Predicting Motor Insurance Claims Using Telematics Data&amp;mdash;XGBoost vs. Logistic Regression

Pesantez-Narvaez, Jessica; Guillén, Montserrat; Alcañiz, Manuela

doi:10.20944/preprints201905.0122.v1

2019

DOI: 10.20944/preprints201905.0122.v1

|View full text |Cite

Preprint

Predicting Motor Insurance Claims Using Telematics Data—XGBoost vs. Logistic Regression

Jessica Pesantez-Narvaez¹,

Montserrat Guillén²,

Manuela Alcañiz³

Abstract: XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims vs. no claims can be used to identify the determinants of traffic accidents. We compare the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contains information from an insurance company about individuals’ driving patterns – including total an… Show more

Help me understand this report

View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

2023

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 24 publications

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

Martínez-Comesaña

Garrido²,

Granada

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

The Heat Loss Coefficient (HLC) characterizes the envelope efficiency of a building under in-use conditions, and it represents one of the main causes of the performance gap between the building design and its real operation. Accurate estimations of the HLC contribute to optimizing the energy consumption of a building. In this context, the application of black-box models in building energy analysis has been consolidated in recent years. The aim of this paper is to estimate the HLC of an existing building through the prediction of building thermal demands using a methodology based on Machine Learning (ML) models. Specifically, three different ML methods are applied to a public library in the northwest of Spain and compared; eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network. Furthermore, the accuracy of the results is measured, on the one hand, using both CV(RMSE) and Normalized Mean Biased Error (NMBE), as advised by AHSRAE, for thermal demand predictions and, on the other, an absolute error for HLC estimations. The main novelty of this paper lies in the estimation of the HLC of a building considering thermal demand predictions reducing the requirement for monitoring. The results show that the most accurate model is capable of estimating the HLC of the building with an absolute error between 4 and 6%.

show abstract

Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

Martínez-Comesaña

Garrido²,

Granada

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions

et al. 2023

View full text Add to dashboard Cite

In mountainous regions, the scarcity of air temperature (Ta) measurements is a major limitation for hydrological and crop monitoring. An alternative to in situ measurements could be to downscale the reanalysis Ta data provided at high-temporal resolution. However, the relatively coarse spatial resolution of these products (i.e., 9 km for ERA5-Land) is unlikely to be directly representative of actual local Ta patterns. To address this issue, this study presents a new spatial downscaling strategy of hourly ERA5-Land Ta data with a three-step procedure. First, the 9 km resolution ERA5 Ta is corrected at its original resolution by using a reference Ta derived from the elevation of the 9 km resolution grid and an in situ estimate over the area of the hourly Environmental Lapse Rate (ELR). Such a correction of 9 km resolution ERA5 Ta is trained using several machine learning techniques, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Extreme Gradient Boosting (Xgboost), as well as ancillary ERA5 data (daily mean, standard deviation, hourly ELR, and grid elevation). Next, the trained correction algorithms are run to correct 9 km resolution ERA5 Ta, and the corrected ERA5 Ta data are used to derive an updated ELR over the area (without using in situ Ta measurements). Third, the updated hourly ELR is used to disaggregate 9 km resolution corrected ERA5 Ta data at the 30-meter resolution of SRTM’s Digital Elevation Model (DEM). The effectiveness of this method is assessed across the northern part of the High Atlas Mountains in central Morocco through (1) k-fold cross-validation against five years (2016 to 2020) of in situ hourly temperature readings and (2) comparison with classical downscaling methods based on a constant ELR. Our results indicate a significant enhancement in the spatial distribution of hourly local Ta. By comparing our model, which included Xgboost, SVR, and MLR, with the constant ELR-based downscaling approach, we were able to decrease the regional root mean square error from approximately 3 ∘C to 1.61 ∘C, 1.75 ∘C, and 1.8 ∘C, reduce the mean bias error from −0.5 ∘C to null, and increase the coefficient of determination from 0.88 to 0.97, 0.96, and 0.96 for Xgboost, SVR, and MLR, respectively.

show abstract

A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya

Yego

Kasozi

Nkurunziza

2021

Data

View full text Add to dashboard Cite

The role of insurance in financial inclusion and economic growth, in general, is immense and is increasingly being recognized. However, low uptake impedes the growth of the sector, hence the need for a model that robustly predicts insurance uptake among potential clients. This study undertook a two phase comparison of machine learning classifiers. Phase I had eight machine learning models compared for their performance in predicting the insurance uptake using 2016 Kenya FinAccessHousehold Survey data. Taking Phase I as a base in Phase II, random forest and XGBoost were compared with four deep learning classifiers using 2019 Kenya FinAccess Household Survey data. The random forest model trained on oversampled data showed the highest F1-score, accuracy, and precision. The area under the receiver operating characteristic curve was furthermore highest for random forest; hence, it could be construed as the most robust model for predicting the insurance uptake. Finally, the most important features in predicting insurance uptake as extracted from the random forest model were income, bank usage, and ability and willingness to support others. Hence, there is a need for a design and distribution of low income based products, and bancassurance could be said to be a plausible channel for the distribution of insurance products.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Predicting Motor Insurance Claims Using Telematics Data—XGBoost vs. Logistic Regression

Cited by 24 publications

References 14 publications

Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models

Machine-Learning-Based Downscaling of Hourly ERA5-Land Air Temperature over Mountainous Regions

A Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya

Contact Info

Product

Resources

About