Artikel
Deeptriangle: A deep learning approach to loss reserving
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 7 ; Year: 2019 ; Issue: 3 ; Pages: 1-12 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
loss reserving
machine learning
neural networks
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Kuo, Kevin
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2019
- DOI
-
doi:10.3390/risks7030097
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Kuo, Kevin
- MDPI
Entstanden
- 2019
Ähnliche Objekte (12)
Loss reserving models: Granular and machine learning forms
Analysis of the financial information contained in the texts of current reports: A deep learning approach
Individual loss reserving using a gradient boosting-based approach
Contracts for difference: A reinforcement learning approach
Lack of global convergence and the formation of multiple welfare clubs across countries: An unsupervised machine learning approach
Idiosyncratic viral loss theory: Systemic operational losses in banks
The two defaults scenario for stressing credit portfolio loss distributions
Bayesian adjustment for insurance misrepresentation in heavy-tailed loss regression
Deep arbitrage-free learning in a generalized HJM framework via arbitrage-regularization
Trend prediction classification for high frequency Bitcoin time series with deep learning
Interest differences and organizational learning
Learning (not) to evade taxes
Loss reserving models: Granular and machine learning forms
Analysis of the financial information contained in the texts of current reports: A deep learning approach
Individual loss reserving using a gradient boosting-based approach
Contracts for difference: A reinforcement learning approach
Lack of global convergence and the formation of multiple welfare clubs across countries: An unsupervised machine learning approach
Idiosyncratic viral loss theory: Systemic operational losses in banks
The two defaults scenario for stressing credit portfolio loss distributions
Bayesian adjustment for insurance misrepresentation in heavy-tailed loss regression
Deep arbitrage-free learning in a generalized HJM framework via arbitrage-regularization
Trend prediction classification for high frequency Bitcoin time series with deep learning
Interest differences and organizational learning
Learning (not) to evade taxes
Loss reserving models: Granular and machine learning forms
Analysis of the financial information contained in the texts of current reports: A deep learning approach
Individual loss reserving using a gradient boosting-based approach
Contracts for difference: A reinforcement learning approach
Lack of global convergence and the formation of multiple welfare clubs across countries: An unsupervised machine learning approach
Idiosyncratic viral loss theory: Systemic operational losses in banks
The two defaults scenario for stressing credit portfolio loss distributions
Bayesian adjustment for insurance misrepresentation in heavy-tailed loss regression
Deep arbitrage-free learning in a generalized HJM framework via arbitrage-regularization
Trend prediction classification for high frequency Bitcoin time series with deep learning
Interest differences and organizational learning