arXiv:1804.09253
공식 코드 · Papers with Code
DeepTriangle: A Deep Learning Approach to Loss Reserving
원문 초록
We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs. We validate the models on loss reserving data across lines of business, and show that they improve on the predictive accuracy of existing stochastic methods. The models require minimal feature engineering and expert input, and can be automated to produce forecasts more frequently than manual workflows.
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The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
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Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical Imaging
CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data
Enhancing Abstractive Summarization of Scientific Papers Using Structure Information
GSDFuse: Capturing Cognitive Inconsistencies from Multi-Dimensional Weak Signals in Social Media Steganalysis
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