Creditworthiness of private persons. An approach using social network sites

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

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.