Rules Based Credit Scoring Methodology
CRF thanks Credit & Management Systems, Inc. http://www.icmsglobal.com
Today’s credit professionals must make accurate,
on-the-spot decisions. Of course, you have high value and/or high volume of
credit decisions. Departments are
consolidating and slimming down as well as under more scrutiny from
auditors. Under these conditions, how
do you perform the detailed and consistent analysis needed to avoid
unneccessary credit risk? Credit scoring can help. Credit scoring, by
definition, is a method of evaluating the credit worthiness of your customers
by using a formula or set of rules. Depending on the make up of your customer
base, credit scoring can produce considerable benefits to some firms and
somewhat lesser benefits to others.
The Benefits of Credit Scoring
- Speed - When used with
an automated software system, each customer is evaluated or scored in
seconds. Customer orders are approved without delay. Also automated data feeds can speed up
data entry of financials, ratings, account status and exposure, and other
key data points.
- Consistency and
Accuracy - All factors involved in the credit score are considered and
used in the same way. Since credit scoring analyzes each customer using a
similar set of rules, there is consistency in the evaluation process by
the entire staff at all locations.
Plus human error is eliminated.
- Reduced Bad Debt
Losses - When approving new customers, all of the necessary factors
involved in the credit decision process are received and scored. High-risk
customers are identified as exceptions and reviewed by a credit analyst.
Customers on the verge of bankruptcy are not approved because of missed
information or incorrect analysis. Orders are not approved simply because
the dollar amount is less than the automatic credit approval amount. By
quickly identifying existing customers that require immediate attention
you can hold orders that otherwise may have been shipped, and take legal
action while assets are still available to be attached.
- Reduced Personnel
Costs - Particularly with firms having many thousands of customers, the
impact of credit scoring, combined with the use of an automated software
system, can significantly reduce personnel costs. Each activity in a
credit department has personnel cost associated with that activity. With
automated credit scoring, fewer people are needed to research customers,
check references and make decisions.
- Collection Activities
are Prioritized - Credit scores enable the credit executive to have
different collection strategies for low risk, medium risk and high risk
customers. When credit risk scores are coupled with amounts owed,
collection activity is prioritized.
- Decision support and
planning tools - Credit scoring enables the credit executive to prepare
reports that accurately reflect the quality of the total accounts
receivable portfolio, and other reports that will reveal if certain groups
of customers carry more risk. Additional reports will focus attention on
the amount of bad debts for each risk category, pricing strategies, overly
rigid or liberal applications of credit policy, credit department staffing
and expenses, and other issues vital to a firm’s future.
- Comply with audit
mandates – Due to Sarbanes-Oxley Act, auditors are more likely to require
supporting materials on the collectability of receivables and class of
receivable. Expert reviews can
reveal inadequate bad debt reserve and overlooked significant write-offs
from prior periods. This failure
could result in material errors in past and current financial statements.
Credit scoring enables you to support decisions by tracking data evaluated
and how decisions were made.
Furthermore, you can score to regularly re-evaluate your portfolio
in a timely manner. In this way,
your decisions will be more reliable and supportable for financial
reporting.
The Different Types of Credit Scoring
Credit
scoring is based on the assumption that past experience can be used as a guide
in predicting credit worthiness. There are two types of credit scoring models. Both can be statistically validated.
Judgmental Scoring Model
A
judgmental scoring model is based on traditional standards of credit analysis.
Factors such as payment history, bank and trade references, credit agency
ratings and financial statement ratios are scored and weighted to produce an
overall credit score.
The
determination of which factors to use, and how each will be scored and
weighted, is generally based on the credit executive's past experience with
their company, the products or services they sell, and the industry they are
in. Judgmental models are enhanced by comparing industry financial profiles
using peer groups from (RMA) Risk Management Associates Statement Studies.
Including scoring factors that reflect the individual characteristics and
policies of their own firm further enhances the judgmental model.
Judgemental
scoring is the most straightforward to implement because it uses your credit
policies and decision process, the number of rules are easily set, and the
grading scale can be simple or complex.
Therefore, it is easier to understand and augment.
Statistical Scoring Model
Statistical
models function in much the same way as judgmental models. However, in choosing
the factors to be scored and weighted they rely on statistical methods rather
than the experience and judgment of a credit executive.
Statistical
models consider many factors simultaneously, a process that calculates and
analyzes multivariate correlation to identify the relevant tradeoffs among
factors, and assigns statistically derived weights used in the model. The key factors are generally captured from
credit agency reports and the credit files of the client.
Statistical
models are often described as a scorecard, a pooled scorecard, and a custom
scorecard. A scorecard uses data from one firm. A pooled scorecard uses data
from many firms. A custom scorecard blends a statistical model with some of the
factors used in a judgmental model.
Who Uses Credit Scoring And What Do They Have In Common?
The
first firms to use credit scoring were credit card companies and the consumer
lending divisions of commercial banks. The huge number of transactions involved
in consumer credit necessitated a computer generated score to approve and
service their customers in a cost effective and timely fashion.
Fortunately,
the credit information they needed for their statistical scorecards was readily
available, much of it free. Their credit application provided data concerning
employment, annual salary, home value, mortgage and other obligations.
Additional data was available in consumer credit reports that were usually very
comprehensive.
The
information required to conduct a credit appraisal for a consumer is far less
than that of a business. A salary of $50,000 can be measured easier than a bank
reference or a financial statement.
Finally,
the dollar amount of each consumer credit transaction is usually low, so that a
single scorecard rejection of a sale will have little impact on overall sales
revenue.
All
of the above factors contributed to making credit scoring quite successful for
those firms in the consumer credit business.
The
second group to use credit scoring was banks, leasing companies and finance
companies lending to small business firms. Similar to the credit card business,
the numbers of transactions were high and the dollar amount on each relatively
low. Since they could not afford to spend hours gathering and analyzing credit
information on each transaction they turned to statistical scorecards.
Now,
credit scoring is beginning to be used by business credit providers of all
types.
As A Business Credit Provider Should You Use Credit Scoring?
If
you have a very high number of customers, and the dollar amount involved with
most of these customers is small, the decision is easy. In fact, you have
little choice but to use credit scoring if you want to increase efficiency.
Because these small business transactions are quite similar to consumer credit
transactions, a combined business/owner credit-scoring model is very often
used. By doing so, the credit executive can gain the same benefits that
consumer credit providers enjoy; namely speed, accuracy, consistency and
reduced personnel costs.
On
the other hand, if you have fewer customers, and medium to large dollar amounts
involved, you need to use credit scoring a little differently to gain similar
benefits.
Consider
first that credit scoring business customers is not as precise as credit
scoring consumer customers because the amount of credit information varies
considerably in business credit and the information is more difficult to
validate and analyze. Secondly, the larger dollar amount of each transaction
will have a far greater impact on overall sales revenue.
These
differences can be compensated for by adjusting how you use the credit score.
In consumer credit scoring, the ultimate goal is to say yes or no on 100% of
the customers. In business credit scoring, you might want to set a goal of
saying yes to the 80 or 90% of the customers whose credit score is good, and
the dollar amount of the transaction is less than your credit limit model. In
other words, say yes to the easy, safe transactions. Then, don't say
"no" to the exceptions; instead refer them to a credit analyst to
decide.
The
credit information gathered for these exceptions will be considerably more
extensive than for those customers that were automatically approved. Likewise,
the credit analyst will typically have a sense of the relative importance of
each piece of information gathered in the decision process and quickly reach a
conclusion.
Perhaps
the most important reason for business credit providers to use credit scoring
is that scoring is the only effective tool to quantify the risk being taken. It
is difficult for a credit executive to do a complete job without having scores
to understand what risk is being taken at the portfolio level. It is even more
difficult to affect changes in your firms credit risk policy unless you have
something that allows you to quantify risk and communicate to senior management
the fact that your accounts receivable portfolio value has changed. Credit
scoring allows credit executives to fine tune credit risk guidelines over time.
An Example Judgmental Scoring Model
A
judgmental scoring model is used in this example because it is an effective scoring
model, and the simplest scoring model to implement. It uses your credit
policies and decision processes, the number of rules are easily set, and the
grading scale can be simple or complex.
And it does not require the services of a costly third party to create
and maintain.
In
this scoring model, the goal is to calculate an overall risk score based on the
principle that the risk or credit worthiness of a customer can be evaluated on:
- Traditional credit
information - This is credit information that is non-financial data that
you would normally use when making a credit decision. Items such as
control years, trade references and pay history fall into this category.
- Credit Agency information
- The outside ratings that you normally consider in your credit decision
process. The D&B rating, Paydex score and Experian Intelliscore fall
into this category.
- Financial Statement
scores – Ratios scored comparing peer companies within the same financial
year by industry by size (sales or assets).
Each
piece of credit information, from trade references to financial information, will
be scored on a 1-6 scale in which 1 is the best score and 6 is the worst.
You
establish the level of emphasis for each item by giving it a weighting. This is
a subjective element that “customizes” the scoring model to your specifications. Sample values for each category are shown
below.
Traditional Credit Information Scoring:
|
Information |
Weight |
Score |
Result |
|
Pay
history to others |
0.15 |
3 |
0.45 |
|
Pay
history to others |
0.15 |
3 |
0.45 |
|
Bank
rating |
0.05 |
3 |
0.15 |
|
Trade
reference #1 |
0.04 |
2 |
0.08 |
|
Trade
reference #2 |
0.03 |
3 |
0.09 |
|
Trade
reference #3 |
0.03 |
3 |
0.09 |
|
Industry
credit group |
0.05 |
3 |
0.15 |
|
Control
years |
0.10 |
5 |
0.50 |
|
NSF
checks reported |
0.05 |
6 |
0.30 |
|
Collection
claims reported |
0.05 |
1 |
0.05 |
|
Suits |
0.10 |
1 |
0.10 |
|
Judgments/tax
liens |
0.20 |
1 |
0.20 |
|
Traditional
Score |
2.61 |
||
Sample Traditional
Scoring Parameters:
As
you can see in the sample provided, traditional items typically consist of non-financial
information that you would normally use when making a credit decision. Feel
free to add the items that you use in your particular industry.
These
items might include some of the following:
