Risk Based Collections: Using Credit Information in the Collections Process
Walker, Robin
Abstract
ERP software is now predominant in most medium and large businesses. Until relatively recently, this type of software has predominantly existed to manage core processes where efficiencies can be gained by standardization, and decisions can easily be made based on data existing in the system. Due to the diversity of processes required to manage a business, and the need to make rapid decisions in all areas of the enterprise, software has become available to cater to many of the processes unavailable in the ERP Systems.
However, with the maturity of core processes within ERP applications, vendors have started to focus on more complex processes, especially with the advent of better technology, both in hardware and software. Credit and collections processes, traditionally handled manually, or with the use of products bolted on to ERP applications are now being incorporated in, with the result of attaining best practices including risk based collections.
Looking Back
Enterprise Resource Planning systems grew out of Manufacturing Resource Planning (or more precisely MRPII), where MRP had been extended to managing resources. The term ERP was first used by the Gartner group in 1988, and defined as a system built on the foundation that all business processes should rely on a single, integrated platform. Benefits to ERP include business process improvement, lower operating costs, and instant access to information.
Up until now, ERP systems have relied upon increases in efficiency of standard processes to enable cost savings. These include manufacturing, purchasing, order management and accounting processes. More recently, increased technology has enabled more complex processes to be managed. Examples of more complex processes include demand planning and scheduling, balanced scorecards, and more importantly to this audience, collections, credit risk and deductions processes.
Whereas ERPs have been based upon core transactions flowing through the system, the latest extensions to ERP systems use transactions created from these transactions. For example, in the Credit to Cash process, credit reviews will be based upon customer data, delinquencies determined based upon invoice information and deductions derived from payment information. All of these new transactions are also influenced by the other processes. For example, a delinquency is determined from an invoice, but could also be influenced by the risk classification of the customer derived from the credit review.
But, to look at the capabilities in isolation is a narrow view. The ability to use this functionality of credit reviews together enables a greater gain than to use them individually. The next section will give an example of how to use this new functionality in harmony.
Part Two – Risk-based Collections Management
This effective use of and sharing of data and system mechanisms is the basis for risk-based collections management. The ability to enable risk-based collections depends on access to many different data elements and mechanisms to generate scoring of those elements. Examples of data elements and mechanisms are:
* Internal data elements – transactional and customer data to calculate delinquencies, and also for credit review and customer scoring
* External data elements – third party credit and industry data to enable objective credit reviews,
* A means to segregate customers for both scoring and strategy manipulation,
* Scoring engines to create delinquencies from AR transactions, risk classifications from credit reviews, and scoring of customers for strategies,
* Configurable collections strategies to manage different groups of customers
Consider the following collections management model illustrated in Figure 1 using the elements discussed in the previous section.
Customer Risk Classification
Initiating the process of risk based collections begins with determination of credit classification of the customer when a credit application is received, or when a periodic review is completed. A decision needs to be made based upon the data compiled. During the process the correct credit limit needs to be set and the risk classification established based upon corporate credit policy.
The decision could be automated based on a scoring model, and should incorporate data elements from external credit agencies (e.g., D&B, Global Credit Services, Experian, Equifax, etc), trade and bank references as well as information received from the customer (e.g. financial statements).
A couple of points should be noted.
* New Customers: If a customer is new, it may be better to segregate and track data building up in the system even though an initial risk classification has been assigned. For instance, conduct automated credit reviews monthly to reclassify the customer risk. After 3 months, slot the customer into the correct risk bucket for collections. Alternatively, tracking payment performance and credit limit usage (how often the customer exceeds credit limit) could be used to trigger changes in credit policy application.
* Automating credit policy with existing customers: If a system is being instituted with an existing customer base, customers should be classified with the credit review process using the data points above as well as any internal data (e.g., Agings, WAP, DSO, etc).
Classification of Delinquent Transactions into Segments
Once the risk classification has been created on the customer, customers can be grouped with each risk class having different scoring on the invoices to create the delinquencies. Transactions can be treated differently if a delinquency is based upon transaction data, and is not the invoices themselves. For example, low risk customers can have a greater grace period on terms than higher risk customers, and can be segmented accordingly before a delinquency is created. Alternatively, a standard single model across risk classifications can be used based on the terms of the customer (and any other factors) to decide delinquencies.
Scoring Customers for Collections Strategies
Once the segments and delinquencies have been created, customer scoring takes place to decide how to deal with those customers when collecting overdue transactions. In this example, customer segments are based on the risk classification and the delinquency data that has been compiled so far. This allows for multiple scoring models for each credit classification, applying different scoring models to different customers segments. The score result determines which collections strategy to apply to each customer.
A benefit of using credit information as part of your collections strategy determination will be where credit classifications overlap. For example, if you had two or more strategies within each risk classification, the low end of high risk could be similar to the high end of medium risk. Both customers could be treated the same, but an additional measure could be made of the trend of the customer as they are either moving down from high to medium or moving up from medium to high.
Other uses of segmentation and data to score customers for strategies include segregating customers by industry. A portfolio of customers could be built using SIC identifiers. Based on credit agencies’ data about customers as well as internal data, one industry may indicate an increase in risk. This could be due to DSO, WAP or increases in overdue invoices for a few customers in the industry. Or it may for the entire industry. Based on this measurement (incorporated into the collections scoring model), regardless of risk classification of the customer, that group of customers may have the collections strategies accordingly.
Of course, customers may be segmented on factors other than credit classification. An example has been noted above with new customers – if they are high risk (which is likely when there is no internal data), you may not want to collect aggressively until they establish a payment behavior. A further example is exception customers – The large customers are not likely to take kindly to aggressive strategies.
Configuring Collections Strategies
Collections strategies themselves need to be defined for each segment. Strategies can include outbound calls, dunning letters via email, fax & mail, and personal visits. Combinations of calls, various levels of dunning letters and visits enable effective collecting. Frequency of calls, levels and frequency of dunning letters, and visits can be determined for each risk classification and subsection of classification. Each strategy (the unique combination of frequency and content) is associated to a customer score range.
A mechanism to inform the collector when they should conduct each task is essential. Additionally, automation of as much of the entire process (e. g. who should be called, sending emailed, faxed or mailing of dunning letters, and timing of activities) should be a goal in the process.
Other Considerations
Best of Breed vs. Single Vendor
The advent of new technology both in software and hardware paired with a decrease in the cost of hardware has enabled the extension of ERP processes to business areas that have been traditionally manual or handled by interfaces with external applications. This calls into question the concept of best of breed applications (i.e. pick the best application for each area of the business, and integrate) versus a single vendor solution.
The advantages of single vendor revolve around immediate availability to all modules of data as business events occur. From a business aspect, especially where financial transactions occur across the enterprise, this is a big plus, but you are tied to one vendor. If the single vendor does not include best practices in their software solution, best of breed may have the advantage in specific business areas (but not likely across the enterprise), and dependence to a single vendor is no longer an issue. However, integrating best of breed solutions is unlikely to match up to a single vendor packages in terms of near real time availability of data even with EAI (Enterprise Application Integration) tools.
ERP systems now offer the ability to incorporate more complex processes into standard applications with the ability to easily build in best practices. Credit scoring is key to a true view of the customer across the enterprise (e. g. is it worth selling to this customer?). Sales organizations need to be aware that a sale is not of value if the customer is unlikely to pay. Alternatively, the enterprise needs to gauge the level of bad debt. If Sales is empowered to sell to higher risk customers, they need to be aware that a higher percentage of bad debt may occur. Collections organizations also need to be aware and change their strategies if credit and sales data indicate changes in general trends or specific customer behaviors.
What is Missing from the Enterprise Wide Systems Available Today?
External to the business, one answer is still the complexity of human interactions and decisionmaking. Any information heard by a credit manager or collections agent about a customer may be relevant to a credit decision. This could be information from industry groups, newspaper articles or any number of other sources. The future will require systems to have intelligent agents (software scanning the internet for information) continually mining the Internet for any data about customers, informing credit managers, and incorporating them into scoring customers. There should also be a mechanism for credit managers (or other employees) to enter information.
Internally, within systems, a view of the customer needs to continually change based upon business events. These could include:
* Exceeding credit limits (taking account of payment habits and external data pointing to successful expansion of the company versus overstretching),
* Additional prospects of business from subsidiary organizations within the customer enterprise,
* Changes in payment behavior,
* Changes in key metrics used by the enterprise
* Changes to economic trends in customer industries,
* Promotional activity with the customer and success of promotions. Sales should be aware of the risk of the customer when promoting to increase sales, and the system should take account of why these changes take place (i. e. are they driven by the company, or the customer).
Conclusion
The need for credit managers to assess customer risk and collections managers to track key customer and financial metrics will not go away in the near future. The level of sophistication is now enabling more complex processes to be built within ERP systems and the business applications they run.
Robin Walker is the managing partner at Caliber Application Services, LLC. He worked for BearingPoint (formerly KPMG Consulting) and Cap Gemini on large scale multinational ERP implementations in the area of Financials applications, and has been working with Oracle in development of modules within the credit to cash process. Prior to consulting, Robin had a variety if jobs in accounting and finance. Robin has qualifications in business, accounting and computing earned in the UK, an MBA from Drexel University, and is a member of the Beta Gamma Sigma AACSB Honor Society. His areas of interest are the credit to cash process and trade promotions management.
Robin can be contacted via email at rwalker@faliber-services. com or by phone on (609) 680-6180
Copyright Credit Research Foundation First Quarter 2008
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