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.
Table of Contents
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
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
5. Facebook profiles of private insolvent persons as an attempt
5.1 Attempt description
5.2 Result of the Attempt
6. Conclusion
Objectives and Research Themes
The primary objective of this work is to evaluate the feasibility of utilizing data from social network sites, specifically Facebook, as a novel database for assessing the creditworthiness of private individuals. The research explores whether digital footprint data can effectively supplement or improve traditional credit scoring methods, especially in light of the rising number of private insolvencies and the limitations of current credit reporting systems.
- Analysis of the functioning and limitations of traditional Credit Reporting Agencies (CRAs).
- Review of technical foundations: Data Warehousing, Data Mining, and the implications of Big Data.
- Investigation of available attributes on social network sites and their potential correlation with financial reliability.
- Practical assessment: Case study of Facebook profiles belonging to private insolvent persons in Northrhine-Westphalia.
- Discussion of data quality, privacy settings, and legal frameworks (BDSG) regarding automated scoring.
Excerpt from the book
2.4 Credit Scoring
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.
Summary of Chapters
1. Introduction and Relevance within the economy: This chapter highlights the rising numbers of private insolvencies and introduces social network data as a potential future resource for assessing creditworthiness.
2. Theory about Credit Reporting Agencies and Scoring: This section explains the historical development, functioning, and criticism of traditional credit reporting agencies, alongside the foundational theories of credit scoring and associated legal regulations.
3. Social Network Data: This chapter defines social network sites and categorizes the types of available user data, discussing the potential for extracting information that could signal financial behavior.
4. Data Warehousing and Data Mining: This section describes the technical processes required to store and analyze large volumes of data, framing the information gathered from social networks within the context of Big Data applications.
5. Facebook profiles of private insolvent persons as an attempt: This chapter presents an empirical study that analyzes the Facebook profiles of a specific sample of private insolvent individuals to determine if relevant data can be extracted.
6. Conclusion: The final chapter summarizes the findings, confirming the hypothesis that social network data can serve as a supplementary source for credit evaluation, while stressing the critical importance of data quality.
Keywords
Credit Scoring, Credit Reporting Agencies, Social Network Sites, Data Warehousing, Data Mining, Big Data, Facebook, Creditworthiness, Private Insolvency, Information Asymmetry, Consumer Data, Data Quality, Fraud Detection, Geoscoring, BDSG
Frequently Asked Questions
What is the core focus of this research?
The work examines whether information derived from social network sites can be utilized to evaluate the creditworthiness of private individuals, acting as a supplement to traditional scoring methods.
Which fields of study are central to this work?
The research bridges financial economics, data science, and social media studies, focusing on how Big Data techniques can be applied to consumer credit risk management.
What is the primary research goal?
The goal is to determine if Facebook profiles contain observable attributes that can help identify a person's creditworthiness, thereby reducing information asymmetry for lenders.
What scientific methods are applied?
The paper uses literature analysis of credit systems, theoretical exploration of Big Data algorithms, and an empirical sample analysis of 110 private insolvent individuals.
What is covered in the main section of the book?
The main part details the mechanics of credit reporting, the technical requirements for processing social data (Data Warehousing/Mining), and empirical results from testing this approach on real-world insolvent profiles.
Which keywords best characterize this work?
Key terms include Credit Scoring, Big Data, Social Network Sites, Data Mining, and Creditworthiness.
What role does the "Social Risk Engine" play in the analysis?
The Social Risk Engine (SRE) is presented as an example of a technological implementation by Payolution to extract and process social media data for identity verification and risk assessment.
Why is data quality considered the most important factor?
Based on findings from Payolution and general industry data, the research concludes that outdated or inaccurate information from social networks renders scoring models ineffective and potentially harmful to both consumers and lenders.
- Quote paper
- Jochen Schweizer (Author), 2014, Creditworthiness of private persons. An approach using social network sites, Munich, GRIN Verlag, https://www.grin.com/document/316442
