credit risk handling in telecommunication

8/19/2019 Credit Risk Handling in Telecommunication

1/14


2/14

118 M. Szczerba and A. Ciemski

in order to nd the most risky customers. This article presents (based on theauthors business practice) the outcomes of the research work and the approachapplied in the commercial projects for telecommunication companies.

Technological development within telecommunications area has signicantly

progressed over recent years. This is connected with the growth in the compet-itiveness within this sector. Telecommunication companies are trying to get asmany customers as they can by offering them a lot of attractive deals and prod-ucts. Finding new customers becomes more difficult though when the marketgets more saturated. Customers who respond to those offers are very valuable,because they generate prot for the company. Unfortunately among them thereare some who fail to pay their bills and put the company at risk of making con-siderable losses. To minimize this risk companies can take precautions by usingdata mining methods.

Data mining help identify customers who may possibly fail to pay their bills.To measure the customer’s credit risk level it is essential to make analysis of the activation data. The results of analysis are fundamental part of the processaiming to prevent the company from increasing bad debt. Among tools used fordata analysis we can distinguish predictive models, which are the most impor-tant part in analysis process. Predictive models help to identify customers withhigher, lower and the lowest credit risk.

This article discusses issues concerning credit risk as well as methods detectingcustomers who may fail on their payments after the activation process. Each of

the chapters will be described below.Chapter two introduces credit risk. It begins with explanation of the termand how it is usually comprehended, and it ends with description of credit risktypes on which predictive models were based. Discussed issues form fundamentalknowledge needed to understand problem of credit risk and they are good intro-duction into next chapters. Chapter three presents description of classicationtrees as one of the data mining methods used to analyze credit risk problem.Classication trees are very well known method used in commercial projects.Chapter four introduces population of the data which was prepared for credit

risk analysis. Chapter ve describes two of seven predictive models detectingcustomers with the credit risk among the population. The rst one is activationmodel predicting credit risk based on all customer population and the second oneis predicting credit risk for individual customers. Chapter six and seven presentsummary of results and future research plans.

2 Credit Risk

“Credit risk” term is used in both everyday and scientic language. It can alsobe interpreted differently depending on type of economic activity. Usually theterm “credit risk” means the risk of unattainability of goals. The meaning creditrisk is not the same across different sectors like for example banking, insurance,telecommunication, energy, industrial and public sector. For telecommunication


3/14

Credit Risk Handling in Telecommunication Sector 119

area credit risk means decrease in potential prots, lack of the cash ow andnancial difficulties which can lead the company to the bankruptcy.

Telecommunication sector is one of the most competitive markets, because itchanges very quickly by bringing new technologies and creating new business

models. The more knowledge the company has got about its customers the moreattractive deal it can offer and this way gain competitive advantage over itscompetitors. Credit risk is sometimes dened as possibility that customer willnot keep to contact conditions and will create a nancial loss for the company.Telecommunication sector is changing and developing very quickly and it is im-portant to nd methods to lower nancial risk and protect company’s prots.Data mining methods became the best solution for this problem.

Telecommunication companies started to make several attempts to controlrisk management by for example verifying customers’ credit history before sign-

ing a contact with them and imposing deposit on customers with lower creditreliability. The deposit is a guarantee for company in case if the customer re-fuses to pay. Risk management activities are called credit scoring which meanscapability to fulll nancial contact obligations with a company.

The term “credit scoring” denes customer’s credit risk. Credit scoring canbe divided into application scoring and behavioral scoring. Application scoringis used when customer is entering in a contract with a service provider. Thisprocess includes ling in the application form (address, age, sex). Credit scoringprocess can be also used after signing the contract and is based on behavioral

customer data (for example the history of payments). It is called behavioralscoring.Special treatment of customers with a high credit risk allows the company to

minimize its nancial losses. It is also very important to have all the necessaryinformation when setting a credit limit for a new customer. Proper identicationof the customer and scoring his risk level (high, medium, low) lets the companyto lower its credit risk. Classication trees are used here to model credit risk.

3 Decision TreesDecision Trees are one of the data mining methods used to classify all observa-tions of population into groups.

Decision Trees, also known as classication trees, consist of nodes and edgescalled branches. The node is called predecessor if a node has got a few branchesconnected to other nodes called successors. If successor has not got any outgoingbranches, the node is known as a nal node (leaf). Successor is created as aresult of decision rule in a node, which splits observations in two groups and

sends them to successors.Construction of classication trees is based on a sample of data (population).Decision rule divides observations from the sample and assigns them to newnodes. Each node is characterized by a diversity of observations. All observationscreate population which is also known as a class or group. This populationconsists of observation vectors described below:


4/14


x 11 , x 12 , . . . ,x 1n 1 from the class (population) 1x 21 , x 22 , . . . ,x 2n 2 from the class (population) 2

. . .x g 1 , x g 2 , . . . ,x gn g from the class (population) g,

where: xki = x 1ki , x 2ki , . . . ,x pki is i-the observation of k-th class. Values of the

observation are coming from the p-dimensional set, where xki ∈ D . In otherwords, this set is a sequence of n-arranged random pairs, which can be notedas: (x 1 , y 1) , (x 2 , y2) , ..., (x n , y n ), where n = n 1 + n 2 + ... + n g , x i meansi-th observation and yi is a observation class label. Decision rule is based onsample of data which is also known as learning sample. Learning sample consistsof g subsample and each of subsamples consists of observations from one class(group).

When observations are already classied in the node, then split criterion is setup for each node. If all observations are classied within the same nal node, thenode changes to class label and shows how many observation it contains. Themain target of classication trees is to predict classication of new observations,which are based on division rules created on the basis of a learning sample.

Each of classication trees consists of subtrees, which are part of a main tree.Subtrees can be dened as “subtree of a T tree is a tree, which is a part of Ttree”. The main target of split rules is to divide learning sample in two groupsin a way that observations assigned in new groups should be the most similar to

each other.There are tree most popular rules used in automatic creation of classicationtrees: misclassication rate, Gini Index, Entropy reduction and Chi-Square test[9][10].

4 The Data and Variables

Database of a telecommunication company consists of individual and business

customers who signed a contract with a company between 1st of January 2007and 31st of March 2008. During this period of 15 months customers were observedand their payment behaviour examined.

Database includes 53433 observations which make up customers’ population.This population consists of 48663 customers who are willing to pay for invoices(good customers) and 4770 customers who had their contracts canceled becausethey failed to pay (bad customers). Customers were then divided in two groups -individual and business, where the rst one includes 35941 observations and thesecond one 17492 observations. Among the group of individual customers there

are 32486 good customers and 3455 bad ones. By analogy, in a business groupthere are 16177 good payers and 1315 bad payers.These two groups of customers were also divided based on a type of services

they use, that is Internet and telephone. Table 1 presents a size of the populationof customers using different type of service. This division will be used later toconstruct predictive models.


5/14


Table 1. The size of population divided with regards to the customer type and servicetype

Population Amount Good payer Bad payer Bad payers per

All population 53433 48663 4770 8,93 %Individual customers 35941 32486 3455 6,46 %Individual customers - Internet 15595 14841 754 1,41 %Individual customers - Telephone 20346 17645 2701 5,05 %Business customers 17492 16177 1315 2,46 %Business customers - Internet 2631 2491 140 0,26 %Business customers - Telephone 14861 13686 1175 2,20 %

5 Credit Risk Models

Seven models were constructed to examine the customers’ population. Beforethey are described in detail, their construction principles will be presented.

Principle of model construction was customer’s activation data signing a con-tract with a telecommunication company. Models were called activation models.Models include customers who signed a contract for 12, 24 and 36 months anddecided to choose either telephone or internet services or both. Contracts were

signed between 01st January 2007 and 31st March 2008. Models were built forindividual and business customers, regardless of the number of services on cus-tomer’s account.

Modeling includes customers who signed a contract from 01st January 2007and 31st March 2008, who failed to make their payments but their contract wasnot terminated. Main target of the model was to predict a risk of payment fail-ure on 75th, which is when a customer’s account is closed during debt collectionprocess. Every model has got dened a target function called reason of terminat-ing a contract which divides customers’ population into good payers (BWSFE

value) and bad payers (WSFE value).Customers who canceled a contract within 10 days from a signing date (basedon special offer conditions), or who did not receive parcel with telephone orInternet device from a courier ( which means that activation process did notstart) or customers who were validated negatively were removed from database,which was the basis of modeling. The contacts, which were overload notes, werealso eliminated from database. The next principle is that not all the invoiceshad payment corresponding with invoice during period 01st January 2007 and31st March 2008 in spite of invoices having balance status in the database and

corresponding balance identier (which means there is a payment connected toinvoice). Therefore , if as of 2008/03/31 there was no payment recorded, it wasassumed that the customer did not make a payment and that he has became adebtor.

There are seven activation models constructed. First one is based on thewhole customer population, Two activation models based on a type of customer


6/14


(individual and business). Four activation models for individual and businesscustomers divided further by service type. Two of them are presented below.

5.1 Credit Scoring Application Model for Whole Population

Model analyses whole population where 8.93 % is a percentage of bad payers.Percentage of bad payers was presented on gure 1. Good payers are marked asBWSFE.

Fig. 1. Percentage of bad payers in relation to good payers in the whole population

Customers who are bad payers cause nancial losses to the company. Thereason of these losses is customer’s failure to pay his bills. This is causing lack of cash ow in the company and impacts the company in a negative way creatingbad debt. Bad debt is very dangerous and it might be the reason of nancialcollapse of the company. Therefore it is crucial for companies to detect and takeup preventive actions.

Fig. 2. Amount of bad debt for individual and business customers

Bad debt occurrence was presented on the chart which describes the amountof bad debt during months (Fig. 2). There were taken into consideration follow-ing lengths of time: 25, 45, 60, 75, 90, 120 and 180 days from the moment of customers’ activation. The chart is based on entire customer population.


7/14


The main aim of the model is to identify reasons for bad debt formation whichwill be used to predict a moment of deactivation caused by failure to pay on 75thday, which is when a customer’s account is closed during debt collection process.

Whole population has been classied and arranged according to the impor-

tance of attributes showed on gure 3. Figure 3 presents a list of variables withimportance measure calculated for each of them, based on training (trainingcolumn) and validation (validation column) set. The last of columns, called Im-portance, shows graphic representation of earlier calculations, where dark greyline means variable estimation based on training set and bright grey line - esti-mation based on validation set.

Fig. 3. Classication of variable importance

According to attributes described above, a classication tree was constructedfor entire population. Classication tree was presented on gure 4.

Fig. 4. Classication tree based on whole population


8/14


9/14


Fig. 6. Misclassication rate chart based on training and validation data

Random model often means making decisions at random. On the basis of this

chart it is easy to notice how many times a probability of better results increaseswhen model is applied. Model can be also compared with base model which doesnot use estimation. The baseline curve on the chart presents results for constantnumber of successes, which means probability of success on validated dataset.In addition, it has been tested how a node slip criterion effects construction of a tree. Therefore two additional models where constructed where split criterionwere Gini Index and Entropy reduction. Results were presented on the gure 7.

Fig. 7. Results of comparison credit scoring models with various node split criterion

The best result was achieved by model with Gini Index as split criterion. Ithas received the highest answer in subsequent units. The second best model afterGini Index criterion is a model which used Chi-square test as split criterion. Thelast one is a model which was using Entropy reduction.

5.2 Credit Scoring Application Model for Individual CustomerPopulation

Credit Scoring model for individual customers population. Model analyses indi-vidual customers population where 9.61 % is a percentage of bad payers. Percent-age of bad payers was presented on gure 8. Good payers are marked as BWSFE.


10/14


Fig. 8. Percentage of bad payers in relation to good payers in individual customerspopulation

Customers who are bad payers cause nancial losses to the company. Thereason of these losses is customer’s failure to pay his bills. This is causing lack of cash ow in the company and impacts the company in a negative way creatingbad debt.

Fig. 9. Classication of variable importance

The main aim of the model is to identify reasons for bad debt formation whichwill be used to predict a moment of deactivation caused by failure to pay on 75th

day, which is when a customer’s account is closed during debt collection process.Whole population has been classied and arranged according to the importanceof attributes showed on gure 9.

Figure 9 presents a list of variables with importance measure calculated foreach of them, based on training (training column) and validation (validation col-umn) set. The last of columns, called Importance, shows graphic representationof earlier calculations, where dark grey line means variable estimation based ontraining set and bright grey line - estimation based on validation set.

According to attributes described above, a classication tree was constructed

for entire population. Classication tree was presented on gure 10.A colour of a node depends on quantity of observations which affect targetvariable. The more observations determining non-payment there are, the darkerthe node is. In addition, the thickness of the line can vary as well. It depends onquantity of observations in branches in relation to quantity of observation in theroot of a tree. It means that line is thicker when the number of observations is


11/14


Fig. 10. Classication tree based on individual customer population

Fig. 11. The most risky customers

bigger. A tariff, which made the rst split in a root of a tree, was chosen as themost important variable. Split method was used here based on Chi-square test.

Whole population has been divided into good and bad payers, which is il-lustrated in leaves of classication tree. Leaves of classication tree have beenanalyzed and classied from the most risky customers to the least risky cus-tomers. Leaves in the gure 11 have been presented in order according to whatpercentage of target variable had been used. The colours used in the gure de-scribe which type of set was used in data analysis. Dark grey bar means variable


12/14


estimation on training set and bright grey bar is estimation based on validate set.For every decision rule important results have been presented, which in additionshow a more detailed domain in telecommunication company reality.

The constructed model can assessed with regards to matching training data

and validation data. Figure 12 presents on the x-axis a model assessment measurefor different subtrees, where a subtree is a tree which was built by pruning nalnodes.

Fig. 12. Misclassication rate chart based on training and validation data

Fig. 13. Lift chart

The chart also presented misclassication of observation rate in training andvalidate dataset. A subtree ts better to the dataset, if misclassication rate of the subtree is closer to zero value. If we look closely at subtree 15 with 15 leafs,we will see it has got the smallest misclassication rate based on training andvalidate set, which means the subtree ts the most data. Trees tend to t better

to training data rather than to validate data because decision rules are createdto t training data.Figure 13 presents model assessment which estimates how the model ts the

data. The chart compares a set of observations in validation dataset with esti-mated model results based on training dataset. A Y-axis contains characteristics,which depend on observation frequency in various groups.


13/14


The chart (gure 13) illustrates how results (predictions) have improved afterapplying model in comparison to baseline, when estimation was not been done.Random model often means making decisions at random. On the basis of thischart it is easy to notice how many times a probability of better results increases

when model is applied. Model can be also compared with base model which doesnot use estimation. The baseline curve on the chart presents results for constantnumber of successes, which means probability of success on validated dataset.In addition, it has been tested how a node slip criterion effects construction of a tree. Therefore two additional models where constructed where split criterionwere Gini Index and Entropy reduction. Results were presented on the gure 13.

The best result was achieved by model with Chi-square test as split criterion.It has received the highest answer in subsequent units. The second best modelafter Chi-square test is a model which used Entropy reduction as split criterion.

The last one is a model which was using Gini Index.

6 Summary

In previous chapter there were models and methods described which support acompany in decision making. These models allow calculating protability for atelecommunication company through measuring customer value and determininglevel of the risk. By using these models we can divide customers into groups with

high, medium, low risk and examine their features are as well as choices theymake. In addition these models can be used to prevent nancial debt by, forexample, implementing deposit policy. The deposit is usually imposed on certaingroups where customers were assigned after data analysis.

Activation models presented here were used to predict customer’s failure topay after the debt collection process had nished. The most signicant variable isa tariff, chosen by an individual customer. This variable divides whole populationof customers into two groups where one is estimated to be ninety percent of goodpayers and the other one ten percent of bad payers.

The most risky customers share some common features. This group can bedivided into certain types of clients. Individuals customers who may fail to paytheir bills activate service for phone restriction and they do not apply for phoneinstallation address. Existing business customers on the other hand do not needto show their document of nancial reliability when signing a new contract witha telecommunication company. In addition individual customers take advantageof exceptions when signing up for new contract. Exceptions usually include illeg-ible copy of identity document, out of date national judicial register or illegiblepayment date on the bill. The characteristic quality of a business customer is

that they do not agree to disclose full information about their nancial obli-gations. Customers with high probability of failing to pay choose the highestInternet transfer and most expensive tariff with special start price. The contractis usually signed for 36 months. Furthermore, credit limit of 300 polish zlotychincreases nancial risk for the company.


14/14


The lowest risk customers are individual customers, who choose low, mediumor no specic tariff at all, or who negotiate better offers, or the ones who do notget any credit limit at all.

7 Future Research

The aim of future research is to develop activation models on the 45 day afterdebt collection process has begun. If the customer fails to pay within 45 daysfrom date of payment, the debt collection blocks customer’s outcoming calls.Therefore it is important to build models which would predict non-payment of an invoice. The next topic for examination could be analysis of other activationmodels based on different split criterion in decision trees. The aim of this projectis to discover new rules which would be able to predict customer ability to pay.

Another aim of future research might be further analysis of activation modelsbased on different split methods than those described earlier in the article. Re-sults of this new analysis could bring very useful information in discovering newrules which identify customers creating nancial risk for a telecommunicationcompany.

Telecommunication Company, which was used as a basis for research in thisarticle, is currently growing on the market and is aiming to increase the numberof its customers. Once it achieves a satisfactory level of clients, the company

will start to analyze customer’s data to prevent non-payment of invoices and toimprove the process of client verication.

References

1. Bluhm, C., Overbeck, L., Wagner, C.: An Introduction to Credit Risk Modeling.Chapman & Hall/CRC (2002)

2. Keating, C.: Credit Risk Modelling. Palgrave (2003)3. Gundlach, M., Lehrbass, F.: CreditRisk+ in the Banking Industry. Springer,

Heidelberg (2004)4. Grandell, J.: Aspects of Risk Theory. Springer, Heidelberg (1991)5. Lando, D.: Credit Risk Modeling: Theory and Applications. Princeton University

Press, Princeton (2004)6. Bühlmann, H.: Mathematical Methods in Risk Theory. Springer, Heidelberg (1996)7. Lu, J.: Predicting Customer Churn in the Telecommunications Industry – An

Application of Survival Analysis Modeling Using SAS. In: SUGI27 Proceedings,Orlando Florida (2002)

8. Hadden, J., Tiwari, A., Roy, R., Ruta, D.: Churn Prediction: Does TechnologyMatter. International Journal Of Intelligent Technology (2006)

9. Breiman, L., Friedman, J.H., Olsen, R.A., Stone, C.J.: Classication and Regres-sion Trees. Chapman & Hall/CRC (1984)

10. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning:Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)

credit risk handling in telecommunication

Documents