This master thesis investigates the influencing factors on consumers’ willingness to share their personal and financial information through open-banking technology in different financing and lending scenarios (e-commerce, car financing, and mortgage). To achieve this, a survey-based empirical study was conducted, covering a variety of questions regarding demographic factors as well as measured preferences and stances along the dimensions of tech-savviness, open-banking knowledge, privacy concerns and financial literacy. The analysis of over 143 survey responses shows how these factors influence and explain, to what extent and under which conditions a consumer is willing to let a company digitally take a direct look into his bank account. The gained results and insights provide a basis to define best practices and use cases for scenarios in which open-banking technology can add value to all parties involved.
Since January 12th, 2016, the EU's second payment services directive (PSD2) has been in force. A key point within the PSD2 is the obligation of banks to make the information of their customers and their associated bank accounts available to third parties via standardized interfaces. A concept that is widely referred to as open-banking. One major field of application of open-banking technology lies within consumer credit application processes. The main idea is that lenders (financial institutions as well as non-financial / retail companies) can get access to the information contained in bank accounts, such as transaction history and balances, by explicit consent of the consumer. The purpose of this is to make more informed decisions as to whether or not to extend credit to the specific person in scope.
Table of Contents
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
Objectives & Scope
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.
- Analysis of consumer willingness to share bank data for credit assessments.
- Empirical evaluation of demographic and psychographic influencing factors.
- Investigation of diverse lending scenarios (e-commerce, car finance, mortgage).
- Assessment of the impact of transparency and perceived benefits on consumer trust.
- Formulation of recommendations to enhance open-banking adoption in consumer lending.
Excerpt from the book
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).
Summary of Chapters
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.
Keywords
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.
Frequently Asked Questions
What is the fundamental purpose of this thesis?
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.
What are the central themes discussed in the work?
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.
What is the primary research goal or question?
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?"
Which scientific methodology is employed?
The author conducted an empirical, survey-based quantitative study, utilizing descriptive statistics and multiple ordinary least squares (OLS) regression analyses processed via Python.
What is covered in the main section of the paper?
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.
Which are the key identifiers for this research?
The research is characterized by keywords such as open-banking, credit lending, risk management, PSD2, and FinTech, among others.
How did the author handle different consumer segments in the survey?
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.
What was the conclusion regarding the age of participants and their acceptance levels?
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.
Did the survey find that explicit benefits impact consumer willingness?
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.
Why was the hypothesis regarding financial literacy rejected?
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.
- Arbeit zitieren
- Thomas Nöding (Autor:in), 2022, Acceptance of Open-Banking Technology in Consumer Lending Applications, München, GRIN Verlag, https://www.grin.com/document/1353583
