An Overview
The Rise of Credit Scoring at the Corporate Level
Credit scoring is nothing new. Creditors and investors have always sized-up or researched the risk/reward of any business commitment or opportunity. Frankly, it is not unique to business but an inherent tool in building all relationships as we try to determine risk, reward, potential, and longevity.
Credit Scoring, in an environment of growing data resources, data volume, published 'opinions' and sources for opinion, tends to replace discernment with a set of 'truths' and calculations. Rating agencies and financial institution spend billions of dollars to leverage available information on business, executives, countries, climates, intelligence, and industries, to grade entities while weeding out rabbit trails.
More and more enterprises are seeking to develop or acquire their proprietary score cards. ERP platforms and rating agencies, or a partnership between the two, provide applications to score trade partners. An increasingly prominent required skill for Credit Professionals is scoring model maintenance and development.
The Dynamics of Risk Scoring Models
For businesses seeking the means to make or validate credit decisions, it is important to keep experience in the mix. Experience includes: a) knowing what key financial indicators tell you, b) intuition borne from the experience of making business decisions, c) knowledge of industries, financial analysis, regions, laws, climate, and customs, d) knowing the various sources of data and their reliability, e) and understanding the importance of building and leveraging reciprocating relationships with trade partners.
A Score card does not replace experience but rather highlights areas to be reviewed in a holistic and comprehensive evaluation. A Score Card is not just a final score but a comprehensive picture of areas of business performance and health. Each area can be scored. While you might not mind a B+ after your annual physical, you probably want to know sub-scores to know your strengths and risk factors.
Flexibility
Score cards are only as good as the inputs, logic, weighting and interpretation. A challenge here is to have a compensatory mechanism or logic so that if data components are missing, the trade partner is not penalized, or the model does not either become skewed or error out. Score cards should use several weightings to established grades on the a) strength or weakness of information, b) volatility of regions, c) business's experience with the partner and d) metrics or metric groups to determine relative values in measuring risk.
Dynamic in Scope
Score Cards cannot be one-dimensional. The score cards I develop, or evaluate are made up of three primary components, including a) financial with supporting notes, b) business or creditor experiences, c) environment or the business climate.
The financial component score will evaluate at least the balance sheet, income statement and statement of cash flows. It generates dozens of widely established ratios or scores for a full evaluation of health, determination of the business's scope, and to calculate a theoretical limit. The model generates ratios to measure risk focused on liquidity, profitability, leverage, efficiency, and solvency. Solvency scores would include the Altman Z-Score or Lambda Score.
The business component score is an observational assessment of a trade partner. The information either comes from outside sources or the current relationship. That information will often be drawn from the ERP system, credit reports, Customer sites, media sources, Sales and direct communications. Areas of focus are establishment as a going concern, management quality, business structure and cash movement, need for business, payment performance, and 'covenant/debt' quality.
The environment component score is drawn from outside expertise, empirical evidence, laws and Customer communications. Some key components or categories are country risk scoring, regional business climate, competition, traditional terms of sales, and laws/government restrictiveness.
Tolerance for risk is an overarching factor for all scoring models. It can be injected into the model as a series of weightings or in setting the score ranges. When models are developed and tested against current Customers, Management and Credit agree on the creditor business's 'tolerance for risk'.
The theoretical credit limit is a very important component to determining credit worthiness and risk and is often excluded. By setting a dynamic calculator you answer a question not supplied by the scoring model: Okay, the business is sound and should be a good partner, but at what point of AR exposure does it become a real or default risk?
The last question calls for a proactive solution.
If the theoretical limit is sufficient let's rock and roll!
If the theoretical limit is unacceptable for business to proceed, how do we get the sale? Do we obtain more information, shorten terms, or negotiate a more secure transaction?
If the theoretical limit is less than optimal, but much of the need can be justified
Okay let's consider the last point and 2nd most common case where the theoretical limit is less than optimal. Understand that the limit will generally line up, by design, with our 'tolerance for risk' and reflect the business's risk perception. Any limit decision outside the scope of the derived limit is also outside the authority of Credit, but is rather owned by Management. Credit is a fiduciary charge to protect Accounts Receivables. Management weighs risk/reward as a 'business decision', in consideration of Credit's assessments, but also applying other considerations. It is not wrong to go out of scope but Credit's role is to identify and communicate risk to Management. Management uses that counsel to form the basis for the 'business decisions'. Do we adjust terms? Do we accept a higher position among creditors? Do we assume a higher portion of exposure relative to the Customer's Accounts Payable, Working Capital or Net Worth?