledgeable professional services to quickly integrate any new system. This enables teams to continue to work within their standard policies and practices while providing the flexibility to load balance their workforce to be as effective as possible.
Workload balance is extremely important to the effectiveness of credit-and-collection organisations. A small but growing trend among them is to not assign accounts to specific collectors. Rather, teams are beginning to permit their strategies to drive the daily assignments across their organisation. Manual processes and ERP systems do not allow for this functionality. It becomes too cumbersome and time-consuming. However, with a specialised solution that provides the flexibility to assign accounts to an individual and/or team, organisations can easily move accounts to ensure 100% utilisation of their workforce.
Teams employing manual processes typically must wait until the end of the month to see what results were achieved. Based on those results, changes may be made and then the managers play the waiting game to see if those changes had the desired impact. Leveraging automated dashboards and performance metrics within a specialised solution, managers can easily identify potential shortfalls, adjust quickly, and see the progress of any alterations immediately.
The use of AI is growing among credit and collection departments. Of those organisations that have either already adopted or are looking to implement AI, 40% have leveraged it with collections prioritisation, 35% in cash application and 25% in credit assessments and reviews.
Within collections prioritisation, AI is only as good as the underlying strategies. A risk-based approach to collections is essential for effectiveness. Leveraging the AI engine to calculate the risk of each customer and then appropriately assigning strategies based on that level of risk provides the most optimal results. After assigning the strategies, the AI engine then prioritises the accounts to ensure the right customer is being contacted at the right time, via the right method to prevent delinquency. Being used in combination with process automation, AI reduces manual work, provides improved visibility to risk, and improves decision-making. The operational improvements alone can help companies achieve a positive return on investment after implementation.
For those companies which have not embraced AI for collections prioritisation, a surprising number are still using age (38%) and value (36%) as the main driver for determining who they should contact first (fig. 4). To illustrate the point even further, when asked what percentage of their portfolio is typically past due each month, 46% of respondents indicated that their overdue A/R is greater than 10% (fig. 5). Allowing artificial intelligence guided by a collections risk score, provides a platform for continuous improvement. Teams no longer waste time on the administrative tasks. They simply spend more time actually collecting and preventing delinquency.
Fig 4: Rank in order the drivers you use to prioritise collections  |
Fig 5: On average, what percentage of your receivables portfolio is typically due each month?  |
A more recent introduction of AI is in the cash application arena. More specifically, machine learning is improving first-pass hit rates by monitoring user action. With traditional machine learning models, the ramp-up period in which the AI engine reviews examples to become more confident in taking action can take weeks, months, if not more. However, with accelerated machine learning that monitors user action when clearing an exception, the AI engine learns after the first example how to apply the next payment that arrives from that customer.
The advantages of using AI in the credit process is being adopted at a slower rate, however, it also has a significant impact on performance and efficiency. In combination with process automation, AI can save companies time by automatically pulling internal payment history and external bureau data and calculating a credit-risk score. Depending on each company’s risk tolerance, a credit line can automatically be assigned and routed for approval if necessary.
Not so long ago, AI seemed superfluous to a credit and collections organisation. It seemed too advanced to warrant installing in a cost centre environment that does not generate revenue for the company. However, businesses have realised the value of investing in their credit-to-cash process towards improving cash flow. With volumes increasing and budgets remaining flat or even shrinking, the introduction of AI and process automation are becoming a prerequisite for success. With 81% of credit-and-collection professionals understanding how AI can help improve their processes, the race is on to becoming a best-in-class organisation.
Keith Cowart
Senior Product Manager
Keith Cowart is a Senior Product Marketing Manager in FIS’ Corporate Liquidity - Receivables group which features the award-winning Credit-to-Cash product, GETPAID and Integrated Receivables. He has over 20 years of professional experience in accounting and finance leadership roles including Accounts Payable and G/L Accounting, as well as Credit and Collections in large global companies with shared service centres. Keith’s focus has always been on continuous improvement and leveraging technology to automate processes to achieve extraordinary results. He holds a Bachelor of Business Administration degree from Piedmont College and a Master of Business Administration degree in Management from Georgia State University.
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