Titlebook: Machine Learning and Principles and Practice of Knowledge Discovery in Databases; International Worksh Irena Koprinska,Paolo Mignone,Sepide

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Privacy-Preserving Machine Learning in?Life Insurance Risk Prediction One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption

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Financial Distress Model Prediction Using Machine Learning: A Case Study on?Indonesia’s Consumers Cyal distress affects the sustainability of a company’s operations and undermines the rights and interests of its stakeholders, also harming the national economy and society. Therefore, we developed an accurate predictive model for financial distress. Using 17 financial attributes obtained from the fi

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Towards Explainable Occupational Fraud Detectionenable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable art

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Towards Data-Driven Volatility Modeling with?Variational Autoencodersautonomously learns concepts such as the volatility level, smile, and term structure without leaning on hypotheses from traditional volatility modeling techniques. In addition to introducing notable improvements to an existing variational autoencoder approach for the reconstruction of both complete

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