Creditworthiness of private persons. An approach using social network sites E-Book

Jochen Schweizer

• Herausgeber: GRIN Verlag
• Sprache: Englisch
• Veröffentlichungsjahr: 2016

Beschreibung

Bachelor Thesis from the year 2014 in the subject Business economics - Offline Marketing and Online Marketing, grade: 2,0, Rhine-Waal University of Applied Sciences, language: English, abstract: 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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"[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[1]

Table of Contents

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

3.3.1 Likes

3.3.2 Pictures

3.3.3 Places

3.3.4 Groups

3.3.5 Friends

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

Volume - Scale of Data

Velocity - Analysis of streaming Data

Data Variety-Different forms of Data

Veracity and Accessibility - uncertainty of 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

4.4.3 Problems

4.4.3.1 Quality of Data

4.4.3.2 Terms & Conditions

4.4.3.3 Data Availability

5 Facebook profiles of private insolvent persons as an attempt

5.1 Attempt description

5.2 Result of the Attempt

6. Conclusion

7. Bibliography

List of figures

Appendix

A.1 Rating Agencies Rating Guide

A.2The four V's of Big Data

A.3 Attempt Details

List of abbreviations

1. Introduction and Relevance within the economy

On 31 December 2013, 757 million users logged on to Facebook.[2] 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.[3] 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[4] of private persons becomes more important, due to the increasing distance trade.[5]The different trade partners usually don’t know each other. That leads to an information asymmetry[6] 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[7].

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[8], but also the available data on social networks, makes it an interesting source of information about a private person's financial situation.

Figure 1 Private Insolvent Persons in Germany from 2000 to 2013[9][10]

Since there is a plurality of available information about private persons on social network sites[11], 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[12]. 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.