DeepAI
Log In Sign Up

DeepTriangle: A Deep Learning Approach to Loss Reserving

04/24/2018
by   Kevin Kuo, et al.
RStudio, Inc.
0

We propose a novel approach for loss reserving based on deep neural networks. The approach allows for jointly modeling of paid losses and claims outstanding, and incorporation of heterogenous inputs. We validate the models on loss reserving data across lines of business, and show that they attain or exceed the predictive accuracy of existing stochastic methods. The models require minimal feature engineering and expert input, and can be automated to produce forecasts at a high frequency.

READ FULL TEXT VIEW PDF
08/29/2019

A Robust Image Watermarking System Based on Deep Neural Networks

Digital image watermarking is the process of embedding and extracting wa...
05/04/2022

A deep domain decomposition method based on Fourier features

In this paper we present a Fourier feature based deep domain decompositi...
09/06/2021

Using Proxies to Improve Forecast Evaluation

Comparative evaluation of forecasts of statistical functionals relies on...
01/31/2022

Deep Learning Macroeconomics

Limited datasets and complex nonlinear relationships are among the chall...
09/25/2021

Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?

The acute respiratory distress syndrome (ARDS) is a severe form of hypox...
06/10/2020

Hybrid Tree-based Models for Insurance Claims

Two-part models and Tweedie generalized linear models (GLMs) have been u...

Code Repositories

deeptriangle

DeepTriangle: A Deep Learning Approach to Loss Reserving


view repo