Abstract

This research presents a comprehensive framework for optimizing lending risk analysis and management using advanced machine learning techniques, Big Data, and cloud computing. Peer-to-peer (P2P) lending platforms, such as Lending Club, have revolutionized the financial industry by directly connecting borrowers with investors. However, this innovative approach also introduces significant challenges in credit risk assessment due to the high volume of loan applications and the complexity of evaluating borrower creditworthiness. The proposed framework addresses several critical stages in the machine learning pipeline: data preprocessing, feature engineering, model development, evaluation, and deployment. Data preprocessing involves cleaning and preparing the data to ensure accuracy and reliability, including handling missing values, encoding categorical variables, and normalizing continuous variables. Feature engineering focuses on creating and selecting significant features based on domain knowledge and their relevance to lending risk. The results of our study demonstrate significant improvements in predictive performance compared to traditional credit risk assessment methods, highlighting the potential of machine learning, Big Data, and cloud computing to enhance financial decision-making processes. The implementation of such advanced models can lead to better risk management, improved investor confidence, and a more efficient lending process, ultimately benefiting both borrowers and investors in the P2P lending ecosystem. This research underscores the transformative power of these technologies in the financial sector and provides a robust framework for future developments in credit risk management, while also offering insights into the social sciences of planning and development by promoting equitable access to financial services and fostering economic growth