Masterarbeit, 2022
92 Seiten, Note: 1,0
1 Introduction
1.1 Motivation
1.2 Objectives
1.3 Structure / Outline
2 Consumer Lending: An Overview
2.1 Historic evolution of consumer credit
2.2 Common credit assessment practices
2.2.1 Credit scoring
2.2.2 Affordability assessment
2.3 Information asymmetry and the role of credit bureaus
2.4 Socio-economic impact of consumer lending, financial inclusion, and social debates around credit scoring
2.5 Recent developments in consumer lending, alternative data and fintech
2.5.1 Digitization and increasing expectations toward the user experience
2.5.2 Digital footprints and alternative data
2.5.3 FinTech
3 Open-Banking
3.1 The second European payment services directive and an introduction to open-banking
3.2 Exemplary open-banking user-flow for authentication
3.3 Application of open-banking in consumer lending and its benefits
3.4 Data privacy issues around open-banking and the status quo to its application
4 Acceptance of Open-Banking Technology in Consumer Lending Applications
4.1 Problem description and research question
4.2 Review of similar literature to the subject matter
4.3 Derivation of research hypotheses
4.4 Approach of the study
4.4.1 Survey design and structure
4.4.2 Sampling
4.4.3 Approach of analysis
4.5 Analysis results
4.5.1 Descriptive statistics
4.5.2 Testing for Cronbach’s alpha
4.5.3 Inferential analysis and regression results
4.6 Hypothesis testing
4.7 Further analysis
4.7.1 OLS regression on single likert items
4.7.2 OLS regression on demographic and other categorical questions
4.7.3 T-test on offered benefits
5 Conclusion and Outlook
This master thesis investigates the factors influencing consumers' willingness to share personal financial information via open-banking technology in various credit lending scenarios, such as e-commerce, car financing, and mortgages. Using a survey-based empirical analysis, the research evaluates how variables like tech-savviness, open-banking knowledge, privacy concerns, and financial literacy impact consumer acceptance, aiming to derive best practices for implementing open-banking in credit applications.
2.2.1 Credit scoring
In credit scoring, data and information that can be gathered from the credit applicant are analyzed to identify their correlation with payment defaults. This is typically done by analyzing a large dataset from past credit arrangements and their performance concerning their repayment. Each applicant data variable (e.g. employment status) and their corresponding attributes (e.g. unemployed, part-time employment, etc.) are analyzed with regard to their predictive quality for payment defaults. Only those variables or their combinations, which yield the highest correlation to payment default, are later put into a scorecard. The scorecard assigns specific point scores to the different variable attributes, which are commonly derived from their degree of predictiveness. These point scores are then added up throughout the further process to produce a final overall score. The overall score corresponds to a statistical probability of default, often expressed by a percentage value. The creditor can then use this value to make a credit decision based on their specific risk appetite (Thomas, Crook and Edelman (2017), p.2). Figure 2.1 shows an example of items that may be included in a scorecard.
A prominent and widely used way of making credit decisions, is the definition of cut-off values that define score ranges to sort applications into the categories ‘accept’, ‘refer’ or ‘reject’. Applications that fall into the ‘refer’ range are associated with a degree of risk or probability of default, that the creditor neither wants to directly accept nor reject. Rather they are passed on to trained credit analysts that manually evaluate the client application or even contact the applicant in order to gather additional information that will help make a final decision (e.g. providing bank statements or salary slips). The ultimate goal of credit scoring systems is to minimize type 1 and type 2 errors. In the context of credit decisions, a type 1 error would mean accepting a borrower that later defaults his payments. A type 2 error stands for rejecting an applicant, even though he would have paid without any problems (Capon (1982), p.83).
1 Introduction: Provides an overview of the thesis, clarifying the motivation, objectives, and the structured outline of the research regarding open-banking in credit lending.
2 Consumer Lending: An Overview: Offers a comprehensive history and analysis of consumer credit practices, including credit scoring, affordability assessments, and the socio-economic context of FinTech and modern alternative data.
3 Open-Banking: Explains the regulatory background of PSD2, describes typical user authentication flows in open-banking, and discusses the benefits and privacy considerations for credit applications.
4 Acceptance of Open-Banking Technology in Consumer Lending Applications: Details the empirical methodology, the experimental survey design, and provides the systematic analysis of factors influencing consumer acceptance.
5 Conclusion and Outlook: Summarizes the study's findings, acknowledges the limitations of the sample size, and provides strategic recommendations for financial institutions to improve market adoption.
open-banking, credit lending, risk management, PSD2, Account Information Services, credit application process, risk assessment, credit decisioning, FinTech, financial inclusion, consumer behavior, privacy concerns, survey analysis, affordability assessment, digital transformation.
The thesis explores the factors that influence whether consumers are willing to share their bank account data through open-banking technology specifically within consumer lending applications.
The main themes include the evolution of consumer credit, the transformation of credit assessment techniques through technology, the regulatory impact of PSD2, consumer privacy concerns, and the role of FinTech in personal finance.
The primary research question is: "What are the influencing factors that define consumer’s willingness to share data through open-banking services in credit application processes?"
The author conducted an empirical, survey-based quantitative study, utilizing descriptive statistics and multiple ordinary least squares (OLS) regression analyses processed via Python.
The main section covers the theoretical foundations of credit assessment, the technical and regulatory framework of open-banking, and the empirical study designed to test five specific research hypotheses concerning consumer acceptance.
The research is characterized by keywords such as open-banking, credit lending, risk management, PSD2, and FinTech, among others.
The survey randomly divided participants into two groups with varying levels of preliminary information about open-banking security to test if increased transparency positively influences consumer trust and acceptance.
The regression results showed that acceptance of open-banking technology usage significantly decreases for consumers aged 50 and older compared to the younger reference age group.
Yes, the data indicated that consumers are more willing to share their data for specific benefits, particularly valuing cost-saving potentials higher than simply increased speed or convenience during the application process.
Hypothesis H4 was rejected because the survey's measurement subset for financial literacy demonstrated poor internal consistency, as evidenced by a Cronbach’s alpha value of only 0.10, making it unreliable.
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