fensterstock albert - credit scoring and the next step

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E A T U R E

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NTRODUCTIONThe use of credit scoring for evaluatingcredit risk is at an all-time high inAmerican businesses. This is probably due

to several factors inherent in the use of this technol-ogy. Among them, credit scoring is more efficientthan having credit department personnel evaluateevery potential sale, which saves moneya majorconsideration for corporate America these days.Additionally, Sarbanes-Oxley and its requirementspoint to the use of credit scoring as a good methodfor improving internal control over risk-based deci-sions. For the most part, however, regardless of thetype of scoring system in place, businesses are notgetting everything they can from the implementa-tion of a credit scoring system.

So, there is more that can be done, however, beforewe get into what it is, and why it should be done,lets briefly review what types of credit scoring

systems are available and the differences betweenthem. If the reader needs more than a brief review,you may want to take a look at an article I wrote,Credit Scoring Basics, that appeared in the March2003 issue ofBusiness Credit. This article covers thenature of a credit scoring model, credit scorecards,the requirements for maintaining a credit scoringsystem, and how to determine whether to build orbuy, subjects that will not be covered here.

What Is Credit Scoring?Essentially, credit scoring is a systematic methodfor evaluating credit risk that provides a consistent

analysis of the factors that have been determinedto cause or affect the level of risk. Factors areusually determined through an analysis of histori-cal bill payment activity as well as various descrip-tive parameters that may be used to classify anaccount into one of several defined categories orcustomer classes.

Once the analysis necessary to create the credit scor-ing modelor in most instances modelshas beenaccomplished, information about an applicantcompany and its credit experiences are entered intothe model, when a credit decision is needed. Then,

credit scoring software, usually provided by acompany that specializes in this type of analysis,helps to predict:

Whether the debtor company is likely to pay itsdebts on time, and/or

The likelihood the debtor company will file forbankruptcy or default on the debt.

What Are The Different TypesOf Credit Scoring Systems?The different types of credit scoring systems mostusually used to evaluate credit risk in B2B situa-tions are: Judgmental/Rules-Based Systems Neural Network-Based Systems Statistical-Based Systems Genetic Algorithm-Based Systems

They all have strengths and weaknesses and thenature of your business, the amount of risk you arewilling to take, and the funds available to developand implement the scoring system will play a majorpart in determining which technology is best for

your company to employ. To be sure that you employthe one that is best for your company, you shouldreview all of the options before you make a decision.

Judgmental/Rules-Based SystemsJudgmental/rules-based systems evaluate creditworthiness using a formula or a set of rules basedupon internal and external credit experience. Thedetermination of the rules used and their associatedvalues is a manual process that is dependent uponcertain individuals intuition and past experience.

Some of the strengths of this procedure are:

It is based on accepted credit risk standards(judgment) and takes such items intoconsideration as:- Customer payment history- Bank and trade references- Credit agency ratings- Financial statements- Various financial ratios

Past experience is the basis of the factors andweights used

Provides a consistent alternative for evaluatingnew credit applications

Some of its weaknesses are:

Credit Scoring And

The Next Step

bert Fensterstock isManaging Director of

Albert FensterstockAssociates, a

consulting firm thatspecializes in theapplication of theInternet and the

tilization of decisionsupport technologyfor improving credit

department risknalysis capability andollection department

efficiency. He canbe reached at516.313.1020

or via e-mail [email protected].

A L B E R T F E N S T E R S T O C K

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It is inefficient to build as all of the work is done manually The factor weights can be biased towards data elements

that may not be mathematically relevant to risk The effect of a specific factor can not be accurately meas-

ured and, therefore, the sensitivity to a change in thatfactor can not be accurately determined

Risk cannot be quantified as expressed by the probability

of payment within a specific time period or of default It is difficult, if not impossible, to determine the source of

error if the systems predictions are not accurate, therefore,updating the system in an effort to improve results is a hitand miss proposition

Neural Network-Based SystemsThese systems utilize artificial intelligence algorithms appliedto historical data to find relationships between account char-acteristics and the probability of default. They have the capa-bility to classify accounts into various credit risk classes suchas: good, indeterminate, delinquent, charge-off and bankrupt.


Has the ability to determine the characteristics that aremost important in predicting credit risk

In some circumstances, may be more flexible than standardstatistical techniques as no assumptions are made aboutthe relationships of the risk factors prior to analysis

Can deal with non-linear relationships Correlations between factors are accounted for Models are adaptive in that the nature of forecast errors

(i.e., bad model decisions) can be easily evaluated and themodels improved accordingly


Results are essentially a black box Credit personnel have no idea of the structure of the credit

scoring model, i.e., the weights connecting nodes betweenmodel layers and, therefore, cannot properly evaluate themodels decisions

Model output must be accepted by credit personnel basedon the word of an expert, i.e., the consultant who devel-oped the model, who is rarely a competent credit analyst.

Almost all applications in the credit industry have been inconsumer-based risk evaluation

Statistical-Based Scoring Systems

Statistical-based scoring systems utilize statistical analysis,usually in the form of a multivariate non-linear regressionmodel, (remember Y = a + bX, only with more variables, hencemultivariate), to estimate the probability a customer willdefault or become delinquent. These systems are very similar inoperation to judgmental systems except that the factors usedand their assigned weights are based on statistical analysis

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not human judgmentof a companys past operations togetherwith other data considered by the analyst as pertinent.


Can use and evaluate any internal and external data toscore accounts

Many factors are considered individually and simultaneously The model development process calculates and analyzes the

correlation between all of the variables to identify factortradeoffs and eliminate redundant relationships

Variables can be chosen by the statistical analyst to ensurethe relationships makes financial sense (e.g., older firmsare less risky then younger firms)

Users are able to identify sources of estimation error andimprove model accuracy


Need someone with a statistics background and experi-ence with credit information to build and implementthe model

Bulk of analysts time may be spent in data preparationand analysis of statistical relationships, rather than inmodel building

If there are a lot of variables to consider, an analyst mayneed to predetermine the important variables based on aseparate analysis

Some statistical models can be difficult to implement

Genetic Algorithm-Based SystemsThis is the new guy on the block and may offer, in certainsituations, a superior solution to any other method. GeneticAlgorithms (GA) are based on the principle of survival of the

fittest ala Darwin. Like selective breeding, a GA breeds aninitial generation of random predictive models. The modelsare tested for fitness against user-defined criteria. The bettermodels are more likely to be selected for breeding. Followingfitness evaluation, the GA will apply the basic principles ofgenetics to breed the next generation of models. Theseconsist of cloning, mating, mutating and the introduction ofrandom models.

An extensive trial and error process is utilized that may breedmillions of models resulting in a final model that can be usedto estimate the probability of payment within a given timehorizon. Basically, GAs are very sophisticated search algorithms

that by means of an iterative procedure search through all ofthe possible scoring models in an attempt to locate the bestscoring model.


Can use and evaluate any internal and external data toscore accounts

GAs can use 100% of the available data rather than ananalyst selected subset

The interaction between variables is not a problem As with statistical-based systems, users are able to identify

sources of estimation error and improve model accuracy


Extremely limited knowledge of this technology withinthe business community, almost all practitioners areuniversity-based

The few business-based practitioners have little experiencein applying GAs to credit risk analysis

In-house statisticians may resist the implementation of atechnology they do not know or understand

Why Use Scientific-Based Credit Scoring?With respect to neural, statistical and genetic algorithm-based credit scoring systems, the main reasons for using oneof them are: They provide a significantly more accurate analysis by

delivering 10 percent to 30 percentor moreimprove-ment in predicting customer credit risk over non-scientificmethods

The cause of prediction error in scientific-based modelscan be identified and these models can be improved andkept current. Updates can be scheduled on a regular basis,or as needed

They can provide the basis for meeting certain require-ments of Sarbanes-Oxley

Providing the work discussed in the next section is accomplished:

They can help in the development of optimal strategies fordealing with at-risk account.

Scientific-based credit scoring can dramatically increasecredit and collection department productivity

The Next StepWe submit that the implementation of a scientific-based creditscoring system is only the first step in improving credit andcollection department efficiency. To really achieve the maxi-

mum benefits available from these technologies you need to gothe next step, and sadly very few companies have.

Consider the obvious. Credit scores in a given B2B environmentwill range from very low (high risk) to very high (low risk). Yet,in most instances, the collection procedures applied within acompany are very much the same for all accounts shipped, untilan account starts falling behind in their expected payments.Then, the collectors spring into action to do that which theyshould have expected they would have to do at the time theaccount was shipped.

We propose a different strategy, whereby a given score prede-

termines the collection strategy to be applied from the time ofshipment. If different scores indicate a different level ofrisk, doesnt it make sense that they also imply that differ-ent collection procedures be utilized?

A Procedure For Achieving The Next StepAssume that the new scientific-based scoring system has beendeveloped and implemented; here is one approach to finishingthe job:

1. Determine a set of collection procedures that might rangefrom mild to very aggressive. Lets assume that you havedefined three different strategies, where one of the strate-

gies is what you currently do.


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2. Score all of your accounts using the new system, and clas-sify them into homogeneous groups or strata. If we assumescores can range from 0 to 100, then perhaps one stratacontains all of the accounts that scored from 0 25; asecond strata might contain all of the accounts that scoredfrom 26 50, and so on. You may also want to segmentthe strata if there are different divisions, i.e., even though

accounts have the same score there is another characteris-tic that you believe makes them different; such that theymight respond differently to the same collection strategy.

3. Within each stratum, select a random sample of somepercentage of the accounts. The size of the sample can bedetermined based upon the variance within the strata.I would suggest that this variance be based upon adiscounted cash flow (DCF) analysis of the accounts pastpayment history converted into a percentage of the maxi-mum DCF possible, where the maximum DCF occurs if theinvoice is paid at the time of shipment. Realistically, thisis the statistic you are trying to improve in that it meas-

ures the time-value of money. The sooner after shipmentan invoice is paid; the greater the percentage of themaximum possible DCF is achieved. Your credit scoringsystems provider can help you with this.

4. Next, divide the sample within each stratum into threegroups. You can do this by assigning every third accountto a different group.

For one of the groups, within each stratum, apply yournormal collection procedures. For each of the other

groups, apply one of the other procedures you determinedin 1. above.

5. Ninety to 120 days after you have started the test,compute the percentage of DCF achieved for each groupand compare it to the historical group average. You canalso compare the percentage DCF on an account-by-account

basis. There is a standard statistical test you can utilize toperform this comparison that will determine whether thedifference between your history and the current results aresignificant, i.e., the alternative procedures have produced ameasurable change.

6. If the alternative collection procedures have produced ameaningful change in the bill payment activity of certainclasses of accounts, youll need to consider whether youshould change your collection activities relative to all ofthe types of accounts where a more favorable percentage ofDCF has been realized. A simple rule-based program thatcan determine the type of collection procedure required on

an account-by-account basis and then direct the personnelassigned to these accounts how and when to handle themcan be readily designed and implemented into an inhouseor ASP based computer system.

If you want to achieve all of the benefits promised by scien-tific-based credit scoring, an analysis similar to the onedescribed above should be part of any credit scoring project.While this procedure may seem a little complicated, anybodywith a basic knowledge of statistical sampling should be ableto help you.

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fensterstock albert - credit scoring and the next step

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