Thesis Abstract

Abstract
The banking sector plays a crucial role in the global economy by providing financial services to individuals and businesses. One of the key challenges faced by banks is managing credit risk effectively to maintain financial stability and profitability. In recent years, advancements in technology and data analytics have paved the way for the adoption of predictive analytics in credit risk assessment. This thesis aims to explore the application of predictive analytics in assessing credit risk in the banking sector. The introduction chapter provides an overview of the research topic, highlighting the significance of predictive analytics in enhancing credit risk assessment processes. The background of the study delves into the evolution of credit risk management practices in banking and the emergence of predictive analytics as a powerful tool in this domain. The problem statement identifies the gaps in traditional credit risk assessment methods and sets the stage for the research objectives. The objectives of the study are to investigate the effectiveness of predictive analytics in credit risk assessment, identify the key factors influencing credit risk in banking, and develop a predictive model for assessing credit risk. The limitations of the study are also outlined, acknowledging constraints such as data availability, model complexity, and the dynamic nature of credit risk. The literature review chapter explores existing research on predictive analytics, credit risk assessment, and their application in the banking sector. Key themes include data sources for credit risk assessment, predictive modeling techniques, and the impact of predictive analytics on decision-making in banking institutions. This chapter sets the foundation for the research methodology, guiding the selection of appropriate data sources, variables, and modeling techniques. The research methodology chapter outlines the research design, data collection methods, and analytical techniques employed in the study. The data sources include historical loan data, economic indicators, and demographic information, which are used to train and validate the predictive model. The methodology also covers data preprocessing, model development, and performance evaluation metrics. The findings chapter presents the results of the predictive analytics model in credit risk assessment. Key findings include the identification of significant risk factors, the predictive accuracy of the model, and the impact of predictive analytics on credit risk assessment outcomes. The discussion chapter interprets the findings in the context of existing literature, highlighting the implications for banking practices and future research directions. In conclusion, this thesis demonstrates the potential of predictive analytics in enhancing credit risk assessment in the banking sector. The study contributes to the growing body of knowledge on the application of data analytics in financial risk management and provides practical insights for banks looking to leverage predictive analytics for credit risk assessment. Overall, the findings underscore the importance of integrating predictive analytics into credit risk management practices to improve decision-making and mitigate risks effectively.

Thesis Overview

The project titled "Predictive analytics for credit risk assessment in banking" aims to explore the application of predictive analytics in enhancing the credit risk assessment process within the banking sector. Credit risk assessment is a critical function in banking, as it involves evaluating the creditworthiness of borrowers to determine the likelihood of default on loan payments. Traditional credit risk assessment methods rely on historical data, financial ratios, and credit scores to make lending decisions. However, these methods may not always provide accurate predictions, especially in complex and dynamic financial environments. Predictive analytics, which involves the use of statistical algorithms and machine learning techniques to analyze data and make predictions, offers a more advanced and sophisticated approach to credit risk assessment. By leveraging predictive analytics, banks can harness the power of big data to improve the accuracy and efficiency of their credit risk assessment processes. This project seeks to investigate how predictive analytics can be effectively applied to identify and mitigate credit risks in banking operations. The research will begin by providing an introduction to the topic, highlighting the significance of credit risk assessment in banking and the potential benefits of predictive analytics in this context. The background of the study will explore the evolution of credit risk assessment methods and the challenges faced by traditional approaches. The problem statement will outline the gaps and limitations in current credit risk assessment practices that justify the need for a more advanced solution like predictive analytics. The objectives of the study will be clearly defined to establish the specific goals and outcomes expected from the research. The limitations of the study will also be acknowledged to provide a realistic assessment of the scope and constraints of the project. The scope of the study will delineate the boundaries within which the research will be conducted, specifying the target audience, data sources, and analytical techniques to be employed. The significance of the study will be emphasized to highlight the potential impact of implementing predictive analytics for credit risk assessment in banking. By improving the accuracy of risk predictions and enhancing decision-making processes, banks can reduce loan defaults, optimize capital allocation, and enhance overall financial performance. The structure of the thesis will be outlined to provide a roadmap of the research framework, including the chapters, sections, and key components that will be covered. In the subsequent chapters, a comprehensive literature review will be conducted to examine existing research and industry practices related to predictive analytics and credit risk assessment in banking. The research methodology will detail the data collection methods, analytical tools, and research design that will be employed to achieve the research objectives. The discussion of findings chapter will present the results of the data analysis and provide insights into the effectiveness of predictive analytics in credit risk assessment. Finally, the conclusion and summary chapter will synthesize the key findings, implications, and recommendations of the research. By exploring the potential of predictive analytics for credit risk assessment in banking, this project aims to contribute to the advancement of risk management practices in the financial sector.