Mitigating Corporate Credit Risk
Companies with a broad range of customers often deal with the risk of customer non-payment due to bankruptcy, financial misfortune or fraud. The ability to accurately gauge these risks before entering into a relationship with a customer is critical to ensuring that revenues materialize as expected. In addition, tightly managing that risk on an ongoing basis with that customer also drives sustainable profitability from better cash flow management.
Figure 1 shows the typical problems that are present in the customer-to-cash cycle. The financial relationship with the customer is really divided into two distinct parts in the cycle. The first part is the front-end credit risk evaluation process where the credit worthiness of potential customers as well as existing customers is assessed. The second part is the back-end revenue assurance process that entails the collections, delinquency management and payment dispute resolution functions. Unfortunately, in many companies both of these processes operate as independent silos, even though they exist in the same finance department.
For corporations with a high amount of transactions, the front-end credit evaluation function can be quite cumbersome for the credit department due to the prevalence of manual and paper-based processes. The limited amount of personnel in these areas combined with the intensive financial analysis required has forced companies to look for new ways to drive efficiencies. This situation results in high amounts of bad debt write-offs, lower quality in the receivables portfolio, slower order processing cycles and higher operational expenses. The answer lies in automating as much of this front-end evaluation as possible and providing decision support tools that empower credit analysts to focus on resolving high-risk exceptions.
The back-end collections and revenue assurance processes also contain many deficiencies. When these functions are not seamlessly integrated to the credit risk scores in the front-end process, companies often find that their cash collections efforts are not effective, leading to higher days sales outstanding and past due payments. An integrated approach that takes into account the customer’s credit risk all the way from credit extension through collections offers a powerful alternative to current modes of operation in today’s credit and collections department.
Lastly, with the advent of corporate mandates such as Sarbanes-Oxley (SOX), all public and many private companies are now being forced to comply with the SOX mandate. SOX calls for higher standards in financial operations and reporting, particularly in the area of preventing fraudulent activities. The impact of SOX on credit and collections is quite extensive and requires the following:
Employing an innovative approach composed of best practices-based business process improvements and specialized technology can help companies quickly comply with these requirements while providing credit and collections personnel with a business-critical tool to carry out daily operations.
Managing corporate credit risk has undergone significant changes in recent years. Corporates have come to the realization that a ‘one size fits all’ approach does not deliver sustainable value when dealing with customer-facing financial processes. The end-to-end process of qualifying initial customer credit-worthiness, conducting ongoing credit reviews, carrying out collections functions and assessing customer payment performance requires tailored approaches unique to each customer. The iterative cycle of determining the risk associated with each customer and evaluating external and internal financial data is often a time-consuming, manual process that contains inherent inaccuracies and inefficiencies. All of this leads to sub-optimal decision making during the customer-to-cash process, resulting in lost revenues and higher operational expenses.
Companies that have successfully identified and managed risk in their receivables portfolio have segmented their customers using a broad range of criteria including:
In addition, these companies carefully track their customers’ performance and compare those metrics to other companies within the same industry segment. Having this depth of insight into customer behavior requires a specialized approach where companies can assign multiple types of attributes to specific customers that drive the overall credit and collections process. As an example, during new customer risk evaluation, the ability to review credit risk scores within the same industry segment or region gives companies an insight into the relative credit-worthiness of a prospective customer. Similarly, looking at a customer’s revenue growth and recent financial performance can also be an important consideration when developing a credit evaluation and collections policy. Due to the fact that multiple factors need to be considered in this process, an innovative approach towards credit scoring is now required.
Most currently available commercial credit scoring approaches are ‘black boxes’ in that they simply calculate a credit score and related limits without providing any insight into how those calculations were carried out and what factors influenced the outcome. In contrast, a new approach to credit scoring employs an innovative ‘glass box’ approach. This approach gives the credit analyst the capability to understand the calculations of credit scores, analyze past payment trends and modify different conditions to simulate exception scenarios. An exception scenario could entail predicting the revenue effects of raising a particular account’s credit limit by 1 or 2 per cent on a specific order. The many business levers that a credit analyst can manipulate allow for better informed and thereby more effective credit granting decisions.
This new approach to credit scoring incorporates information from multiple data sources, both external and internal, and utilizes flexible scorecards to calculate the resulting credit score. This credit score then drives the order processing functions of credit limit checking, rules-based acceptance or rejection of credit limit change requests and finally, workflow-based exception handling.
With respect to integrating with third party credit data bureaus, a common frame of reference should be used when comparing scores from one credit data provider to another. For example, using the flexible score-carding functionality, companies can normalize credit scores from Dun & Bradstreet and Experian, for example, and compare the results to one another before making credit extension decisions. In addition, publicly available financial data from SEC filings as well as bank and trade references can be used as inputs into the decision-making process.
The ability to take a holistic view of the company’s financial relationship with a customer is a critical success factor in effectively managing revenue risk. Companies that can successfully gain a 360-degree visibility into a customer’s credit exposure, past payment history and future performance can in turn make better decisions that positively impact cash flow.
Gaining global visibility into credit exposures: With the increased level of mergers and acquisitions across most industries today, companies have subsequently inherited multiple disconnected financial systems. One consequence of this is a lack of global visibility into credit and receivables data across different business units, regions and customers. Looking at credit exposures spanning multiple currencies, payment terms and locations requires a specialized technology solution with global capabilities. Tying together credit data across these systems with a cash flow performance management platform gives deeper insight into overall credit exposures and allows for better risk mitigation on a company-wide basis.
Real-time credit evaluation and decision-making: A cash flow performance management solution offers the ability to perform real-time credit evaluation and decision-making utilizing a rich set of credit data and credit scoring models. The flexible scorecards, business rules and workflows within the application are designed to maximize efficiency by automating low risk, standard credit evaluation scenarios while routing significant exceptions to credit analysts for further review. Using the customer segmentation capabilities, the application gives corporations the ability to manage individual credit cases in an effective manner using the deepest level of analysis required. Automated business rules also ensure that credit decisions are always being performed in a consistent manner.
New customers can enter in required data using online credit application forms that then feed into a pre-screening credit scoring process. External credit data, publicly available financial statements, trade references and input from internal departments can drive the scoring methodology to produce a credit score. Based on an acceptance of the potential customer’s credit risk, the new customer profile can then be set up in the back-end ERP system with subsequent creation of sales quotes and customer orders. If the business rules flag an exception in the process, the information can be routed to the appropriate credit analyst for review. All relevant information about the potential customer is available to the analyst during this decision making process.
Evaluating ongoing credit limit changes can occur at a variety of times during an existing customer relationship. Each customer is subject to an automated periodic credit review within the system. Past history and projected financial performance of the customer is used to review current credit limits and determine any required changes. This removes a great deal of redundant and manual activity from credit analysts and redirects them towards higher value-added tasks. Exceptions in this review process are flagged and can be reported to multiple parties, including the management team as necessary. Corrective actions such as proactively raising credit limits or renegotiating terms and limits with customers can then take place.
During order processing, credit checks can be carried out using deeper insight into credit risk. Requests to raise credit limits for specific orders can be compared to credit limits suggested by the application based on the credit scorecard results. Again, for low risk, standard credit requests, credit limit changes can be automatically accepted. For higher risk, exception scenarios, workflows can then route the request to credit analysts along with the full range of backup documentation.
Leveraging comprehensive credit risk analytics: The richness of credit and cash flow data allows companies to gain valuable insight into their credit risk and receivables portfolios. By leveraging both transaction level information as well as powerful financial analytics, companies can gain a higher level of financial transparency in their credit, collections and receivables management operations. Critical management reporting functions should include: continuous account monitoring, predicting bad debt exposure, proactive risk analysis, payment terms analysis and order management metrics.