This paper tackles corporate fraud detection using real-world Chinese stock market data. It highlights challenges like information overload and hidden fraud. The proposed KeGCNR model enhances detection with knowledge graph embeddings and robust training. Experiments show superior performance. Future research should address class imbalance and IND noise. Public datasets are provided.
This research evaluates different regression models to predict #flood-induced #insuranceclaims, using the #us #national #floodinsurance Program (#nfip) dataset from 2000 to 2020. The models studied include #neuralnetworks (Conditional Generative Adversarial Networks), #decisiontrees (Extreme Gradient Boosting), and #kernel-based regressors (#gaussian Process). The study identifies key predictors for regression, highlighting factors that influence flood-related financial damages.
"... we develop a framework based on a combination of a neural network together with a dimensionality reduction technique t-SNE (t-distributed stochastic neighbour embedding)... The obtained results, which are based on real insurance data, reveal a clear contrast between the high and low risk policy holders, and indeed improve upon the actual risk estimation performed by the insurer."
" The aim is to come up with a convex risk functional that incorporates a sefety margin with respect to nonparametric uncertainty and still can be approximated through parametrized models. The particular form of the parametrization allows us to develop a numerical method, based on neural networks, which gives both the value of the risk functional and the optimal perturbation of the reference measure."
"... blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities."
"Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions."
"Neural networks are suggested for learning a map from d' dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions."