ERP: Assisting in the Collections Process

Most companies have an Enterprise Resource Planning (ERP) system in place today. Unfortunately, ERP systems have only managed to be a transaction management system at best. This has led to a plethora of ancillary systems built to run on top of base ERP systems. However, more recently, technology has extended these transaction management systems to enable extended processes to be handled. Both credit scoring and collections processes are now appearing in ERPs. This article outlines how a major ERP system can now handle risk based collections management.

Introduction

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. However, more recently, processes have extended to enable more flexibility and better management of processes in ERP systems. Areas include planning and scheduling, Balanced Scorecards, and more importantly to this audience, Collections, Credit Management 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. In the case of the Credit to Cash process, these are credit reviews, delinquencies and deductions.

Modules, Modules everywhere…

The modules that create transactions each have the ability to automate parts of the credit to cash process. Credit Management modules have the ability to automate scoring of customers into risk classifications through business events such as credit checking an order, or periodic credit reviews using internal and external data. This will enable the concept of consumer credit scoring in a business environment becoming a reality. Collections modules can be used to manage collecting strategies based on internal data. Deductions management systems have the ability to automate the routing to the correct resources and closing of short and overpays.

But, to look at the capabilities of each of these modules is a narrow view. The ability to use these modules together can enable a greater gain than to use them individually. The next section will give an example of how to use these new functionalities in harmony.

Risk based collections management

The ability to enable risk based collections management depends on access to many different data elements, and mechanisms to generate scoring of those elements. The data elements and mechanisms in the example are detailed here:

  • Internal data elements – invoices to calculate delinquencies, other elements for credit review,
  • External data elements – to enable objective credit reviews,
  • A means to segregate customers for both scoring and strategy manipulation,
  • Scoring engines to generate delinquencies from AR transactions, risk classifications from credit reviews, scoring of customers for strategies,
  • Strategies to manage different groups of customers

 

The risk based collections management model example

Here is an example of a model using the elements noted in the previous section.

1.  Risk classification of customers

The most important process that needs to be set up is the credit classification of the customer when a credit application is received. A decision needs to be made based upon the application to set up both the credit limit and the risk classification. 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 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 weekly to reclassify the customer risk. After 3 months, slot the customer into the correct risk bucket for collections.

  • Instituting the system 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., tracked overdues, WAP, DSO, etc).

2.  Classification of transactions into delinquencies

Transactions can be treated differently if the delinquencies are not the invoices themselves. Once the risk classification has been created on the customer, customers can be grouped, each risk class having different scoring on the invoices to create the delinquencies. For example, low risk customers can have a greater grace period on terms than higher risk customers 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 required) to decide delinquencies.

3.  Scoring the customer for collections

Once the classification and delinquencies have been created, a scoring can take place to decide how to deal with those customers when collecting overdue transactions. At this point, customers can be segregated 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. A good example of this could be that credit classifications could overlap. A borderline low-end high risk could be similar to a borderline high-end medium risk. If you had 2 or more strategies within each risk, the low end of high risk could be the same as the high end of medium risk. Both customers are treated the same, but a 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 possibilities of scoring customers into the strategies could include segregating customers by industry. A portfolio of customers by company could be built up using SIC identifiers. Based on credit agencies data about customers as well as internal data, an industry could show an increase in risk. This could be due to DSO, WAP or increases in overdue invoices for a few customers in the industry. Based on this measurement (incorporated into the collections scoring model), regardless of risk classification of the customer, the group of customers could have the collections strategies augmented accordingly.

Of course, there will be a need to segregate customers not only based on the credit classification, but also due to other factors. An example has been noted above with new customers – if they are high risk (which is likely when there is no internal data), you do not want to necessarily collect aggressively (i.e., assuming they are not likely to pay). A further example is exception customers – The Walmarts of this world are not likely to take kindly to aggressive strategies…!

Collections Strategies

The strategies themselves need to be defined for each group. Strategies can include calls, dunning letters via email, fax & mail, and personal visits. Combinations of calls, various levels of dunning letters and visits should 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, and a mechanism to inform the collector of when they should conduct each task is essential. Automation of as much of the process (e.g. who should be called, automation of email, fax or mailing of dunning letters) should be a goal in the process.

Conclusion

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 out to business areas that have been traditionally manual or handled by interface 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 interface them all together) versus single vendor application.

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. Best of breed has the advantages of best practices in each business area (but not across the enterprise necessarily), and dependence to a single vendor is no longer an issue. However, interfacing is still not as good as single vendor packages in terms of near real time availability of data even with EAI (Enterprise Application Integration) tools.

ERP systems do now offer the ability to incorporate more complex processes into the standard applications. 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.

What is missing from the enterprise wide systems available today?

Externally to the business, one answer is still the complexity of human intervention and decision-making. Any information heard by a credit manager 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 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,
  • 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).

In short, the need for credit managers to assess customer risk will not go away in the near future. Tools and automation are becoming available in systems such as ERPs, that many companies have heavily invested in to automate manufacturing, order management and financial processes. The level of sophistication is now enabling more complex processes to be built within these applications.

It should be noted that the example model in this article and all functionality discussed, other than future requirements, are available and have been tested in Oracle ® E-Business Suite release 11i.9.

Copyright 2004. Reprinted with permission.