Thesis Abstract

Abstract
This thesis investigates the application of predictive analytics in credit risk management for commercial banks. In recent years, the banking sector has been leveraging advanced data analytics techniques to enhance risk assessment processes. The study aims to explore the effectiveness of predictive analytics models in predicting credit risk and improving decision-making in commercial banking operations. The research begins with a comprehensive review of the existing literature on credit risk management, predictive analytics, and their integration in the banking industry. The literature review highlights the significance of predictive analytics in identifying potential credit defaults, improving loan approval processes, and mitigating financial risks for banks. The research methodology section outlines the research design, data collection methods, and analysis techniques employed in the study. Data will be gathered from commercial banks through surveys, interviews, and document analysis to understand current credit risk management practices and the adoption of predictive analytics tools. The findings reveal that predictive analytics models can significantly enhance credit risk assessment by providing more accurate predictions of borrower behavior and potential defaults. The study identifies key factors that influence credit risk, such as borrower credit history, income stability, debt-to-income ratio, and macroeconomic indicators. The discussion of the findings delves into the practical implications of integrating predictive analytics in credit risk management processes. It examines the challenges and opportunities faced by commercial banks in implementing predictive analytics models, including data quality issues, model interpretability, and regulatory compliance concerns. In conclusion, this research contributes to the existing body of knowledge on credit risk management by demonstrating the value of predictive analytics in improving decision-making processes for commercial banks. The study recommends strategies for banks to enhance their risk assessment capabilities through the adoption of advanced analytics tools and data-driven approaches. Overall, this thesis provides valuable insights into the potential of predictive analytics in credit risk management and offers practical recommendations for commercial banks to leverage these tools effectively in their operations.

Thesis Overview

The project titled "Predictive Analytics in Credit Risk Management for Commercial Banks" aims to explore the application of predictive analytics in enhancing credit risk management practices within the commercial banking sector. In recent years, advancements in data analytics and machine learning techniques have provided financial institutions with powerful tools to assess and mitigate credit risk more effectively. This research seeks to investigate how predictive analytics can be leveraged to improve decision-making processes related to credit risk assessment and monitoring in commercial banks. The research will begin with a comprehensive review of the existing literature on credit risk management, predictive analytics, and their intersection within the banking industry. By analyzing previous studies and industry reports, the project will identify key trends, challenges, and opportunities associated with the adoption of predictive analytics in credit risk management. Subsequently, the research methodology will be outlined, detailing the approach to data collection, analysis, and interpretation. The project will utilize both quantitative and qualitative research methods to gather insights from commercial banks and industry experts regarding their experiences with implementing predictive analytics in credit risk management. The core of the study will focus on the discussion of findings obtained through data analysis and case studies. By examining real-world applications of predictive analytics in credit risk management, the project aims to provide valuable insights into the benefits and limitations of these technologies for commercial banks. Additionally, the research will highlight best practices and strategies for effectively integrating predictive analytics into existing credit risk management frameworks. Finally, the project will conclude with a summary of key findings, implications for practice, and recommendations for future research. By shedding light on the potential of predictive analytics in credit risk management, this research endeavors to contribute to the ongoing dialogue surrounding innovation and risk management in the banking sector.