Consumer Loan Credit Risk Analyser Using Neural Networks

Master's Thesis from the year 2007 in the subject Computer Sciences - Artificial Intelligence language: English abstract: Imagine a world where loan defaults are predicted with uncanny accuracy safeguarding financial institutions and empowering responsible lending. This book delves into the cutting-edge application of artificial neural networks to revolutionize consumer loan credit risk assessment. Embark on a journey through the intricate landscape of machine learning exploring the biological inspiration behind neural networks and their evolution into powerful predictive tools. This comprehensive work meticulously compares the performance of two prominent neural network architectures: feed-forward backpropagation and radial basis function networks against traditional statistical methods offering a balanced perspective on their strengths and limitations. Discover how these sophisticated algorithms are implemented using MATLAB's Neural Network Toolbox to construct a robust credit risk analysis system. Uncover the secrets of data preprocessing network training and performance evaluation gaining invaluable insights into the practical aspects of building a real-world risk prediction model. This book provides a rigorous performance analysis offering a statistical method for credit risk analysis and the experimental method using neural networks. Whether you're a seasoned data scientist a finance professional or an academic researcher this book provides a holistic understanding of consumer loan credit risk neural networks feed-forward backpropagation radial basis function networks and machine learning techniques transforming the financial sector. Explore the future scope of these innovative technologies uncovering the vast potential for applications in diverse domains beyond credit risk. Prepare to be captivated by the potential to reshape the future of finance through the power of intelligent algorithms and data-driven decision-making. This exploration