I. A. Vasilev, I. G. Pridanova, M. I. Petrovskiy, I. V. Mashechkin
{"title":"Competing Risks Survival Models for Churn Prediction","authors":"I. A. Vasilev, I. G. Pridanova, M. I. Petrovskiy, I. V. Mashechkin","doi":"10.1134/S1064562425700346","DOIUrl":null,"url":null,"abstract":"<p>Predicting customer churn requires considering multiple reasons for customer leave, including financial difficulties, dissatisfaction with service quality, and switching to competitors. Survival analysis methods allow us to estimate the probability of an event occurring over time, but have limited functionality in the case of competing risks. The paper proposes adapting classical survival analysis methods to competing risks by constructing a set of individual models using the One-vs-Rest scheme, with different approaches for incorporating censored events. The proposed method for aggregating forecasts using a multi-class classification meta-model will simplify the process of making expert decisions. According to the results of an experimental study using Freddie Mac mortgage lending data, the proposed methods surpassed existing solutions in quality and can be applied to mortgage lending problems.</p>","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":"112 1","pages":"233 - 238"},"PeriodicalIF":0.8000,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Doklady Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1134/S1064562425700346","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting customer churn requires considering multiple reasons for customer leave, including financial difficulties, dissatisfaction with service quality, and switching to competitors. Survival analysis methods allow us to estimate the probability of an event occurring over time, but have limited functionality in the case of competing risks. The paper proposes adapting classical survival analysis methods to competing risks by constructing a set of individual models using the One-vs-Rest scheme, with different approaches for incorporating censored events. The proposed method for aggregating forecasts using a multi-class classification meta-model will simplify the process of making expert decisions. According to the results of an experimental study using Freddie Mac mortgage lending data, the proposed methods surpassed existing solutions in quality and can be applied to mortgage lending problems.
期刊介绍：
Doklady Mathematics is a journal of the Presidium of the Russian Academy of Sciences. It contains English translations of papers published in Doklady Akademii Nauk (Proceedings of the Russian Academy of Sciences), which was founded in 1933 and is published 36 times a year. Doklady Mathematics includes the materials from the following areas: mathematics, mathematical physics, computer science, control theory, and computers. It publishes brief scientific reports on previously unpublished significant new research in mathematics and its applications. The main contributors to the journal are Members of the RAS, Corresponding Members of the RAS, and scientists from the former Soviet Union and other foreign countries. Among the contributors are the outstanding Russian mathematicians.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
