3 research outputs found

    Credit Risk Prediction Using Explainable AI

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    Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications

    Deep Learning for Enterprise Decision-Making: A Comprehensive Study in Stock Market Analytics

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    This study explores the transformative impact of deep learning, specifically Convolutional Neural Networks (CNNs), on organizational decision-making in the stock market. Utilizing CNN architectures like VGG16, ResNet50, and InceptionV3, the research emphasizes the significance of leveraging deep learning for improved business intelligence and management. It highlights the superiority of CNN models over traditional algorithms, with VGG16 achieving an accuracy rate of 90.45%. The study underscores the potential of deep learning in extracting valuable insights from complex data, leading to a shift in optimizing organizational processes. Additionally, it stresses the importance of investing in infrastructure and expertise for successful CNN integration, alongside addressing ethical and privacy concerns. Through a dive into real-time mathematical concepts, the study provides insights into CNN functionality and offers comparisons between different architectures, aiding in specialized applications such as stock market trends

    Revolutionizing Banking Decision-Making: A Deep Learning Approach to Predicting Customer Behavior

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    This article explores a machine learning approach focused on predicting bank customer behavior, emphasizing deep learning methods. Various architectures, including CNNs like VGG16, ResNet50, and InceptionV3, are compared with traditional algorithms such as Random Forest and SVM. Results show deep learning models, particularly ResNet50, outperform traditional ones, with an accuracy of 86.66%. A structured methodology ensures ethical data use. Investing in infrastructure and expertise is crucial for successful deep learning integration, offering a competitive edge in banking decision-making