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55 Seiten, Note: 2,0
List of abbreviations
1. Introduction and Relevance within the economy
2. Theory about Credit Reporting Agencies and Scoring
2.1 Credit Reporting Agencies
2.2 History of Credit Reporting Agencies
2.3 Functioning of Credit Reporting Agencies
2.4 Credit Scoring
2.4.1 Data Sources of Credit Reporting Agencies
2.5 Data Protection Act and Criticism towards Credit Reporting Agencies
3. Social Network Data
3.1 Social Network Sites: A Definition
3.2 History and Development of Social Network Sites
3.3 Available Data on Social Network Sites
3.3.6 Family status
4. Data Warehousing and Data Mining
4.1 Data Warehousing
4.2 Data Mining
4.3 Big Data
4.3.1 Characteristics of Big Data
4.3.2 Applications of Big Data
4.4 How to access Data
4.4.1 Payolution GmbH
4.4.2 Kreditech Holding SSL GmbH
5 Facebook profiles of private insolvent persons as an attempt
5.1 Attempt description
5.2 Result of the Attempt
List of figures
A.1 Rating Agencies Rating Guide
A.2 The four V's of Big Data
A.3 Attempt Details
"[The use if credit scoring Technologies ] has expanded well beyond their original purpose of assessing credit risk. Today they are used for assessing the risk-adjusted profitability of account relationships, for establishing the initial ongoing credit limit availability to borrowers, and for assisting in a range of activities in loan servicing, including fraud detection, delinquency intervention, and loss mitigation. These diverse applications have played a major role in promoting the efficiency and expanding the scope of our credit delivery systems and allowing lender to broaden the populations they are willing able to serve profitability."
Alan Greenspan, U.S Federal Reserve Chairman, in an October 2002 speech to the American Bankers Association
illustration not visible in this excerpt
On 31 December 2013, 757 million users logged on to Facebook. The tremendous number shows the huge size of the Facebook network. 1,23 Billion active monthly users produce more than 30 billion pieces of information every month. The stunning size of information can be used for different analyses. One area of application may be the checking of the creditworthiness of private persons.
In today’s world, the checking of the creditworthiness of private persons becomes more important, due to the increasing distance trade. The different trade partners usually don’t know each other. That leads to an information asymmetry in the sense of reliability. Additionally, the number of private insolvent person increased since 2000 dramatically. In 2000 there were around 14024 private insolvent persons in Germany and in 2013 already 121.784 (see figure 1). Even if the private insolvencies decreased after 2010, it is still on a high level. To resolve this information asymmetry and reduce the risk of the inability of customers to pay, companies can use the provided services of credit reporting agencies like Schufa, Creditreform or Arvato Infoscore. Those credit reporting agencies use different public and non-public sources to evaluate a private person's creditworthiness.
The highly discussed social network data could be a future database for the evaluation of the creditworthiness of private persons. Not only the high numbers of users, but also the available data on social networks, makes it an interesting source of information about a private person's financial situation.
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Figure 1 Private Insolvent Persons in Germany from 2000 to 2013 
Since there is a plurality of available information about private persons on social network sites, a hypothesis can be drawn, that social network sites can help to identify a private person's creditworthiness. Therefore it is assumed, that the accessor of the information is not a "friend" (see 3.1) within the network, with the analyzed person. All aspects are analyzed under the additional assumption, that the private persons don't know about the evaluation of their creditworthiness. Otherwise an attention towards this data could be drawn, and the behavior could change.
To understand the process of the evaluation of a private person's creditworthiness, chapter two will provide basic information about credit reporting agencies and scoring. It is especially important to see, which information is already used and how the scoring works. Therefore, a small side trip towards the history of credit rating agencies will be provided. But also the functioning and criticism towards credit reporting agencies will be discussed.
In chapter three, social network sites will be reviewed. First, social network sites will be defined. To understand how social network sites emerged, the development during the last years will be shown. The available data on Facebook, which could be relevant for the identification of a private person's creditworthiness, is overviewed and evaluated, too. It is especially important to see, which information may be used.
Chapter four highlights the importance of Data Warehousing and Data Mining. It will be explained how data can be stored and analyzed. The information on social networks can be defined as Big Data. Therefore, the characteristics of Big Data will be pointed out. Additionally, the statistical tool of similarity measures are explained, to see a concrete example how the data can be analyzed. To draw a connection to the praxis, two examples of firms who accessed Facebook data will be presented. The different problems, which appeared during the process, are also shown.
To identify which information is actually provided by private insolvent persons on Facebook, a sample was taken. Chapter five shows how the sample was taken and in which sense it is relevant regarding future researches.
Furthermore, in chapter five, the results of the Bachelor-Thesis are presented. In the last part, the conclusion demonstrates the interpretation of the results and point out future impacts.
In this chapter credit reporting agencies will be deeply discussed, to understand how they are functioning and which role they have within the economy. It is especially important to understand how Social Network Data could be implemented in the credit reporting agency's system and how the information in the context of scoring could be used. Important to know is, that credit reporting agencies are also known as credit bureau, credit agency or credit register. In this paper, the most common term "credit reporting agency" is used.
A credit reporting agency is a service provider which compiles reports on the economic activities, the creditworthiness and ability to meet financial obligations of companies and private individuals for the purpose of minimizing entrepreneurial risks e.g. in case of conclusion of business transactions. These details are stored in datasets and are transmitted in writing or in an automated process to inquiring and legitimate third parties for a fee. Credit reporting agencies and other types of public information registries are critical elements of a well-functioning and modern financial system. Credit reporting agencies and their credit reports are becoming increasingly important throughout the world, accelerated by the fast growing e-commerce market and globalization. They address fundamental problems of credit markets (1) asymmetric information between borrowers and lenders that leads to adverse selection and (2)moral hazards. To resolve the information asymmetries credit reporting agencies collect information, which is relevant to determine the creditworthiness of private persons or organizations. This information is collected via partners, which provide economic information about consumers or organizations. Those partners are integrated into the IT system of the credit reporting agency and transfer data constantly. As stated earlier, the access to the information is connected to a fee. The purchasable credit report consists usually of positive and negative information (see 2.4.1), so that responsible borrowers can document also good credit histories. The credit records are often connected to a score, which gives a summary of the creditworthiness.
Credit reports can also play a key role in improving the efficiency of financial institutions by reducing the process cost for loans as well the time required to process loan applications. This leads to the fact, that credit reporting ads value and makes the economy work more smooth.
The credit reporting agency was an institutional response to the problem of information asymmetry. Credit reporting agencies originated during the 1830s in the USA. Since then the credit reporting agency model has spread through the world. In almost every country exists at least one credit reporting agency. At the beginning, credit reporting agencies were only active in the Business-to-Business Market. During the American colonial era, credit terms stretched to a year or more. Of course they shortened until today, recently they span from 30 to 90 days, depending on the goods involved. In the United States business credit reporting appeared ahead of consumer credit reporting. Today we can distinguish between credit rating agencies and credit reporting agencies.
Compared to credit reporting agencies, credit rating agencies only evaluate companies and financial productd. Famous examples are Standard & Poor's, Moody's and Fitch. After the evaluation, credit rating agencies announce grades in the scale of school grades. The most famous rating grades are from Standard & Poor's e.g. AAA, AA, A, BBB, BB and so on, with plus and minuses as well. Another difference is that today the firms pay for the evaluation of their default rate. In the business of credit reporting agencies it is the other way round, the score or rating already exists and the investor pays for the access of to the information.
In Germany, credit rating agencies started to develop at the end of the 18th century. The risk of distance trade needed to be handled, because the salesman in Hamburg was not informed about the financial situation of his partner in Munich. In 1860 the first credit rating agencies were founded. They were associations, where the members were able to buy "blacklists". Those "blacklists" consisted of names and firms which were expected to become insolvent.
In a nutshell it is possible to say, that the institutionalization of credit reporting agencies originated as a side-job of salesmen in the 18th century. Later specialists and service providers known as credit reporting agencies overtook the task.
According to Poerting (1979) covers the functioning of credit reporting agencies, the collection and saving of information, including the formation of information. Ganßauge (1995) makes one further step, and includes the action of providing information to a certain audience. This includes the pre- and on-demand -collection plus -saving of information. It is a systematic collection and selection, combined with an advice.
Therefore, it is possible to say, that the general function of a credit reporting agency, is the collection, saving, analyzing and providing of information about private persons or consumers. It can be described as a "transformation" process, where collected information (Input) is transformed into new information (Output).
Furthermore, the transformation process can be split into two different tasks. (1) The processing of information and (2) the evaluating of information and advice for action. (1) Processing of information means e.g., the allocation of two different data sets to a certain consumer. This includes, that credit reporting agencies can combine old and new data. Under (2) evaluation of information and advice for action can be understood e.g. the advice of the credit rating agency towards an online-shop, which payment method should be offered to an individual customer.
In this chapter, the theories of credit scoring and applied practices are illustrated. To understand the term credit scoring, the two terms are split into the single terms credit and scoring.
The word credit comes from the Latin word "credo" which means, "trust in", or "rely on". That means, if something is landed to somebody this means this person trusts in him or her, that the landed object will be returned to the owner. Most people within the society understand the access to credit as a right, but it comes with its own obligations. Usually borrowers must pay the price of (1) creating the impression of trust, (2) repaying according to the agreed terms and (3) paying a risk premium for the possibility they might not repay. Here the word credit risk and creditworthiness come into the context. Credit risk means, that the borrowing party must be aware of the possibility that things may not be, as they seem. If there is a lack of trust, lenders will increase their chargers to cover the risk. In addition, the trust can be strengthened through securities, collateral or more information. The modern information age allows lenders to enhance trust by, using data about borrowers financial and other circumstances, whether at the time of application or ongoing thereafter. With this information gained the creditworthiness can be determined. According to Thomas et al. (2002) creditworthiness is not an attribute of individuals like weight, height, eye color or even income. It is an assessment by a lender of a borrower and reflects the circumstances of both and the lender's view of the likely future economic scenario. Sometimes people think they are not creditworthy to one lender. However, if the risk premium is adjusted, reveal more information, reduce the amount or shorten the term, a person might be creditworthy to another lender. It is sometimes just a question of the right price.
Credit is not a physical object or commodity. It is more a construct created by human. Before a lender gives credit to a borrower, he or she first needs some information about the borrower to evaluate, if he or she will pay back the agreed amount. In this case it can be assumed, that information is needed to manage the risk. This leads to the term information goods and information economies. In markets where information plays a huge role, we often have the phenomenon of asymmetric information. In sales situations, usually the seller is in a favorable position and benefits from the information rents. Because the seller knows more about his products than the buyer does. When we take a look at the situation, where someone gives a credit to another person, it can be identified, that the person who is receiving the credit, is in a more favorable position. Because he knows more about his own personal circumstances. The phenomena of adverse selection was first described by Akerlof and can be applied to the customers in credit and insurance markets. Likewise, for moral hazard, just as people may engage in riskier behavior when they know they are insured, so too many borrowers become less financially responsible, once they have funds in hand.
Scoring can be described as followed. "Scoring refers to the use of a numerical tool to rank order cases (people, companies, fruit, countries) according to some real or perceived quality (performance, desirability, salability, risk) in order to discriminate between them, and ensure objective and consistent decisions (select, discard, export, sell). Available data is integrated into a single value that implies some quality, usually related to desirability or suitability. Scores are usually presented as numbers that represent single quality..." Anderson (2007)
Scoring has become very important in areas where predictions about the future are needed. In the credit business, those predictions are highly crucial to evaluate the risk and determine the credit conditions. A predictive scoring model tries to analyze the past experiences and calculates the likelihood of a future event. The more information from the past is available, the better is the prediction for the future. Today, we can experience that computers are used to automatically combine scores and strategies to make decisions, it provides a form of artificial intelligence (AI), which substantially reduces the cost of decision making.
The different meanings of credit and scoring were explained to get a better understanding, now the single elements are put back together. A credit scoring transforms, with the assistance of statistical models, relevant data into numerical measures that guide credit decisions. Data in this context means credit related information about the customer, whether obtained directly from customer's internal systems (Payment experiences see 2.4.1) or the credit reporting agency. This could mean, that some companies (e.g. Onlinewarehouses) have their own scoring model and they don’t use external data from credit reporting agencies. Within the public, the credit score is often only called “score”. A score of a credit reporting agency is often just a number, grade or percentage. The most famous German credit reporting agency the Schufa, has several scores for private persons. The so called “Basis Score” is calculated in percentag and a private person can achieve a maximum score of 100%, which represents an excellent creditworthiness. Furthermore, the Schufa offers also the so-called “Branchenscores” which makes enterprises able to check the creditworthiness of private consumers. In theory, there is a variety of different names and types of scores, depending upon where and how they are used. Usually the name depends on (1) the information source (2) the task being performed or (3) what is being measured. Some examples are. Application Score, Behavioral Score, Collection Score or Customer Score.
From the different types and name of scorings, can be concluded that scoring is not only used for the decision-making process, it is also used to manage credit generally. This includes the measurement of risk, response, revenue and retention (4Rs), whether for marketing, new-business processing, account management, collections and recoveries. Figure 2 shows the different areas where scoring is used. An example for marketing would be, that certain shops only send their catalog to people who can actually pay for the goods.
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Figure 2 Scoring aspects.
The Information, which the credit rating agencies use to determine the score, comes from different sources. They can be either public or non-public. Furthermore, they can be differentiated in positive and negative data. According to Becker (2006), positive data exists when a person acts according to its contractual obligations. If a person always pays on time and fulfils the contractual requirements, then positive data exists. On the other hand, negative data exists where a non-compliant contractual behavior is displayed. According to Siegl (2011) Negative data exist only for 7% -15% of the population of Germany.
Address information is one of the main source, which credit reporting agencies use. Under Address is meant (1) Salutation, (2) first name and last name, (3) academic title and (4) postal address, including street name, house number, postal code and name of place.
The information is used to proof the correctness of the postal address and to proof if the postal address is accessible. Therefore, data is compared with the data of postal services. If there is no additional data about a person available, then usually address data is used as main source to calculate a score.
Payment experiences about private persons are also used by credit rating agencies. They include the earlier describe negative and positive data. Therefore, companies are allowed to report the negative payment experiences of customers after the third reminder of payment. In addition, when default actions are executed against a person, companies are permitted to transfer this information,too. To identify the payment experiences different public and non-public data sources are used.
Public data sources, are accessible by everyone. They include newspapers, telephone directories, internet data banks or data banks from the residents' registration offices. Data from social network sites can be accounted to public data, depending on the social networks terms and conditions. Today, Information of social network sites is not used by credit reporting agencies.
To non-public data sources belong non-public debtor directories or lawsuits, which credit rating agencies can access. Lists from the Chamber of Industry and Commerce are often used, too. They include information about a person's profession, date of birth, place of living and details about the affirmation in lieu of oath.
Internal company information is additionally used for the score calculation. This non-public information is gathered by the credit reporting agency's partner businesses. Those can include banks, insurance companies, utility companies, collection companies, mobile telephony providers and many more. Depending on the credit reporting agency's contracts with other firms. Usually the business partner's information system is connected to the credit reporting agencies' information system. It assures an automated process.
From time to time we can read in the newspapers, that private persons do not get access to a flat or a credit, because their credit scores are miserable. What makes some cases unique, is that the information on which the scores are based on, is just wrong or the calculation methods for the credit scores are questionable.
The German federal ministry of food, agriculture and consumer protection made a study about the scorings from credit reporting agencies in 2008. In the study 100 men and women between 20 and 66 asked at different credit reporting agencies for their own credit report. The result was frightening. Not at one tested agency was the information about the consumers complete. At the Schufa for example, were 35% of the provided information incorrect and 26% of the incorrect data was caused by the Schufa itself. Other incorrect data is also often integrated into the credit reporting systems by contractual partners of the agencies. It leads to the result that some consumers got negative elements in their credit report, which were not caused by themselves.
The credit reporting agencies get a lot of criticism towards their outdated and wrong data. As shown by the study, it is justified. Companies take decisions based upon the provided information, and when the information is incorrect or outdated it can lead to an economic damage (see 4.4.3).
Another issue which is often criticized, is the calculation of the scoring. As lately approved by the BGH, the concrete calculation is allowed to kept in secret by the credit reporting agencies. Some measures, which are used, are highly discussed in their expressiveness e.g. the so-called geoscoring. Geoscoring is a scoring method where only geographical data is used to calculate the default rate. This could lead to the event, that a person gets evaluated according to his neighbors paying behavior. To avoid discrimination through geoscoring, § 28 b BDSG was introduced. It is stated that "...the probability value is not calculated solely on the basis of address data... if address data are used, the data subject shall be notified in advance of the, planned use of these data; this notification shall be documented."
To support the consumer, and make the credit scoring process a bit more transparent, a new amendment was introduced on 01.04.2010 to the BDSG. The duty of disclosure was integrated. Part of the disclosure is, that each private person can annually request his or her personal credit score. Additionally, information for scoring is only allowed to be used within 6 month, after the first saving. To enhance transparency credit scoring agencies are only allowed to use mathematical and statistical formulas. As stated earlier, the concrete formula can be kept in secret, but it must be able to detect possible mistakes in the data and have the right to proffer their own alternative explanations for any discrepancies.
The purpose and scope of the BDSG is to protect individuals against infringement of their right to privacy as the result of the handling of their personal data. Part III of the BDSG is specially addressed to commercial enterprises, in particular are mentioned credit reporting agencies. Therefore, the recording, alteration or transfer of personal data or their use as means to pursue own commercial purposes is only lawful if (1) necessary to create, perform or terminate a legal obligation or quasi-obligation with the data object, (2) as far as necessary to safeguard legitimate interest of the controller and there is no reason to assume that the data subject has an overriding legitimate interest in ruling out the possibility of processing or use, or (3) the data is public available.
Before the data from SNSs is analyzed, the history and the origins are pointed out. Therefore, the development of the SNSs culture will be shown. Additionally, the available attributes of users on SNSs will be analyzed, if there is a potential expressiveness about a private person's creditworthiness.
Since the introduction, SNSs like Myspace, Facebook or LinkedIn have attracted millions of users. Facebook has worldwide 1,1 Billion users (2013), what leads to an incredible Database. While their key technological features are fairly consistent, the cultures that emerge around SNSs are different. Many sites strength existing personal relationships and others help strangers connect based on shared interests, political views, or activities. Today it can be identified, that the different networks have different business models. Some set their focus on picture sharing and others focus on mobile connectivity, video-sharing or business relationships.
SNSs are defined as web based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection and (3) view and traverse their list of connections and those made by other within the system. Often in public discussions it is spoken about the phrase social networking sites, but scientists don’t want to employ the term “networking". “Networking” emphasizes relationship initiation, often between strangers. While networking is possible on these sites, it is usually not the primary practice on them. What SNSs makes unique, is the fact, that users can articulate and make visible their social network and communicate with them. It is more the feature of interacting with already existing and real friends that makes it so attractive. Not the opportunity that users can meet strangers. To lay open personal information, can lead to new contacts, but it is often not the goal. Primarily participants communicate with people who are already a part of their extended social network. To emphasize this articulated social network as a critical organizing feature of these sites, that is why it is more reasonable to label them “social networks sites”. SNSs have a big variety of technical features, but the heart consists of visible profiles that display an articulated list of friends who are also users of the site. Profiles are unique pages where one can “type oneself into being”. Once registered to a website, the user needs to provide some personal information like age, interests, location and hobbies. This data is then used to generate the personal profile.
The visibility of the profile varies by sites and according to user discretion. The so called “privacy settings” can be set by the users individually. That means the user can decide by himself, if he wants, that everyone who is searching through a search engine can find his or her provided information. Usually everything can be locked except for the profile picture and name. As you can see in Figure 2, in Facebook for example, the user can decide within four categories. The user can make information visible to (1) everyone, to his (2) friends, just some (3) selected friends or (4) nobody. Of course, the different SNSs vary in their privacy settings, so can users in LinkedIn see more information when they have a premium account.
illustration not visible in this excerpt
Figure 3 Privacy Settings, Screenshot Facebook
The term of contacts differs from site to site. The most popular terms are “Friends”, “Contacts” or “Fans”. To simplify everything, we will use the term Friends. Many SNSs require bi-directional confirmation of friendship. That means once you asked a person to be his/her friend, the selected person can decide again, if he/she wants to be your friend. The term “Friends” can be misleading, because the connection does not necessarily mean friendship in the everyday vernacular sense, and the reasons why people connect are varied.
The public displaying of connections is another crucial component of SNSs. The so-called “Friendlist” contains links to profiles. Through this feature, users are able to find fast other people from their real social network. Once a user landed on another users profile, he/she is able to leave a comment on the profile. This comments have different features in each SNSs. Additionally, on almost every SNS it is possible to write private messages through a chatting tool. While both private messages and comments are popular on most of the major SNSs, they are not universally available. Generally, SNSs are often designed to be widely accessible. Many attract homogeneous populations initially, so it is not uncommon to find groups using sites to segregate themselves by nationality, age, educational level, or other factors that segment society, even if that was not the intention of the designers.
 “Www.federalreserve.gov, Quoted inMays (2004:4),” accessed April 3, 2014, http://www.federalreserve.gov/.
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 Mike Friedrichsen, [ HANDBOOK OF SOCIAL MEDIA MANAGEMENT: VALUE CHAIN AND BUSINESS MODELS IN CHANGING MEDIA MARKETS (Springer, 2013). P. 375
 Creditworthiness: Borrowers' willinges and ability to repay.
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 Asymmetric Information: Differences in information available to different game players, expecially those that provide competitive advantage (game/theory/economics).
 “Studie - Scoring Im Praxistest 15.01.2008,” accessed November 4, 2013, http://www.vzbv.de/mediapics/scoring_studie_15_01_2008.pdf.
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 Andrew McAfee and Erik Brynjolfsson, “Big Data: The Management Revolution,” Harvard Business Review 90, no. 10 (October 2012): 60–66, 68, 128. P. 5
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 “Credit Report Agency,” accessed December 3, 2013, http://www.buergel.de/en/faq/credit-report-agency.
 Margaret J. Miller, Credit Reporting Systems and the International Economy (MIT Press, 2003). P.1
 Adverse Selection: Poor choices that result from information asymmetries, especially where these are conciously exploited by other parties (econonomics/insurance)
 Moral Hazard: Risk of parties to a contact changing their behaviour once a contract is in place (law/economics)
 “Studie - Scoring Im Praxistest 15.01.2008.”
 Jentzsch, The Economics and Regulation of Financial Privacy. P. 75
 Miller, Credit Reporting Systems and the International Economy. P. 114
 Lawrence J. White, “Markets: The Credit Rating Agencies,” The Journal of Economic Perspectives 24, no. 2 (April 1, 2010): 211–26, doi:10.1257/jep.24.2.211.
 An illustration of the credit rating angecy's grades will be found in the appendix (see figure 14)
 Transparenz in Kreditmärkten: Auskunfteien und Datenschutz vor dem Hintergrund asymmetrischer Information (Frankfurter Allgem. Buch, 2007).
 Verband Der Vereine Creditreform e.V. 2003/2004, 125 Years of Creditreform, Annual Report (Neuss: Verband der Vereine Creditreform e. V., 2004). P. 12
 Marcus Siegl, Auskunftei-Informationen Zur Reduzierung Des Zahlungsausfallrisikos Im Online-Handel: Empirische Analyse Und Ansatz Zur Verbesserung Der Auskunftei-Wahl, Schriftenreihe Innovative Betriebswirtschaftliche Forschung Und Praxis. - Hamburg 305 (Hamburg / Kovaéc, 2011). P. 69
 P. Poerting, “Die Funktionen des Wirtschaftsauskunftswesens aus der Sicht der Anfrager und der Beurteilten: Darst. u. Möglichkeiten zur Beurteilung ihrer Erfüllung” (1979).
 Klaus Ganssauge, Datenverarbeitung und -nutzung von Kreditwürdigkeitsdaten durch fremdnützige Verarbeiter: mit einer Darstellung der Rechtstatsachen bei der SCHUFA und der Organisation Creditreform (Duncker & Humblot, 1995). P. 24
 Siegl, Auskunftei-Informationen Zur Reduzierung Des Zahlungsausfallrisikos Im Online-Handel. P. 69
 Poerting, “Die Funktionen des Wirtschaftsauskunftswesens aus der Sicht der Anfrager und der Beurteilten: Darst. u. Möglichkeiten zur Beurteilung ihrer Erfüllung.” P. 25
 Andreas Horsch, Rating und Regulierung: ökonomische Analyse der Prozesse, Strukturen und Regeln der Märkte für Ratings (Nomos, 2008). P. 121
 Raymond Anderson, The Credit Scoring Toolkit : Theory and Practice for Retail Credit Risk Management and Decision Automation: Theory and Practice for Retail Credit Risk Management and Decision Automation (New York: Oxford University Press, 2007). P.4
 Credit Risk: The Potential financial impact of any real or perceived change in borrowers creditworthiness.
 Lyn C. Thomas, David B. Edelman, and Jonathan N. Crook, Credit Scoring and Its Applications (SIAM, 2002).
 Anderson, The Credit Scoring Toolkit. P.4
 Information goods: Information traded as a commodity
 George A. Akerlof, “The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism,” The Quarterly Journal of Economics 84, no. 3 (August 1970): 488, doi:10.2307/1879431.
 Anderson, The Credit Scoring Toolkit. P.4
 Ibid. P. 5
 Ibid. P.4
 “Studie - Scoring Im Praxistest 15.01.2008.” P.10
 Anderson, The Credit Scoring Toolkit.
 Application Score: Used for new business origination, and combines data from the customer, past dealing and the credit rating agency.
 Behavioral Score: Used for account management (limit setting, over-limit management, authorizations).
 Collection Score: Used as part of the collections process, usually to drive predictive dialers in outbound call centers, and incorporates behavioral, collections and bureau data.
 Customer Score: Combines behavior on many accounts, and is used for both account management and cross-sales to existing customers.
 Anderson, The Credit Scoring Toolkit. P. 9
 Ibid.P. 9
 I. Becker, “Datenschutzrechtliche Fragen Des Schufa-Auskunftverfahrens. Unter Besonderer Berücksichtigung Des Sogenannten ‘Scorings’.” (2006). P. 54
 Siegl, Auskunftei-Informationen Zur Reduzierung Des Zahlungsausfallrisikos Im Online-Handel. P. 87
 M. Siegl, P. Raab, and S. Sckmann, “Vergleich von Auskunftei-Informationen Zur Bewertung Des Zahlungsausfallrisikos Im E-Commerce, Ausgewählte Ergebnisse Einer Empirisischen Untersuchung Im B-to-C-Segment,” in Durch Nachhaltigkeit Geprägtes Credit Management (Goch, 2010), 1–20.
 Siegl, Auskunftei-Informationen Zur Reduzierung Des Zahlungsausfallrisikos Im Online-Handel. P. 89
 Ibid. P. 89
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