Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

Łukasz Neumann , Robert Marek Nowak , Rafał Okuniewski , Paweł Wawrzyński

Abstract

Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms. We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data. Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method. Presented work shows the applicability of machine learning in the car insurance market.
Author Łukasz Neumann (FEIT / ICS)
Łukasz Neumann,,
- The Institute of Computer Science
, Robert Marek Nowak (FEIT / IN)
Robert Marek Nowak,,
- The Institute of Computer Science
, Rafał Okuniewski (FEIT / ICS)
Rafał Okuniewski,,
- The Institute of Computer Science
, Paweł Wawrzyński (FEIT / IN)
Paweł Wawrzyński,,
- The Institute of Computer Science
Journal seriesApplied Artificial Intelligence, ISSN 0883-9514, (A 15 pkt)
Issue year2019
Vol33
No9
Pages817-828
Publication size in sheets0.55
ASJC Classification1702 Artificial Intelligence
DOIDOI:10.1080/08839514.2019.1630151
URL https://doi.org/10.1080/08839514.2019.1630151
Languageen angielski
File
Machine Learning Based Predictions of Customers Decisions in Car Insurance.pdf 1.69 MB
Score (nominal)15
ScoreMinisterial score = 15.0, 23-07-2019, ArticleFromJournal
Publication indicators Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 0.599; WoS Impact Factor: 2017 = 0.587 (2) - 2017=0.711 (5)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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