Project Abstract

Abstract
Artificial Intelligence (AI) has emerged as a transformative technology in various industries, and its application in credit scoring for improved risk assessment in banking has gained significant attention. This research focuses on exploring the potential benefits of integrating AI algorithms in credit scoring processes to enhance the accuracy and efficiency of risk assessment in the banking sector. The study aims to investigate how AI techniques such as machine learning and deep learning can be leveraged to analyze vast amounts of data and predict creditworthiness more effectively than traditional methods. The research begins with a comprehensive review of the background of credit scoring in banking, highlighting the limitations and challenges faced by conventional models in accurately assessing credit risk. By incorporating AI technologies, this study seeks to address these limitations and enhance the predictive power of credit scoring models. The research objectives include assessing the impact of AI algorithms on credit risk assessment, identifying the key factors influencing credit scoring outcomes, and evaluating the performance of AI-based models compared to traditional methods. Methodologically, the research employs a mixed-method approach, combining quantitative analysis of historical credit data with qualitative interviews with banking experts to gain insights into the practical implications of AI adoption in credit scoring. The study methodology involves data collection, preprocessing, model development, and performance evaluation using appropriate metrics to measure the accuracy and reliability of AI-based credit scoring models. The findings of the research highlight the significant improvements in risk assessment accuracy achieved through the application of AI in credit scoring. The AI models demonstrate superior predictive capabilities, capturing complex patterns and relationships in credit data that traditional models may overlook. Moreover, the research identifies the key factors driving credit risk and provides valuable insights for banks to enhance their credit decision-making processes. In conclusion, the research underscores the potential of AI technologies to revolutionize credit scoring in banking, leading to more informed and reliable risk assessments. The study contributes to the growing body of knowledge on the integration of AI in financial services and provides practical recommendations for banks seeking to enhance their credit risk management practices. Overall, the findings of this research support the view that AI-driven credit scoring can significantly improve risk assessment outcomes and drive better decision-making in the banking sector.

Project Overview