Classification System for Mortgage Arrear Management

Zhe Sun, Marco Wiering, Nicolai Petkov

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Due to the economic recession in the recent years, more and more mortgage customers default on the payments. This brings tremendous losses to banks and forces their arrear management departments to develop more efficient processes. In this paper, we propose a classification system to predict the outcome of a mortgage arrear. Each customer who delays a monthly mortgage rate payment is assigned a label with two possible values: a delayer, who will pay the rate before the end of the month, and a defaulter, who will fail to do so. In this way, the arrear management department can only treat defaulters intensively. We use arrear history records obtained from a data warehouse of one Dutch bank. We apply basic classifiers, ensemble methods and sampling techniques to this classification problem. The obtained results show that sampling techniques and
ensemble learning improve the performance of basic classifiers considerably. We choose balanced random forests to build the ultimate classification system. The resulting system has already been deployed in the daily work of the arrear management department of the concerned bank, and this leads to huge cost savings
Original languageEnglish
Title of host publicationIEEE Computational Intelligence for Financial Engineering and Economics
PublisherIEEE (The Institute of Electrical and Electronics Engineers)
Number of pages8
Publication statusPublished - 2014

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