Evaluating Bank-Firm Credit Scoring in SME Lending Empirically

Chapter ONE

INTRODUCTION

• 1.1Introduction Topic-specific overview of bank-firm credit scoring in SME lending and its empirical relevance
• 1.2Background of the Study Historical development of credit scoring in SME finance and evolving bank risk practices
• 1.3Statement of the Problem Identifying gaps in predictive accuracy, data quality, and external validity of SME credit scoring models
• 1.4Aim and Objectives of the Study To evaluate empirical performance of bank-firm credit scoring mechanisms in SME lending and identify drivers of predictive success
• 1.5Research Questions What is the predictive accuracy of bank credit scoring for SMEs? which variables most influence SME default risk? how do scoring models perform across sectors and firm sizes?
• 1.6Research Hypotheses Hypothesis 1: Bank credit scoring models significantly predict SME default better than baseline models; Hypothesis 2: Macro and firm-specific variables differentially impact scoring accuracy across sectors; Hypothesis 3: Data quality and discontinuities reduce predictive validity of scoring models
• 1.7Significance of the Study Advancing SME credit risk assessment, informing bank lending policies, and contributing to theory on data-driven credit decisions
• 1.8Scope and Delimitation of the Study Empirical evaluation using a multi-bank SME lending dataset from a defined country over a five-year window, with sectoral stratification
• 1.9Limitations of the Study Potential sample bias, data access constraints, and model generalizability limitations across jurisdictions
• 1.10Organisation of the Study Outline of chapters and their logical progression from theory to empirical findings
• 1.11Operational Definition of Terms Definitions of key terms: credit scoring, SME, default, non-performing loan, model validation, predictive accuracy

Chapter TWO

LITERATURE REVIEW

• 2.1Conceptual Review: Credit Scoring in SME Lending and Bank Risk Management Concepts of scoring systems, credit cycles, and SME credit risk norms
• 2.2Conceptual Review: Data Inputs and Feature Engineering for SME Scoring Variables from financial statements, bank data, and alternative data sources
• 2.3Conceptual Review: Model Types in Credit Scoring for SMEs Logistic regression, machine learning classifiers, and hybrid ensembles
• 2.4Conceptual Review: ESG and Non-Financial Factors in SME Credit Scoring Incorporating governance, credit history, and relational data
• 2.5Theoretical Framework: Theory of Signal Detection in Credit Decisions Implications for threshold setting and decision errors
• 2.6Theoretical Framework: Prospect Theory in Banking Risk-Taking Behavioral biases affecting credit scoring outcomes
• 2.7Theoretical Framework: Information Asymmetry and Screening Models Mechanisms by which banks differentiate between high- and low-risk borrowers
• 2.8Empirical Review: Prior Studies on Bank-Firm Credit Scoring for SMEs Summary of methodologies and findings across contexts
• 2.9Empirical Review: Data Quality and Model Validation in SME Credit Scoring Impact of data completeness, linkage, and out-of-sample performance
• 2.10Empirical Review: Sectoral and Regional Variation in SME Default Risk How industry dynamics influence scoring performance
• 2.11Identified Gaps in the Literature Limited cross-bank comparative analyses, insufficient external validity, and under-explored non-traditional data sources
• 2.12Conceptual Model / Summary of the Review Diagrammatic representation linking inputs, models, and outcomes in SME credit scoring

Chapter THREE

RESEARCH METHODOLOGY

• 3.1Research Design Empirical field study employing comparative model evaluation across banks and SME segments
• 3.2Philosophical Paradigm Pragmatic epistemology aligning methodological pluralism with practical relevance
• 3.3Population of the Study All SMEs and corresponding loan applications within participating banks during the study period
• 3.4Sample Size and Sampling Technique Stratified random sampling by sector and firm size; power analysis to determine robust sample size
• 3.5Sources and Instruments of Data Collection Bank lending records, credit scoring outputs, financial statements, and macroeconomic indicators
• 3.6Validity and Reliability of Instruments Triangulation, back-testing, and calibration of scoring models; inter-rater reliability where expert judgment is used
• 3.7Data Management and Ethical Considerations Data anonymization, consent where applicable, and compliance with regulatory requirements
• 3.8Model Specification or Analytical Framework Specification of benchmark and alternative scoring models; performance metrics and model comparison framework
• 3.9Data Preparation and Feature Engineering Handling missing data, normalization, and creation of composite indicators
• 3.10Statistical Methods and Hypothesis Testing ROC-AUC, Brier score, calibration plots, and out-of-sample tests; regression analyses for explanatory factors
• 3.11Robustness Checks and Validity Tests Cross-validation, bootstrap significance, and sensitivity analyses to model assumptions
• 3.12Ethical Considerations in Reporting and Use of Findings Responsible disclosure of risk, implications for borrowers, and policy relevance

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

• ANALYSIS AND DISCUSSION OF FINDINGS
• 4.1Data Presentation Overview Structure of the dataset, descriptive statistics, and initial data diagnostics
• 4.2Descriptive Analysis of SME Sample Firm size, sector distribution, financial health indicators, and loan characteristics
• 4.3Descriptive Analysis of Scoring Outputs Score distributions, threshold settings, and model deployment footprints
• 4.4Hypotheses Testing: Predictive Accuracy Comparison of scoring models vs. baseline benchmarks using ROC-AUC and Brier scores
• 4.5Hypotheses Testing: Calibration and Discrimination Calibration curves and discrimination metrics across sectors and firm sizes
• 4.6Hypotheses Testing: Variable Significance and Feature Importance Influence of financial ratios, leverage, liquidity, and macro indicators
• 4.7Interpretation of Results: Model Performance Across Banks and Segments Cross-bank variability and sectoral patterns in predictive power
• 4.8Discussion of Findings in Relation to Literature Concordance and deviations from prior studies and theoretical expectations

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

• CONCLUSION AND RECOMMENDATIONS
• 5.1Summary of Findings Concise synthesis of empirical results and their implications for SME credit scoring
• 5.2Conclusions Inferences about the effectiveness and limitations of bank-firm credit scoring in SME lending
• 5.3Contribution to Knowledge Theoretical and practical contributions to credit risk assessment and lending policies
• 5.4Recommendations for Practice Guidance for banks on model selection, data strategy, and threshold tuning
• 5.5Policy and Regulatory Implications Implications for credit reporting, transparency, and regulatory risk management
• 5.6Suggestions for Further Studies Potential avenues for extending the research, including longer horizons and additional data sources