Automatic analysis of insurance reports through deep neural networks to identify severe claims
نویسندگان
چکیده
Abstract In this paper, we develop a methodology to automatically classify claims using the information contained in text reports (redacted at their opening). From automatic analysis, aim is predict if claim expected be particularly severe or not. The difficulty rarity of such extreme database, and hence difficulty, for classical prediction techniques like logistic regression accurately outcome. Since data unbalanced (too few observations are associated with positive label), propose different rebalance algorithm deal issue. We discuss use embedding methodologies used process data, role architectures networks.
منابع مشابه
rodbar dam slope stability analysis using neural networks
در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...
Automatic Tagging Using Deep Convolutional Neural Networks
We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and subsampling layers only. In the experiments, we measure the AUC-ROC scores of the architectures with different complexities and input types using the MagnaTagATune dataset, where a 4-layer architecture shows ...
متن کاملUsing Neural Networks to Identify
A neural network method for identifying the ancestor of a hadron jet is presented. The idea is to nd an eecient mapping between certain observed hadronic kinematical variables and the quark/gluon identity. This is done with a neuronic expansion in terms of a network of sigmoidal functions using a gradient descent procedure, where the errors are back-propagated through the network. With this met...
متن کاملEvaluation and comparison performance of deep neural networks FCN and RDRCNN in order to identify and extract urban road using images of Sentinel-2 with medium spatial resolution
Road extraction using remote sensing images has been one of the most interesting topics for researchers in recent years. Recently, the development of deep neural networks (DNNs) in the field of semantic segmentation has become one of the important methods of Road extraction. In the Meanwhile The majority of research in the field of road extraction using DNN in urban and non-urban areas has been...
متن کاملUnderstanding Neural Networks Through Deep Visualization
Recent years have produced great advances in training large, deep neural networks (DNNs), including notable successes in training convolutional neural networks (convnets) to recognize natural images. However, our understanding of how these models work, especially what computations they perform at intermediate layers, has lagged behind. Progress in the field will be further accelerated by the de...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Annals of Actuarial Science
سال: 2021
ISSN: ['1748-5002', '1748-4995']
DOI: https://doi.org/10.1017/s174849952100004x