From Legacy to Revolutionary – Managing Credit and Collections in a Modern World

Credit and collections, or customer to cash management, is an integral part of every company’s drive to improve cash flow. It has only been relatively recently that corporations have truly invested time and resources into improving this main component of a flourishing organisation, however. The most successful companies are leading the way in modernising their practices utilising the latest technology and cloud deployment methods. Those that are simply maintaining old practices are needlessly allowing cash to flow out of their business and putting their corporations at risk.

In a recent study, FIS surveyed 131 credit and collection professionals from corporations around the globe to understand how they are modernising their processes and procedures within the broader credit-to-cash landscape and the role technology plays therein.

Doing more with less

Based on the survey results, many corporations are still struggling with having to do more with fewer resources. When asked about the most challenging aspect of managing a credit and collections team over the last 24 months, a significant 78% pointed to collections’ volume increasing while staffing levels remained flat or even reduced. These conditions are leading organisations to seek automated solutions for relief. Resource restrictions hit across all areas of the credit-to-cash cycle, including credit, collections, disputes and deductions, as well as cash application. Solutions that provide process automation decrease resource requirements while increasing cash flow and reducing days sales outstanding (DSO). These are key metrics used by executives to measure the health of their organisation and benchmark themselves against best-in-class companies (fig. 1).  

Portfolio size (fig. 2) plays into the effectiveness and efficiency of teams. As resources decrease, automation clearly has a role in handling the size of portfolios for collectors. Otherwise, simple maths dictates that collectors will become overwhelmed with the increased volume in their portfolio and will not be able to effectively manage the workload. Our latest results show the average portfolio size to be roughly US$10m to US$50m, with on average 51 to 1000 invoices, and greater than 750 accounts (fig. 2). As these average volumes grow, results will suffer without the introduction of automation. 

Portfolio risk evaluation

Another top challenge for corporations is the lack of visibility into the risk of their entire portfolio (77% find it at least somewhat challenging). More specifically, the inability for organisations to routinely review and score their entire portfolio (fig. 3). Of those surveyed, 21% stated that they are never able to score their entire portfolio. Only 39% indicated that they are able to score their entire portfolio at least on a quarterly basis.  

When asked why it is so difficult to score the entire portfolio more frequently (fig. 4), companies indicated that it was a ‘lack of employee expertise’ (24%), process decision to use order holds as a trigger for account reviews’ (20%), as well as ‘too many accounts to score’ (20%). The latter pointing back to the lack of resources to sufficiently protect the business from risk. Top performing companies have addressed these challenges by leveraging automation and artificial intelligence (AI). The combination of an AI engine that learns and predicts customer patterns and process automation, which manages tasks without the requirement of human touches, creates a streamlined environment that accurately identifies future risk while putting necessary mitigations in place.  

Prioritising collections

Despite the proven success of prioritising collections using an AI engine with a predictive risk score as the basis, many companies are still using value (36%) or age (35%) as their primary driver. It is a difficult habit to break. In today’s world, short-term goals seem to rule the day. Using value and/or age as a company’s prioritisation core creates a cyclical pattern of results. Teams do exactly what they are incentivised to do. Most organisations use percent current as the main monthly target for collectors.  This causes collectors to focus on the largest dollar invoices to prevent them from rolling overdue. They do this at the expense of smaller dollar invoices that may never get any attention. Over time, these small dollar invoices add up.  

When predictive risk scoring is leveraged, the collector no longer chases accounts that are going to pay anyway. They are focused on the riskier accounts. As weeks and months pass, this smooths out the results, making them more consistent. It also creates a positive, continuously improving trend for results as the collectors are focused on preventing the risky accounts from becoming delinquent.  

Automating disputes and deductions

Companies have begun to embrace the best practice of segregating disputes and deductions from the non-disputed portion of the invoice (70%). This is a great sign that organisations recognise the benefit of eliminating unnecessary work for their collectors. Now that this is becoming commonplace, we need to look at the next evolutionary step. Automating the routing and escalation of disputes and deductions cuts out the time wasted by collectors determining the appropriate owner and manually routing disputes. Of those surveyed, 18% of companies are already taking advantage of an automated process that enables flexible routing based on organisational needs, such as business unit, type of dispute, amount. With the additional time saved, companies can shift to begin focusing on root cause analysis (RCA) and prevention of disputes and deductions.

Increasing first-pass hit rates in cash application

Our survey shows 71% of cash application teams are living with the unjust burden of manually applying payments within their enterprise resource planning (ERP) system (fig. 5). These systems are traditionally ineffective at handling multiple payment methods and are extremely inflexible with automatic matching options. Teams rely on their internal IT organisations to create matching policies. This generally requires a ticket that falls somewhere near the ‘never going to get to it’ level of prioritisation among the various other needs of an ERP system. Of those questioned, 77% of these companies report a first-pass hit rate equal to or less than 80% (fig. 6). Modern companies are beginning to embrace specialised solutions that have AI embedded into the cash application function. Leveraging machine learning, the AI engine assimilates the most successful and accurate matching methods by monitoring exception processing. Combining multiple data sources such as open invoices, remittance details, and collection data, the AI engine is able to automate the cash application process and continue to improve its hit rate over time. 

When asked what the top challenges were for corporations to improve their cash application hit rates, 89% stated the lack of remittance details from a customer was at least somewhat challenging (fig. 7). Those organisations that have embraced AI have been able to overcome the challenges related to remittance delivery timing, or lack of remittance. The AI engine reviews various combinations of open invoices, again utilising collection data in the decision-making process to determine the appropriate set of matching invoices. These teams no longer have to manually research and wait for responses to enquiries from the customer. The process can automatically match the payments to the invoices, which in turn releases the team to focus on other value-added work.  

Simplification through cloud deployment

The benefits of cloud-based systems are well known, including cost savings for hardware and IT infrastructure as well as security management and maintenance. Those companies leading the way with modernising their organisations (24%) have already switched, or are likely to do so, within the next 12 months (fig. 8). Cloud deployments reduce the cost of maintaining a self-hosted solution. This includes obvious overheads such as servers and database and systems administrators as well as the not-so-obvious costs such as maintaining security protocols, maintaining and upgrading hardware and server capacity, and power and cooling requirements for the servers. By leveraging a hosted environment, corporations can focus their teams on collections rather than IT infrastructure.

Fig 8 - Migrate to a Cloud-Based System Within 12 Months

The results of this year’s market study show that the modernisation of credit and collections management involves embracing technology in areas never thought possible. The most advanced companies are taking advantage of machine learning and AI combined with process automation to offset the lack of resources to handle the increasing volumes.  

Traditional or legacy processes are giving way to revolutionary ideas, completely changing the landscape and approach to increasing cash flow while mitigating risk.    

* All charts and statistics contained within are from: FIS Market Study: Modernisation of Credit and Collections. Figures have been rounded and therefore may not equal 100%.