Unfold income myth: Revolution in income models with advanced machine learning techniques for better accuracy¹

Issue title: Special Issue – SAS Global Forum 2018

Guest editors: Jennifer Waller and Tyler Smith

Article type: Research Article

Authors: Liu, Lefei | Liu, Peter | Liu, Jiawei | Chang, Vickey^*

Affiliations: Equifax Inc., USA

Correspondence: [*] Corresponding author: Vickey Chang, Equifax Inc., USA. Tel.: +1 770 740 6092; E-mail: vickey.chang@equifax.com.

Note: [1] This paper is being submitted on April 27, 2018, for SAS Global Forum 2018 Special Issue MASA.

Abstract: Consumer IncomeView is the Equifax next-generation income model that estimates a consumer’s individual annual salary/wage income. This model provides an optimal income solution to our clients by incorporating premier multi-source data assets and advanced machine learning modeling techniques. As a result, the new model significantly increases the scorable population rate compared with the predecessor models and significantly improves the prediction accuracy. The results of Consumer IncomeView have been successfully validated by an array of new proprietary accuracy metrics for model performance measurement, both on internal out-of-time and clients’ data. This paper presents the design, development, methodology and main results obtained from the development of the Equifax Consumer IncomeView model.

Keywords: Individual salary income, machine learning, multi-source data, trended data, accuracy metric

DOI: 10.3233/MAS-180442

Journal: Model Assisted Statistics and Applications, vol. 13, no. 4, pp. 319-327, 2018