Risk of credit and lending in an artificial adaptive banking system
Table Of Contents
Chapter ONE
INTRODUCTION
- 1.1Introduction
- 1.2Background of Study
- 1.3Problem Statement
- 1.4Objective of Study
- 1.5Limitation of Study
- 1.6Scope of Study
- 1.7Significance of Study
- 1.8Structure of the Research
- 1.9Definition of Terms
Chapter TWO
LITERATURE REVIEW
- 2.1Overview of Credit and Lending
- 2.2History of Banking Systems
- 2.3Types of Artificial Intelligence
- 2.4Applications of AI in Banking
- 2.5Risks in Credit and Lending
- 2.6Regulations in Banking
- 2.7Credit Scoring Models
- 2.8Machine Learning in Finance
- 2.9Challenges in Implementing AI in Banking
- 2.10Future Trends in AI and Banking
Chapter THREE
RESEARCH METHODOLOGY
- 3.1Research Methodology Overview
- 3.2Research Design
- 3.3Data Collection Methods
- 3.4Sampling Techniques
- 3.5Data Analysis Procedures
- 3.6Ethical Considerations
- 3.7Validity and Reliability
- 3.8Limitations of the Methodology
Chapter FOUR
DATA PRESENTATION AND ANALYSIS
- 4.1Analysis of Credit and Lending Risks
- 4.2Impact of AI on Risk Management
- 4.3Case Studies in Banking Sector
- 4.4Comparison with Traditional Banking
- 4.5Customer Perception of AI in Banking
- 4.6Financial Performance Metrics
- 4.7Recommendations for Improvement
- 4.8Future Research Directions
Chapter FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
- 5.1Conclusion and Summary
- 5.2Summary of Findings
- 5.3Implications for Banking Industry
- 5.4Contributions to Knowledge
- 5.5Recommendations for Practice
- 5.6Areas for Future Research
Thesis Abstract
We present a simulation tool we have developed to build artificial banking systems and to study the interaction among banks and firms under various conditions. In this application, we consider a banking system composed of artificial adaptive banks, which have to make decisions about the opportunity to lend money to prospective borrowers. Such borrowers are risky firms whose value evolve stochastically over time according to an heterogeneous (across firms), time-varying probability. Banks decide whether to give out loans or not on the basis of an information set which is partly firm-specific and partly of a macroeconomic nature. The evaluation of such information set takes place by means of neural networks which learn over time to distinguish among good and bad borrowers. We consider the model as a useful simulation instrument to analyze the dynamic evolution of an economy where some of the variables are not common knowledge. The results show that these learning techniques are effective and that banks learn to discriminate among borrowers. Moreover we can see that this simulation tool allows to study not only the effects of general macroeconomic conditions on such learning, but also the interactions among artificial agents and their behavior under different initial assumptions.
Thesis Overview
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